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The challenge of integrating data from many data sources has persisted as an issue in several industry areas. With the evolution of technology in the upstream petroleum sector (i.e., exploration and production), the petroleum business must contend with technological silos from diverse service providers and suffers from the associated waste of time to locate data and information throughout siloed databases. Based on a thorough compilation of industry-oriented requirements in the form of use cases and competency questions, this document defines a domain ontology for defining entities in offshore petroleum production plants. The objective is to develop a uniform and clearly defined reference vocabulary to aid engineers and information technology professionals in labeling and relating production plant monitoring, simulation measures, and facilities. BFO is the top-level ontology, while GeoCore and a continuing version of the core ontology developed by the Industry Ontology Foundry (IOF) configure the middle-level ontologies. We have studied and combined several other resources to build the ontology, such as glossaries from the industry and related ontologies. The research resulted in a well-founded domain ontology that provides universals, defined classes, and relations that can be useful in several types of applications in the domain. We have demonstrated the utility of the ontology within an actual scenario in an offshore petroleum field in Brazil, where we conceived and applied the domain ontology. This study is a component of the PeTWIN project, 2 2 Petwin is an academic cooperation between UFRGS, UiO, and Libra, Equinor, and Shell companies (www.petwin.org). which looks at the best approaches for creating digital twins of offshore petroleum plants. • O3PO is a domain ontology for offshore petroleum production plants based on BFO. • It allows modeling equipment properties, functional decomposition and connections. • It was validated with data and use cases from Mero Field in Brazil's Pre-Salt. • O3PO contributes to dealing with semantic interoperability issues in oil and gas. • The ontology may serve as a semantic backbone for digital twins in oil and gas. [ABSTRACT FROM AUTHOR]

• This paper proposes a method of recognizing human activity intentions. • Human activity intentions were identified by event calculus and the intention ontology. • The method addresses the challenge of building error-free percept sequences. This paper proposes a method of recognizing the intention of human activity based on percept sequences that represent the activities of daily living (ADL) in a residential space. Based on the activity intention ontology representing actions, poses, and nearby objects related to human activity intentions, the proposed method identifies a human activity intention by using the event calculus when a percept sequence is entered. It is very difficult to recognize ADL occurring in various places in a residence by using regular percept sequences without error. Furthermore, a human activity can have complex intentions. To solve these problems, this paper proposes an activity intention recognition process consisting of three steps. First, the activity intention inference step recognizes the intention of a given percept sequence based on the activity intention ontology. Second, the complex intention-identifying step determines whether to terminate the previously maintained activity intention or continue maintaining it in a complex manner based on the newly inferred activity intention. Third, the uncertainty handling step corrects an inaccurate activity intention caused by an error in the percept sequence. To evaluate the proposed method presented in this paper, activity intention recognition experiments were conducted based on the ADL data that were collected for six households of elderly persons older than 70 years. The results showed that the proposed method has a precision of 99.62% and recall of 92.83%. [ABSTRACT FROM AUTHOR]

• A broad systematic mapping study for legal ontologies is provided. • 4315 articles from 7 databases were analyzed through a rigorous search strategy. • Formalization of legal ontologies is practically falling on semantic web standards. • Semantic shortcomings in legal corpus suggest the use of non-monotonic formalisms. • Legal ontologies are, in general, context-sensitive, impairing reusability. Over the last 30 years, AI & Law has provided breakthroughs in studies involving case-based reasoning, rule-based reasoning, information retrieval and, most recently, conceptual models for knowledge representation and reasoning, known as Legal Ontologies. Ontologies have been widely used by legal practitioners, scholars, and lay people in a variety of situations, such as simulating legal actions, semantic search and indexing, and to keep up-to-date with the continual change of laws and regulations. Given the high number of legal ontologies produced, the need to summarize this research realm through a well-defined methodological procedure is urgent need. This study presents the results of a systematic mapping of the literature, aiming at categorizing legal ontologies along certain dimensions, such as purpose, level of generality, underlying legal theories, among other aspects. The reasons to carry out a systematic mapping are twofold: in addition to explaining the maturation of the area over recent decades, it helps to avoid the old problem of reinventing the wheel. Through organizing and classifying what has already been produced, it is possible to realize that the development of legal ontologies can rise to the level of reusability where prefabricated models might be coupled with new and more complex ontologies for practical law. [ABSTRACT FROM AUTHOR]

Healthcare 5.0 is a research trend promoting a patient-centric approach leveraging Artificial Intelligence (AI)-based solutions. It aims to enhance care and quality of life for all patients, including those with a disability. However, when applied to the health sector, AI may be perceived as not transparent: the Explainable AI (xAI) paradigm attempts to solve this issue by providing more understandable, reliable, and human-interpretable AI-based applications. In a field such as disability – characterized by various impairments, limitations in performing activities, or other kinds of restrictions – the possibility to rely on computable representations of domain knowledge in the form of ontologies can support the development of xAI for healthcare. This work proposes a systematic literature review to identify which disabilities are currently represented in domain ontologies, examining which applicative contexts the ontologies were developed for. This review also investigates how the domain ontologies are modelled, underlining several relevant aspects that may foster their adoption in xAI systems. The review process results allow for shedding light on the main disabilities represented in ontologies, tracing the research trends that were at the basis of their development. Results also enable the identification of research lines that can support semantic interoperability – thus enabling ontologies to play a significant role in explaining decision processes performed by AI-based systems in healthcare. [ABSTRACT FROM AUTHOR]

Public bidding for the information and communication technology (ICT) products offers an external regulatory perspective to identify endogenous risks of ICT supply chain. Owing to the multi-level outsourcing, the modeling methods of the first-order topological similarity are prone to misjudgment. Moreover, only using basic structural attributes (such as enterprise type, registered capital, etc.) is difficult to accurately quantify the enterprise' supply capacity, resulting in the models cannot identify the risky nodes with insufficient supply capacity. In this work, a risk identification Model for ICT supply chain based on Network Embedding and Text Encoding (NETEM) is proposed. In addition to the basic business features of enterprises, the DeepWalk is utilized to obtain the high-order topological representation of the ICT supply chain. Meanwhile, the TextCNN combined with keyword sequence extraction is utilized to supplement text semantic information, and excavate the association between business scope and supply capacity of enterprises. Finally, the basic business features, high-level topological features and text semantic features are spliced to obtain the final enterprise node representation vector, then the node risk classification model is trained. In addition, an ontology is developed for ICT supply chain integration and risk knowledge reuse. The experimental results show that NETEM improves the F1 value of the optimal baseline algorithm by 7.3% on the ICT risk identification data set, and the case study in financial industry demonstrates that the model can be practically applied to identify the risk of supply interruption. [ABSTRACT FROM AUTHOR]

Ontologies have become a standard for knowledge representation across several domains. In Life Sciences, numerous ontologies have been introduced to represent human knowledge, often providing overlapping or conflicting perspectives. These ontologies are usually published as OWL or OBO, and are often registered in open repositories, e.g., BioPortal. However, the task of finding the concepts (classes and their properties) defined in the existing ontologies and the relationships between these concepts across different ontologies – for example, for developing a new ontology aligned with the existing ones – requires a great deal of manual effort in searching through the public repositories for candidate ontologies and their entities. In this work, we develop a new tool, KNIT , to automatically explore open repositories to help users fetch the previously designed concepts using keywords. User-specified keywords are then used to retrieve matching names of classes or properties. KNIT then creates a draft knowledge graph populated with the concepts and relationships retrieved from the existing ontologies. Furthermore, following the process of ontology learning, our tool refines this first draft of an ontology. We present three BioPortal-specific use cases for our tool. These use cases outline the development of new knowledge graphs and ontologies in the sub-domains of biology: genes and diseases, virome and drugs. • A solution for embedding the reusability component in ontology design. • KNIT produces ontologies based on user keywords. • Implementation developed for Life Sciences using BioPortal. • Neo4j is used for managing the Knowledge Graph under development. [ABSTRACT FROM AUTHOR]

Highlights • A new approach to find ontology mapping using inductive logic programming. • The ability to use background knowledge, as an input to induction algorithm. • Can resolve structural inconsistencies between two different ontologies. • Generating generalized logical rules based on background knowledge as mappings. • Achieving high, more acceptable and efficient F-Measure for ontology alignment. Abstract Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively. [ABSTRACT FROM AUTHOR]

Highlights • A computational system was developed according to software engineering prototyping. • An original ontology-based Medical Report Mapping Process was automated in this work. • The system supports automatic mapping of unstructured texts into structured format. • An experimental evaluation was performed in a set of 100 artificial textual reports. • Experts found that the system is valuable to extract and analyze textual patterns. Abstract We have developed, in collaboration with medical and computer experts, the ontology-based Medical Report Mapping Process to support the transformation of unstructured reports into a structured representation. Nevertheless, the techniques employed in this two-phase process must be performed individually and manually by computer instructions, which hinder their use by users not familiar with such language. Thereby, this work proposes a tool to automate and optimize this process by integrating its techniques in a computational system, which was built using a software engineering prototyping approach. This system was experimentally evaluated by applying it to a set of 100 textual reports. The first phase decreased the total number of phrases (853) and words (2520) by 82.25% (48) and 92.70% (184), respectively. In the second phase, 100% of the relevant pieces of information (previously established) present in the reports were transcribed. Also, the second phase was applied, using the same configuration as the first study, in another set with 250 textual reports, resulting in a mapping rate of 99.74%. The unprocessed and unmapped words, regarding both experimental evaluations, were recorded for later inclusion into the ontology. By using this system, efficient and scalable investigations can be performed from medical reports, contributing to generate new knowledge. Also, the system facilitates the definition of these structures due to the feasibility to analyze different sentences in unique phrase sets. [ABSTRACT FROM AUTHOR]

Open information extraction (Open IE) is a task for extracting relationship triples in plain texts without previously determining these relationships. The Open IE systems are generally applied to solutions on the web-scale such improving question answering systems, ontology constructions, document filtering and clustering. Since 2007, within the first Open IE system TEXTRUNNER , other related works have been proposed in this area. Despite other secondary studies on Open IE, useful information available to initiate new research in the area is limited. Thus, we propose a review of the literature in Open IE by a systematic mapping study. We have retrieved 2484 articles about Open IE in Science Direct, IEEE Xplore, ACM Digital Library, Scopus and Google Scholar databases. Among them, 2411 were filtered by exclusion criteria proposed in our systematic mapping protocol. The remaining 73 papers represent the state-of-the-art from the past seven years. Different researchers have proposed important contributions and have pointed out some open problems for Open IE. As a result, we summarized these contributions and identified significant gaps that could be envisioned as future works. [ABSTRACT FROM AUTHOR]

Due to modeling errors in designing ontologies, an ontology may carry incorrect information. Ontology debugging can be helpful in detecting errors in ontologies that are increasing in size and expressiveness day by day. While current ontology debugging methods can detect logical errors (incoherences and inconsistencies), they are incapable of detecting hidden modeling errors in coherent and consistent ontologies. From the logical perspective, there are no errors in such ontologies, but this study shows some modeling errors may not break the coherency of the ontology by not participating in any contradiction. In this paper, contextual knowledge is exploited to detect such hidden errors. Our experiments show that adding general ontologies like DBpedia as contextual knowledge in the ontology debugging process results in detecting contradictions in ontologies that are coherent. [ABSTRACT FROM AUTHOR]

Maintenance cases outside the Structural Repair Manual (OSRM) often occur in civil aircraft structural maintenance (CASM) work. At this time, maintenance personnel needs to learn from historical cases to formulate new maintenance plans. Since CASM cases are diverse, empirical, and heterogeneous, we propose a knowledge representation and reuse model for CASM cases. Firstly, the CASM cases domain ontology is established using Web Ontology Language (OWL), and the case similarity index system is proposed. Then, the proposed global similarity measure integrates three local similarity measures related to different attributes. In particular, considering the engineering significance of attributes, a semantic similarity measure based on ontology structure is proposed. Next, the historical case with the highest similarity with the target case is retrieved under the condition of similarity threshold. Finally, using CASM cases from actual engineering scenes, it is verified that the model has higher retrieval accuracy than traditional methods. Our study shows that case-based decisions have essential reference significance for designing OSRM structural maintenance plans. • The index system of civil aircraft structural maintenance cases is proposed. • A novel knowledge management model for civil aircraft structural maintenance. • The ontology-based semantic similarity measure semantic attributes. • The first tentative case base in the field of aircraft structural maintenance. [ABSTRACT FROM AUTHOR]

New Unmanned Aerial Systems (UAS) applications will increase air traffic densities in metropolitan regions. Collision avoidance systems (CAS) are a key component in integrating a high number of UAS into the airspace in a safe way. This paper presents a distributed, autonomous, and knowledge-based CAS, called Dronetology System (DroS), for UASs. The CAS proposed here is managed using a novel ontology, called Dronetology-cas, which allows to make autonomous decisions according to the knowledge inferred from the data gathered by the UAS. DroS is deployed as part of the payload of the UAS. So, it is designed to run in an embedded platform with limited processing capacity and low battery consumption. DroS collects data from sensors and collaborative elements to make smart decisions using knowledge obtained from collaborative UASs, adapting the maneuvers of the aerial vehicles to their original flight plans, their kind of vehicle, and the collision scenario. DroS accountability involves recording its internal operation to assist with reconstructing the circumstances surrounding an autonomous maneuver or the details previous to a collision. DroS has been verified using the hardware in the loop (HIL) technique with a UAS traffic environment simulator. Results obtained show a significant improvement in terms of safety by avoiding collisions. • Distributed, autonomous and knowledge-based collision avoidance system for UASs. • Autonomous decisions according to the knowledge inferred from the data gathered. • Accountability of the circumstances surrounding a maneuver or collision. • Verified using HIL with an UAS traffic environment simulator. • Significant improvement in terms of safety by avoiding collisions. [ABSTRACT FROM AUTHOR]

The ubiquitous presence of surveillance systems generates massive amounts of video data. Storage and analysis of this data in real-time is a substantial challenge. There is huge potential in representing data in machine-readable and machine-interpretable format due to the presence of hidden semantics in images and videos. However, such representation requires ontology, which calls for expert domain knowledge. In this paper, a novel NLP-guided approach to generate an ontology for multimedia representation and information retrieval is proposed. A semi-automatic NLP-guided framework, which extracts all possible relations among objects is presented. This framework leverages the textual data of the domain to generate possible descriptions and actions within the domain. Relations among objects get embedded as object properties, whereas the category of an object as a class. Features and attributes of objects encode the data properties of the ontology. The proposed ontology is compared with existing multimedia ontologies and evaluated with regard to its capability to represent relations occurring in benchmark datasets, demonstrating the completeness and thorough coverage of the domain concepts. Spatial reasoning rules are established using Semantic Web Rule Language (SWRL) rules, and information retrieval is demonstrated using Description Logic (DL) and SPARQL queries. The proposed NLP-guided ontology generation approach is general enough to help in the development of ontologies for other domains as well, by providing video and textual data of the domain of interest, with limited human involvement. • Object properties extraction with textbase and NLP-guided approach. • Frequency-based semantic refinement to enhance domain-specific relations. • A novel data-driven video surveillance ontology "VizITS" for the ITS domain. • Semantic information retrieval as events, metadata, and question-answering. [ABSTRACT FROM AUTHOR]

The ubiquitous presence of surveillance systems generates massive amounts of video data. Storage and analysis of this data in real-time is a substantial challenge. There is huge potential in representing data in machine-readable and machine-interpretable format due to the presence of hidden semantics in images and videos. However, such representation requires ontology, which calls for expert domain knowledge. In this paper, a novel NLP-guided approach to generate an ontology for multimedia representation and information retrieval is proposed. A semi-automatic NLP-guided framework, which extracts all possible relations among objects is presented. This framework leverages the textual data of the domain to generate possible descriptions and actions within the domain. Relations among objects get embedded as object properties, whereas the category of an object as a class. Features and attributes of objects encode the data properties of the ontology. The proposed ontology is compared with existing multimedia ontologies and evaluated with regard to its capability to represent relations occurring in benchmark datasets, demonstrating the completeness and thorough coverage of the domain concepts. Spatial reasoning rules are established using Semantic Web Rule Language (SWRL) rules, and information retrieval is demonstrated using Description Logic (DL) and SPARQL queries. The proposed NLP-guided ontology generation approach is general enough to help in the development of ontologies for other domains as well, by providing video and textual data of the domain of interest, with limited human involvement. • Object properties extraction with textbase and NLP-guided approach. • Frequency-based semantic refinement to enhance domain-specific relations. • A novel data-driven video surveillance ontology "VizITS" for the ITS domain. • Semantic information retrieval as events, metadata, and question-answering. [ABSTRACT FROM AUTHOR]

• An ontology based package design system is proposed in fresh e-commerce logistics. • The system is defined in semantic ontologies to achieve package recommendation. • A real-life case from company JY shows the practicality of the proposed system. Package design for mass order is becoming one of the significant issues to influent package quality in fresh e-commerce logistics. However, the package schemes of fresh products remain challenging, due to various fresh products storage and delivery requirements, and complex environment temperature in a long express distance. This paper proposes a package design system in fresh e-commerce logistics to address these challenges. More specifically, the package scheme design is defined in semantic ontologies with three steps such as order identification, package scheme design based on identification and box type recommendation. The intelligent package system standardizes the information flow in fresh e-commerce logistics, as well as shortens the package information processing time. A real-life case study from company JY is conducted involving stages from customer information collection to order delivery. The case demonstrates the working mechanism and system implementation in terms of rationalized work activities and quantitative improvements. [ABSTRACT FROM AUTHOR]

Discovering the semantic relationships among heterogeneous ontologies has been one of the core research topics in Semantic Web. As ontology matching systems inevitably adopt heuristic strategies, wrong mappings are often contained in final alignments. Most methods for mapping revision depend on dealing with logical incoherence. However, erroneous mappings that do not cause incoherence may be left out. Hence, manual validations with domain expertise are needed. Nevertheless, existing interactive methods for mapping revision still suffer from two limitations. Firstly, revision methods designed to present friendly graphical interfaces and contextual information related to mappings pay little attention to reducing manual decisions, leading to the efficiency problem. Secondly, revision methods focusing on automated reasoning to reduce manual decisions employ models of high complexity, leading to the practicality problem when dealing with alignments across large-scale ontologies. To address these problems, we propose a novel graph-based method for interactive mapping revision, aiming to reduce the manual efforts as much as possible. DL-Lite ontologies and their mappings are encoded into an integrated graph, where the mapping arcs will be judged by the experts. We define the decision space tailored for mapping revision, which can be used to improve the efficiency of manual making decisions. After a manual decision is made in each interaction, the mapping arcs will be automatically updated in the integrated graph. The whole update process modeled in our defined graph-based decision space could be accomplished in polynomial time. We further design an impact function based on the integrated graph and weights of mappings, which can display the most influential mappings to experts. In this way, the number of manual decisions can be reduced further. To cope with the practicality of our method for alignments of large-scale ontologies, we introduce the notion of "reliable" mappings as an attempt to alleviate the burden of experts for making decisions, and propose two soft principles to ensure the reliability of selected mappings. Moreover, we define influence relation and design a corresponding algorithm to enhance the method for detecting incoherence of the integrated graph, which is transformed from the ontologies beyond DL-Lite and their mappings. We implement our method and evaluate its efficiency by 16 alignments generated across real-world ontologies. Experimental results show that our proposed method can improve the efficiency by 19% on average and save more manual decisions than other interactive revision methods in most cases. • Update mappings automatically based on manual decisions in each interaction. • Impact function of mappings reduces the efforts of manual decisions further. • Reliable mappings improve the practicality of method for large-scale ontologies. • An influence relation enhances the method for detecting incoherent mappings. • Save manual decisions better than other interactive revision methods in most cases. [ABSTRACT FROM AUTHOR]

One of the most critical issues in Ambient Assisted Living (AAL) is the design of systems that can evolve to meet the requirements of individuals as their needs and health conditions change. Although much work has been done on home and building automation systems for AAL, often referred to as assistive domotics, there is in fact still a substantial lack of solutions capable to support system designers in the early stage of development of such assistive systems. To this aim, the work contributes to the research on design of assistive domotic systems by presenting an ontology-driven methodology aimed to guide the development process. The novel contributions of the paper include the goal-oriented approach of the methodology, which involves the elicitation and analysis of AAL requirements and their formal representation in an ontology, where high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. Moreover, logic-based reasoning enables more advanced functionalities useful at design time. We present a validation of the methodology showing typical use cases both related to the development from scratch of a domotic system with assistive capabilities starting from a set of high-level user requirements and the redesign of existing implementations according to changed requirements. [ABSTRACT FROM AUTHOR]

We propose a new family of abstract argumentation frameworks which we refer to as generalized (identified through the acronym GenAF ), due to its ability of adapting to different representation languages. GenAF s are formalized upon an unspecified representation language L but enriched with some inner structure which allows relating formulæ from the underlying knowledge base with primitive elements of arguments. The well-known Dung’s standard semantics are adapted to construct the GenAF ’s reasoning machinery. As an application, we reify the GenAF ’s abstract language for arguments with the basic ALC flexibility of the presented formalism and a way of applying argumentation for reasoning over inconsistent ontologies. Finally, a detailed study is performed on the matter of argumentation rationality. The GenAF when concretized into a generalized argumentation system ( GenAS ) is studied under specific conditions which turn it into a standard logic-based argumentation system. This brings the opportunity to verify the scope of widely accepted postulates for logic-based argumentation to control the well behavior of the GenAS and to relate it to other argumentation systems. [ABSTRACT FROM AUTHOR]

Sound ease of doing business is essential for building a modernized economy and fostering high-quality growth. Research on the ease of doing business should not only focus on the impact and evaluation system, but also have guiding significance for decision-making related to the ease of doing business, but there are still relatively few studies in this area. Due to the complexity of ease of doing business, to save energy and make the most of the experience knowledge of decision-makers, we have introduced case-based reasoning (CBR) into strategy response, as a possible application of the analysis of case histories. The purpose of this paper is to propose a CBR strategy response method for ease of doing business, which can be used to formulate more intelligent and efficient ease of doing business strategy. The innovation of this research focuses on three key parts. Firstly, a CBR strategy response method for ease of doing business is innovatively developed. Secondly, the ontology method is used to build ease of doing business domain model, including city features, ease of doing business indicators, and response strategy. Thirdly, Missing values are also included in the calculation process. During the calculation process, missing values are also processed, and inappropriate response strategies in similar case sets are adjusted in response to differences between scenarios to generate feasible strategies. Finally, a case study of a city shows that this method can extend the CBR approach to ease of doing business strategy response. • A CBR-based doing business strategy response method is innovatively developed. • Using ontology approach to build an easy of doing business domain model. • A two-step case retrieval method is proposed and missing values are dealt with. • A scenario-based case reuse method is proposed and used. [ABSTRACT FROM AUTHOR]

Plagiarism is a misconduct, which refers to the use of scientific and literary content contained in other sources without reference to them. Today, the rise of plagiarism has become a serious problem for publishers and researchers. Many researchers have discussed this problem and tried to identify types of plagiarism; however, most of these methods are not effective in detecting intelligent plagiarism. In other words, most of these methods focus on direct copying. Therefore, in this study, two methods are proposed to identify Extrinsic plagiarism. In both methods, to limit the search space, two stages of filtering based on the bag of word (BoW) technique are used at the document level and at the sentence level, and plagiarism is investigated only in the outputs of these two stages. In the first method to detect similarities in suspicious documents and sentences, the combination of pre-trained network technique of words embedding FastText and TF-IDF weighting technique to form two structural and semantic matrices and in the second method to form the two matrices, WordNet ontology and weighting TF-IDF is used. After forming the above matrices and calculating the similarity between the pairs of matrices of each sentence, using the Dice similarity and the structural similarity of the weighted composition, two similarity values are calculated. By comparing the similarity of suspicious sentences with the minimum threshold, the document containing the suspicious sentence receives the label of plagiarism or non-plagiarism. Experimental results on the PAN-PC-11 database show that the first method has achieved 95.1% precision and the second method 93.8% precision, which shows that the use of word embedding network compared to WordNet ontology can be more successful in detecting Extrinsic plagiarism. [ABSTRACT FROM AUTHOR]

Majority of customers and manufacturers who tend to purchase and trade via e-commerce websites primarily rely on reviews before making purchasing decisions and product improvements. Deceptive reviewers consider this opportunity to write fake reviews to mislead customers and manufacturers. This calls for the necessity of identifying fake reviews before making them available for decision making. Accordingly, this research focuses on a fake review detection method that incorporates review-related features including linguistic features, Part-of-Speech (POS) features, and sentiment analysis features. A domain feature ontology is used in the feature-level sentiment analysis and all the review-related features are extracted and integrated into the ontology. The fake review detection is enhanced through a rule-based classifier by inferencing the ontology. Due to the lack of a labeled dataset for model training, the Mahalanobis distance method was used to detect outliers from an unlabeled dataset where the outliers were selected as fake reviews for model training. The performance measures of the rule-based classifier were improved by integrating linguistic features, POS features, and sentiment analysis features, in spite of considering them separately. • Fake reviews can be detected by incorporating review content related features. • Linguistic, POS tagging and sentiment features can utilize as content related factors. • Aspect based sentiment analysis was incorporated for exploring sentiment features. • Outliers from unlabeled dataset were considered as spam reviews for model training. • Classification results will be generated according to the SWRL rules of the ontology. [ABSTRACT FROM AUTHOR]

This paper presents a workflow for the design of transformation processes using different kinds of expert's knowledge. It introduces POND (Process and observation ONtology Discovery), a workflow dedicated to answer expert's questions about processes. It addresses two main issues: (1) how to represent the processes inner complexity, and (2) how to reason about processes taking into account uncertainty and causality. First, we show how to use a semantic model, an ontology, and its associated data to answer some of the expert's questions concerning the processes, using semantic web languages and technologies. Then, we describe how to learn a predictive model, to discover new knowledge and provide explicative models by integrating the semantic model into a probabilistic relational model. The result is a complete workflow able to extensively analyze transformation processes through all their granularity levels and answer expert's questions about their domains. An example of this workflow is given on biocomposites manufacturing for food packaging. • Due to their complexity, reasoning with transformation processes is challenging. • POND is a workflow aiming to represent and reason with transformation processes. • Representation is achieved with the ontology PO² and its associated softwares. • Reasoning under uncertainty is achieved with probabilistic graphical models. • Expert knowledge integration allows knowledge and causal discovery. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a knowledge base supporting an intelligent system to solve problems of optimization especially problems of discrete production processes optimization called Intelligent Algebraic-Logical Meta-Model (ALMM) Solver. Using a unified description of selected optimization problems, an ontological knowledge base was designed, which allows for selective selection of Intelligent ALMM Solver components necessary to solve and model problems. Using the definitions of the properties of optimization problems, scalable components describing exemplary optimization jobs were selected. Ontology for this area was developed, with particular emphasis on the requirements of the ALMM Solver. Using the possibility of interactive communication with the ALMM ontology in the form of SQL queries in the experimental part of the work, exemplary queries for the designed Knowledge Base (KB) module were presented, and the response generated by the system is a scenario of intelligent selection of a set of components modeling and solving a given problem. Such an innovative approach allows for dynamic construction of algorithms solving problems of discrete optimization. The use of knowledge about the properties of the considered processes and ALMM technology universalizes the proposed KB system making it an intelligent and efficient tool for solving discrete optimization jobs. The key advantage of the proposed ontological approach is the ability to flexibly expand it and extend its use to other classes of problems which have already been described in the ALMM technology. • Optimization of discrete production processes with ALMM Solver. • Using the properties of optimization problems for exemplary jobs were considered. • Description of problems regarding ontological knowledge base structure was proposed. • Construction of algorithms solving problems of discrete optimization is possible. • Proposed ontological approach can be applied to other classes of problems. [ABSTRACT FROM AUTHOR]

The ever-increasing volume of information available on tourist attractions in cyberspace has made the tourist decision-making process a crucial task. Therefore, tourism recommendation systems can significantly benefit tourists in terms of comfort and satisfaction. In this paper, a context-aware fuzzy-ontology-based tourism recommendation system is proposed. In the proposed system, we have two new propositions that can be individually used in other tourism recommendation systems: a fuzzy-weighted ontology and a new sentiment/emotion score scheme. In CAFOB, the ontology-based scores of a user's reviews are then multiplicatively modulated by sentiment/emotion scores to generate the total scores of the reviews' ontology words, representing the user preferences. Then, the nearby-open- 3 + -bubbled touristic attractions' characteristics are extracted based on their past visitors' reviews. Finally, a recommendation list is produced using a maximum hybrid semantic similarity between the user preferences and attractions' characteristics. The employment of contextual information, including weather, location, and time makes CAFOB context-aware and improves the accuracy and quality of the recommendations. The F-measure, NDCG, and MRR results show the outperformance of CAFOB against the state-of-the-art tourism recommendation systems for all relevant Top N recommendation options and different geographical spans of interest. • A fuzzy-weighted ontology that is more representative of real life. • CAFOB benefits from the advantages of ontology, semantics, and context-awareness. • A higher accuracy than cluster-based TRSs by considering each keyword individually. • A new TRS feature-based taxonomy that provides an insightful research framework. [ABSTRACT FROM AUTHOR]

Fuzzy Cognitive Map (FCM) is a powerful approach to model the dynamics of complex systems, and has been applied in various fields, such as psychology, education, engineering, and management. The construction of FCMs has great importance for its application. The literature, however, takes it for granted that Fuzzy Cognitive Maps allow for a simple aggregation of domain knowledge from several experts without much considering holistic approaches with semantic comparison of concepts. This paper describes the method for constructing Fuzzy Cognitive Maps based on the ontology matching approach in a holistic way. The ontology matching technology through a series of proposed operations is used to find the alignment between semantically related concepts and solves the semantic ambiguity problem, thereby improving the experts-based FCM construction method. This approach enhances the effective collaboration in the heterogeneous environment of today’s internet-based world, and the proposed holistic FCMs also allows the user to draw additional observations concerning the underlying system, which are not available through the individual FCMs. [ABSTRACT FROM AUTHOR]

• Domain ontologies constructed by ontology learning is used to build topic model. • A multi-objective optimization model for link evaluation is put forward. • A multi-objective ACO algorithm is applied to guide the search of crawlers. The focused crawler based on semantic analysis is a research hotspot in the field of information retrieval. The domain ontology is generally applied to construct the topic model of the focused crawler. In order to overcome the limitations of builders' knowledge reserve and subjective consciousness in the process of constructing artificially ontology, a semi-automatic construction method of domain ontology based on ontology learning technology combining the latent Dirichlet allocation and the Apriori algorithm is proposed in this article. When evaluating the relevance between a hyperlink and a specific topic, the joint evaluation method considering both the web text and the link structure is usually used. However, the traditional weighted sum method is difficult to reasonably determine the optimal weights of these evaluating indicators. To solve this problem, a multi-objective optimization model for link evaluation and a subsequent multi-objective ant colony optimization algorithm (MOACO) are proposed. In the MOACO, a method of the nearest farthest candidate solution (NFCS) is combined with the fast non-dominated sorting to select a set of Pareto-optimal hyperlinks and guide the crawlers' search directions. The experimental results of the focused crawling on the domain knowledge of typhoon disasters and rainstorm disasters prove that the ability of the proposed focused crawlers to retrieve topic-relevant webpages. [ABSTRACT FROM AUTHOR]

The paper presents the main results of the KOMET (Knowledge and cOntent structuring via METhods of collaborative ontology design) project, which aims to develop a novel paradigm for knowledge structuring based on the interplay between cognitive psychology and ontology engineering. By the knowledge structure (a conceptual model) we define the main domain concepts and relations between them in the form of a graph, map or diagram. This approach considers individual cognitive styles and uses recent advances in knowledge engineering and conceptual structuring; it aims to create new, consistent and structurally holistic knowledge bases for various areas of science and technology. Two stages of research have been completed: research into correlations between the expert’s individual cognitive style and the peculiarities of the expert’s subject domain ontology development; and research into correlations between the expert’s individual cognitive style and the group ontology design (including design accomplished by groups of experts with either similar or different cognitive styles). The results of these research stages can be applied to organizing collaborative ontology design (especially for research and learning purposes), data structuring and other group analytical work. Implications for practice are briefly delineated. [ABSTRACT FROM AUTHOR]

The ontology learning from text lacks an initial evaluation of the property types present in the resource used in the learning task. We need a way to explore the distribution of property types before the actual ontology learning process. In this paper we propose three algorithms that help ontologists in the preliminary resource exploration. The algorithms are devised for Wordnet, generic corpora and Wikipedia. Minimal assumptions about the property types that can be extracted are made. The algorithms are tested with concepts belonging to five taxonomies. The distribution of property types for the extracted concepts is computed and reported. [ABSTRACT FROM AUTHOR]

This paper presents a novel information retrieval approach for personalized itinerary search in urban freight transport systems. The proposed approach is based on the integration of three techniques: Case Base Reasoning, Choquet integral and ontology. It has the following advanced features: (1) user-oriented ontology is used as source of knowledge to extract pertinent information about stakeholder’s preferences and needs; (2) semantic web rule language is considered to provide the system with enhanced semantic capabilities and support personalized case representation; (3) a CBR-personalized retrieval mechanism is designed to provide a user with an optimum itinerary that meets his personal needs and preferences. The above features lead to a personalized and optimum itinerary search that meets the user’s needs as specified in their queries such as fuel consumption, environmental impact, optimum route, time management etc. This has the potential to effectively manage fright movement according to stakeholder’s needs and alleviate congestion problems in urban areas. The proposed intelligent decision support system (Onto-CBR) is implemented to an itinerary search problem for freight transportation users in urban areas. Its performance is further compared to an itineraries search system that was proposed by the authors in an earlier publication. Both approaches are compared in terms of their ability to meet user’s personal preferences and achieve accuracy in case retrieval. The experimental results showed the ability of the proposed system to improve the accuracy of case retrieval and reduce retrieval time prominently. The ability of the proposed system tailor the search to stakeholders needs, improve the accuracy of case retrieval and facilitate the search process are among the main positive features of the proposed intelligent decision support system. [ABSTRACT FROM AUTHOR]

The interest in the use of ontologies for creating more intelligent and effective enterprise information systems has increased considerably in recent years. The most critical aspects during the development of these systems are: (1) to identify the ontology concepts and (2) to make explicit the business rules by means of the ontology axioms. In order to address these issues, mappings of business rules expressions to ontology statements based on different languages were proposed. Despite the efforts made in this area, some work remain to be done. This work presents a set of ontology design patterns providing a way to obtain an OWL/SWRL ontology by applying metamodel transformation rules over the SBVR specification of a business domain. Patterns are rooted in the structural specification of the standards, providing a set of mappings readily usable for business people or developers concerned with the implementation of a mapping tool. Moreover, translations from SBVR to SWRL language are presented in order to fill the gap in the expressive power of SBVR and OWL. The theoretical expressions of patterns are illustrated by means of an example depicting the core structure of a fictitious company. [ABSTRACT FROM AUTHOR]

In visual domains, such as Medicine, Meteorology and Geology, the tasks are accomplished through intensive use of visual knowledge. In this work, we focus in an essential kind of task that is performed in many visual domains: the visual interpretation task. We call visual interpretation the expert reasoning process that starts with the perception of visual features of domain objects and results in the understanding of the scene. We propose a top-down approach for solving visual interpretation tasks, which is based on symbolic pattern matching supported by well-founded ontologies. Our approach combines well-founded domain ontologies; a meta-model for representing inferential knowledge, which is based on the notion of perceptual chunks; and a reasoning model for visual interpretation. The proposed model was applied for developing an expert system for automating the task of visual interpretation of depositional processes, which a crucial task for petroleum exploration. [ABSTRACT FROM AUTHOR]

Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user’s interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users’ changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours. [ABSTRACT FROM AUTHOR]

In several domains, software applications have intensively used Knowledge Organization Systems (KOS) like database schemas, ontologies, taxonomies and thesauri and their associated semantic correspondences ( i.e. , mappings). This underlines the relevance and capabilities of KOS and mappings to manage and integrate vast amounts of data. However, the dynamic nature of domain knowledge forces knowledge engineers to constantly modify KOS, to keep them up to date and useful. In this context, the maintenance of mappings affected by KOS evolution still remains an open research issue. Although this problem appears relevant for many different computer science fields, ranging from database to artificial intelligence, literature has so far only superficially addressed it to enable more flexible, automatic and precise solutions. This article presents, discusses and compares existing approaches for maintaining mappings and describes open research challenges. [ABSTRACT FROM AUTHOR]

The profiling of background knowledge is essential in scholar’s recommender systems. Existing ontology-based profiling approaches employ a pre-built reference ontology as a backbone structure for representing the scholar’s preferences. However, such singular reference ontologies lack sufficient ontological concepts and are unable to represent the hierarchical structure of scholars’ knowledge. They rather encompass general-purpose topics of the domain and are inaccurate in representing the scholars’ knowledge. This paper proposes a method for integrating of multiple domain taxonomies to build a reference ontology, and exploits this reference ontology for profiling scholars’ background knowledge. In our approach, various topics of Computer Science domain from Web taxonomies are selected, transformed by DBpedia, and merged to construct a reference ontology. We demonstrate the effectiveness of our approach by measuring five quality-based metrics as well as application-based evaluation against the developed reference ontology. The empirical results show an improvement over the existing reference ontologies in terms of completeness, richness, and coverage. [ABSTRACT FROM AUTHOR]

The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines. To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners. [ABSTRACT FROM AUTHOR]

Highlights: [•] An ontology-based system is proposed to model items included in a hospital health catalog. [•] The proposed ontology-based system can be used for sharing and acquiring knowledge by clinical and administrative personnel. [•] Correct usage of medical supplies has a positive impact both on hospital economics and personnel safety. [Copyright &y& Elsevier]

Abstract: Ambient intelligent systems such as Home and Building Automation Systems (HBAS) are becoming evermore accepted and capable of actuating automatically on behalf of users to fulfil their requests or enable activities. However, when multiple users interact with such systems, the requirements of activities often interfere resulting in conflicting actuations which HBAS ought to automatically detect and resolve. Yet, despite recent advances in HBAS, no ambient intelligent solution has been reported that is adequately grounded on knowledge analysis. The contributions of this article are twofold. First, it reviews relevant literature on Ambient Intelligence, conflict detection, conflict resolution and knowledge representation in HBAS. Second, it proposes and validates an ontological framework for conflict detection and resolution backed by knowledge-based analysis. Effectively, the proposed solution performs automatic environment actuations maximizing users comfort and energy efficiency. [Copyright &y& Elsevier]

Semantic Similarity (SS) has become a long-standing research domain in artificial intelligence and cognitive science for measuring the strength of the semantic relationship between entities (e.g., words, documents). Several ontology-based SS measures have been proposed in the recent time due to their ability of mimicking the cognitive process of humans. Among them, intrinsic information content (IC) based approaches have shown a significant correlation with human assessment. The design principle of the existing intrinsic IC-based SS measures constrain themselves to be applicable in a single ontology. However, such SS measures can be leveraged within two ontologies with the help of identifying the most informative common ancestors (MICA) across the ontologies. Existing IC-based MICA identification algorithms follow string matching of the labels of the concepts. In this paper, we propose a novel intrinsic IC-based MICA finding algorithm that exploits two domain-ontologies for finding SS without using string matching of the labels. The proposed approach has been evaluated using a widely used benchmark dataset of medical terms. The experimental results show that the proposed IC-based approach can be a stepping stone to a new direction in the process of finding MICA over two ontologies. • An algorithm for finding Cross-Ontology Semantic Similarity between concepts. • Avoiding strict string matching for finding the most informative common ancestor. • Intrinsic Information Theoretic Knowledge-based paradigm followed. • Correlation Coefficient with human cognition has been measured. • Information retrieval becomes more effective. [ABSTRACT FROM AUTHOR]

Ontology meta-matching techniques have been consolidated as one of the best approaches to face the problem of discovering semantic relationships between knowledge models that belong to the same domain but have been developed independently. After more than a decade of research, the community has reached a stage of maturity characterized by increasingly better results and aspects such as the robustness and scalability of solutions have been solved. However, the resulting models remain practically intelligible to a human operator. In this work, we present a novel approach based on Mamdani fuzzy inference exploiting a model very close to natural language. This fact has a double objective: to achieve results with high degrees of accuracy but at the same time to guarantee the interpretability of the resulting models. After validating our proposal with several ontological models popular in the biomedical field, we can conclude that the results obtained are promising. • Introduction of a novel ontology meta-matching technique for biomedical ontologies. • Use of Mamdani inference for improving the interpretability of the resulting model. • Evaluation using de facto benchmarks datasets from the biomedical domain. [ABSTRACT FROM AUTHOR]

• Identifies educational knowledge and data required by ISO 37,120 educational city indicators. • Defines an ontology for representing education knowledge and data required by indicators. • Precisely represents education indicator definitions using the ontology. • Precisely represents city's educational indicators and supporting data used to derive them. • Provides a standard for publishing educational indicator knowledge and data on the Semantic Web. • Describes an intelligent agent for performing definition, longitudinal and transversal analysis. This paper addresses the problem of how to represent education system measurement definitions and the data used to derive them. ISO 37120 is standard for measuring city performance. It defines 100 indicators of which seven focus on Education. As cities adopt the standard and begin publishing their indicator values, citizens, city bureaucrats and politicians will be able to see how well their education systems perform relative to other cities. Hence, education systems will be subject to greater scrutiny and will have to provide evidence-based explanations for their performance. In order to enable the automated analysis of city performance, four problems must be solved: 1) how are indicator definitions represented? 2) how is the data used to derive an indicator value represented? 3) how is educational system specific knowledge represented, and 4) how is city specific educational system knowledge represented? In this paper we present an Education Ontology designed to represent ISO 37120 education indicators and the information delineated above. The ontology is designed to enable an intelligent agent to perform consistency and root cause analysis of city indicators. It can also be used by cities to publish their education indicators and data, using Semantic Web standards, on their Open Data portals. [ABSTRACT FROM AUTHOR]

• Ontology construction model under cross-domain application. • Proposed Jaccard Similarity Evaluation, Data filtering and Outlier Detection. • Semantic annotation, cluster, ontology construction with optimal centroid selection. • Hybrid algorithm of Whale Optimization Algorithm and Rider Optimization Algorithm. Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models. [ABSTRACT FROM AUTHOR]

• Denoising the distant supervision assumption using semantic similarity is proposed. • Different semantic similarity measures are proposed using word embeddings. • The proposed method can be applied in any ontology lexicalization framework. • Results show a significant increase in the generated ontology lexicon's quality. Ontology lexicalization aims to provide information about how the elements of an ontology are verbalized in a given language. Most ontology lexicalization techniques require labeled training data, which are usually generated automatically using the distant supervision technique. This technique is based upon the assumption that if a sentence contains two entities of a triple in a knowledge base, it expresses the relation stated in that triple. This assumption is very simplistic and would lead to generating wrong mappings between sentences and knowledge base triples. In other words, a sentence may contain two entities of a triple, but the relation of entities in the sentence differs from the relation of the triple. Such wrong mappings cause to generating wrong ontology lexicon entries. In this paper, a new method, called denoising distant supervision, is presented to reduce the wrong mappings between sentences and triples by taking the semantic similarity between sentences and the label of triples' predicate into account. For this purpose, different semantic similarity measures are proposed, which use pre-trained word embeddings to calculate the semantic similarity between the sentences and the label of the triples relation. Then, the sentences whose semantic similarity is low are removed from the mapping. The proposed solution is evaluated in the M−ATOLL framework. The experimental results show that the quality of the generated ontology lexicon under the proposed solution is improved compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

• Handling compound sentences increases precision of a SPARQL query. • Handling opposing scalar adjectives increases precision and recall of a SPARQL query. • Handling Negation increases precision and recall of a SPARQL query. This paper provides a dependency based framework for converting natural language to SPARQL. We present a tool known as PAROT (which echos answers from ontologies) which is able to handle user's queries that contain compound sentences, negation, scalar adjectives and numbered list. PAROT employs a number of dependency based heuristics to convert user's queries to user's triples. The user's triples are then processed by the lexicon into ontology triples. It is these ontology triples that are used to construct SPARQL queries. From the experiments conducted, PAROT provides state of the art results. [ABSTRACT FROM AUTHOR]

• A concept-based method incorporates an ontology to support text categorization. • This proposed method addresses limitations of feature-based text categorization. • A concept-based representation reduces feature space and makes results more interpretable. • Evaluation results show it outperforms feature-based k NN and semantic-based techniques. Voluminous, conveniently accessible textual documents, created and disseminated by modern information technology, makes automated document organization increasingly important for both individuals and organizations. Many existing techniques rely on document content analysis that classifies new, unlabeled documents by examining the similarity based on the overlap between their important features and the representative features of each document category. However, the performance of feature-based techniques can be significantly hindered by word mismatch and ambiguity problems. As a remedy, this study takes a concept-based approach and propose a text categorization method that incorporates a domain-specific ontology to support automated document categorization more effectively. The proposed method classifies documents according to their respective range of relevant concepts. We empirically evaluate our method versus several prevalent benchmarks that include feature-based k-nearest neighbors (k NN) and semantic-based techniques. The results show the proposed method more effective than the benchmark techniques; it achieves better performances when using a complete concept hierarchy without considering the hierarchical relationships among concepts. The proposed method illustrates how to incorporate a domain-specific ontology to improve document classification. Our method is computationally efficient because it produces a concept space of relatively few dimensionalities and does not require semantic space reconstruction as new documents arrive. Moreover, the relationships and patterns for classifying documents, generated by our method, are explicit and comprehensible. [ABSTRACT FROM AUTHOR]

• A systematic mapping on metaheuristics-based ontology meta-matching approaches. • An analysis of algorithms usually used in unsupervised and supervised approaches. • Answering research questions to guide new researchers in ontology meta-matching. Ontologies have emerged to establish a well-defined meaning for information, solving problems of heterogeneity in data semantics and facilitating the process of information exchange. However, ontologies have generated a new semantic problem, since using more than one ontology can generate ambiguity in the meaning of a given data. The problem of ontology matching is to search for relationships between entities of distinct ontologies, solving the problem of semantic heterogeneity of the data. The problem is a relevant issue in the area of knowledge representation and several approaches have been proposed to solve it. This work presents a systematic mapping of the main works published in the area with an emphasis on metaheuristics-based meta-matching approaches. [ABSTRACT FROM AUTHOR]

• A unified timetabling ontology – General Timetabling Problem (GTP), is proposed. • GTP addresses cross-domain heterogeneity and provides a common modeling basis. • Structure of the GTP model is exploited using a two-stage solution approach. • Elite initial solutions found in the first stage are improved via meta-heuristics. • Numerical analysis exhibits high computational efficiency of the solution approach. Timetabling is a managerial problem that recurringly appears in various domains such as education, transport, sports, and staff management. The combinatorial nature of this problem poses solution challenges that aggravate with an increase in the problem size. While heuristics and metaheuristics initially offered promise, the progress plateaued as attempts to solve even bigger problems showed exorbitant costs while sampling feasible solutions. This issue is criticized for the lack of exploiting the underlying problem structure and the prevailing fragmentation in modeling and solution approaches. To address these issues, we first propose a novel timetabling ontology that serves as a common modeling basis, resolving the existing heterogeneity across various application domains. This ontology facilitates mapping the anatomy of any real timetabling problem onto its general structure. Second, it offers a unique two-stage solution approach for solving this generalized problem. The first stage of this approach entails generating elite initial solutions by exploiting this general problem structure, while the second stage uses a metaheuristic to improve these solutions at a very low computational cost. Using a university timetabling problem, we demonstrate the applicability of this approach. The numerical results show that the proposed algorithm converges within a fraction of computational costs incurred by other techniques for comparable problem sizes. This research paves the way for consolidating efforts for the development of generalizable cross-domain timetabling approaches. [ABSTRACT FROM AUTHOR]

• This study develops a formal ontology of over 400 consumer financial goals. • The ontology is designed for wide range of financially situated users. • This research extends the Consumer Financial Protection Bureau's Financial Capability Model. • The study provides specific financial goals for consumers to improve their financial capability. • It focuses discussion on the objectives required to achieve financial capability. In this research, we communicate the design and evaluation of a consumer financial goals ontology to be utilized as a knowledgebase within recommender systems applications designed to provide decision support in the domain of financial planning. The goal of this research is to provide a domain conceptualization and knowledge classification of a comprehensive set of financial capability enhancing objectives contextually appropriate for a wide range of socio-economically situated consumers. Currently, to the best of our knowledge, within the domain of consumer financial planning no formal conceptual model or knowledge classification of financial goals exists which might be utilized as a knowledgebase for applications such as a FinTech recommender system. Achieving financial goals is a key behavior associated with consumer financial capability, a topic of national economic importance. A holistic representation of domain concepts is critical for advancement of solutions to problems pertinent to a domain. One primary reason for the dearth of applications designed to assist consumers with financial goal setting is the absence of a common domain ontology of financial goals. This study addresses a gap in the literature by contributing to the research knowledgebase an ontology for a domain of consumer financial goals. In doing so, it advances scholarly research through novel domain knowledge classification while providing researchers and practitioners with an ontological knowledgebase for indexing and retrieval within applications designed to improve consumer financial capability through identification and recommendation of specific, context-aware financial goals. The ontology could be used, for example, as a knowledgebase for a Personal Financial Recommender System (PFRS), or other financial technology (FinTech) application, designed to assist users with identification, setting, and tracking of financial goals. [ABSTRACT FROM AUTHOR]

• SPARQL debugging with ontology and constraint reasoning. • Tool for discovery and diagnosis of wrong SPARQL queries. • Correctness method for detecting Ill-typed and unsatisfiable queries. • Type validity method for assisting in query completion and repairing. • Online web tool. The discovery and diagnosis of wrong queries in database query languages have gained more attention in recent years. While for imperative languages well-known and mature debugging tools exist, the case of database query languages has traditionally attracted less attention. SPARQL is a database query language proposed for the retrieval of information in Semantic Web resources. RDF and OWL are standardized formats for representing Semantic Web information, and SPARQL acts on RDF/OWL resources allowing to retrieve answers of user's queries. In spite of the SPARQL apparent simplicity, the number of mistakes a user can make in queries can be high and their detection, localization, and correction can be difficult to carry out. Wrong queries have as consequence most of the times empty answers, but also wrong and missing (expected but not found) answers. In this paper we present two ontology and constraint reasoning based methods for the discovery and diagnosis of wrong queries in SPARQL. The first method is used for detecting wrongly typed and unsatisfiable queries. The second method is used for detecting mismatching between user intention and queries, reporting incomplete, faulty queries as well as counterexamples. We formally define the above concepts and a list of examples to illustrate the methods is shown. A Web online tool has been developed to analyze SPARQL queries according to the proposed methods. [ABSTRACT FROM AUTHOR]

• A Decision Support System for urban infrastructure management is presented. • The system handles the asset dependencies using a set of domain ontologies and rules. • The system models both the uncertainty and incompleteness of data and knowledge. • A web-based prototype has been developed and demonstrated to potential users. Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact. [ABSTRACT FROM AUTHOR]