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Representing knowledge with ontologies and performing reasoning with semantic reasoners is important in many intelligent applications. However, existing reasoners do not take into account the available resources of the device where they run, which can be important in many scenarios such as reasoning with very large ontologies or reasoning on resource-constrained mobile devices. In this paper, we propose a novel approach to adapt the size of knowledge managed by applications, taking into account several criteria about resources available (such as time, memory, and battery consumption), at the same time. Thus, rather than giving no answer due to the lack of resources needed to deal with a full ontology, we propose a novel architecture to compute a subontology to provide an incomplete answer at least. Our approach makes use of existing approaches to predict the performance of semantic reasoners and to compute ontology modularisation and ontology partition, but taking into account the associated resource consumption. We also propose a novel measure to estimate the semantic loss when replacing the original ontology by a subontology. Finally, we present an implementation and evaluation of the whole pipeline, showing that the semantic loss incurred in the process is acceptable. [ABSTRACT FROM AUTHOR]

Considering that the world population is aging, health-support issues are in evidence, and many dangerous situations concerning users in their living environment may arise. This paper presents an approach to allow Ambient Assisted Living (AAL) systems to identify and predict situations that may endanger users in their living environment. Elderly people generally have difficulties to perform daily activities as using some electronic device, cooking, dressing among others. This paper offers an alternative to acquire comprehension of a former person's behavioral providing this knowledge when cognitive impairments will occur. This is a significant improvement to traditional expert systems applied to AAL as the handling of the knowledge base is adapted to the failing users’ behavior. We consider the user profile dynamics where new and different situations not initially planned in the development of the systems may occur. This approach was developed considering the principles of reactive and proactive behavior and the extensibility capacity of the system. Systems developed following the above-described principles can react to the current situations and anticipate proactively a dangerous situation eliminating it or controlling its impact. The approach is evaluated and verified by a case study where unwanted, dangerous situations were created and subsequently detected. The case study is based on a simulated smart house fulfilled with sensors and appliances. An application was build based on the developed approach, and it was capable of choosing the most appropriate actions to solve the unwanted situations. The main contributions of the present work are the identification and prediction of situations in smart houses, an approach developed with the principles of reactive and proactive behavior validated with a case study and supported by a probabilistic ontology. [ABSTRACT FROM AUTHOR]

Ontology has received considerable attention in the semantic technology community. This paper discusses the roles of ontology modularization in human-web interface design for knowledge sharing. It stresses that ontology can support human-web interaction as a key component of knowledge sharing on the Internet. The paper proposes an approach of ontology modularization to navigating and searching web portals for the purposes of knowledge sharing. A case study of human-web interface design using ontology modularization for a commercial software web portal is used to illustrate the application of the proposed approach. [ABSTRACT FROM AUTHOR]

To practice the evidence-based medicine, clinicians are interested to find the most suitable research for the clinical decision making. The use of knowledge graphs (KGs) in evidence-based clinical decision support systems is becoming increasingly popular. However, existing KG construction frameworks are not fully automated and contextualized, thus unable to adapt to new domains and incorporate constantly changing information into their knowledge base, resulting in loss of relevance over time. Furthermore, existing KGs construction frameworks don't generate KG that provide relevant information within an acceptable response time for evidence-based practitioners because the organization of constructed subgraphs is neither topic-specific nor evidence-based PICO (Participants/Problem P, Intervention-I, Comparison C, Outcome O) query-friendly. By employing concept extraction, semantic enrichment, optimized clustering, and state of art Recurrent Neural Networks (RNNs) with BioBERT based encoded representation to categorize PICO elements and predict relationships between concepts using huge corpus of publicly available literature on COVID-19 and cerebral aneurysm, this paper proposes a topic specific, PICO enabled, and fully automated framework to curate information and create KG of different clinical domains. The evaluation shows that the proposed framework achieves significant improvement over baseline models and has 93 %, and 82 % accuracy on aneurysm and COVID data set respectively for PICO classification. Also, the relationship extraction module has an accuracy of 96 % with precision and recall being 92 %, and 90 % respectively. [ABSTRACT FROM AUTHOR]

Semantic Web technologies (especially, ontologies) are very important nowadays in intelligent applications to represent the important knowledge in an application domain. Shortly after the birth of ontologies, the inability to adequately handle knowledge affected by imprecision or vagueness, which is inherent to many real-world scenarios, was observed. Fuzzy ontologies were proposed and developed as a solution. However, the problem of building fuzzy ontologies has received little attention in the literature. In fact, there are few examples of publicly available fuzzy ontologies, suggesting that new techniques helping users to build fuzzy ontologies are needed. In this paper, we develop advanced strategies to learn fuzzy datatypes for fuzzy ontologies. Our approach uses real-world data and unsupervised clustering algorithms to compute the fuzzy datatype definitions in an automatic way. Our learning algorithm is implemented in the Datil system, a tool supporting different input formats and devices, including a mobile version. Our approach could be orthogonal to other existing approaches learning fuzzy subclass axioms. • A novel learning algorithm for fuzzy ontology datatypes. • An incremental version of the algorithm to support dynamic learning. • An implementation, the Datil system, for both desktop and mobile devices. • An evaluation of the performance on mobile devices. [ABSTRACT FROM AUTHOR]

Abstract: Ontologies have been largely exploited in many domains and studies. In this paper, we present a new application of a domain ontology for generating personalized user interfaces for transportation interactive systems. The concepts, relationships and axioms of transportation ontology are exploited during the semi-automatic generation of personalized user interfaces. Personalization deals with the capacity of adaptation of a user interface, reflecting what is known about the user and the domain application. It can be performed on the interface container presentation (e.g., layout, colors, sizes) and in the content provided in their input/output (e.g., data, information, document). In this paper, the transportation ontology is used to provide the content personalization. This paper presents the ontology and how it is used for the personalization of user interfaces for developing transportation interactive systems by model-driven engineering. [Copyright &y& Elsevier]

Abstract: Data summarization has recently received considerable attention in the knowledge systems community. This paper discusses the design of data summarization query system. Based on an initial analysis of requirement representations in data summarization, the study develops a generic organization of ontology for data summarization query system. Furthermore, this paper proposes a framework of ontology-based query language of data summarization based on the proposed ontology structure. A prototype project of data summarization ontology-based Query by Examples (QBE) for summarizing the data incompleteness demonstrates the effectiveness of the proposed framework. [Copyright &y& Elsevier]

Abstract: With the development of the Semantic web the use of ontologies as a formalism to describe knowledge and information in a way that can be shared on the web is becoming common. The explicit conceptualization of system components in a form of ontology facilitates knowledge sharing, knowledge reuse, communication and collaboration and construction of knowledge rich and intensive systems. Semantic web provides huge potential and opportunities for developing the next generation of e-learning systems. In previous work, we presented tutoring system named Protus (PRogramming TUtoring System) that is used for learning the essence of Java programming language. It uses principles of learning style identification and content recommendation for course personalization. This paper presents new approach to perform effective personalization highly based on Semantic web technologies performed in new version of the system, named Protus 2.0. This comprises the use of an ontology and adaptation rules for knowledge representation and inference engines for reasoning. Functionality, structure and implementation of a Protus 2.0 ontology as well as syntax of SWRL rules implemented for on-the-fly personalization will be presented in this paper. [Copyright &y& Elsevier]

Abstract: This paper suggests a cause-and-effect function network (CEFN) to support technology innovation in a direct way. To support this CEFN, a cause-and-effect relationship, a function model and an ontological approach are proposed. In the CEFN, technologies from different domains can be connected because defining technologies as functions provides abstractive, representative and formal expressions of them. Using ontology guarantees linguistic disambiguation in defining or searching heterogeneous technologies. Consequently, this paper summarizes construction of a CEFN which can be used as a searching system, by which users can get results by making a query using only ‘Action–Object’ (verb–noun) combinations. The proposed system can be used both for problem solving and for discovering technological opportunity. In this paper, a method consisting of procedure and analyses to build the CEFN is suggested, and a case study is performed to demonstrate the suggested method and system. [Copyright &y& Elsevier]

Abstract: Although ontologies and organizational learning are issues that have been discussed for many years, there is not an approach on literature that gives an overview about how both issues have been applied together. This literature review has the objective of exploring how ontologies are being applied in the organizational learning process recently; as a consequence, only studies from the year of 2005 onwards have been searched. The identification process produced 353 papers from 11 different databases. After applying the exclusion criteria, the set was reduced to 11 papers, which clearly fitted to the criteria defined for accomplishment of the systematic review, which were then analyzed and classified. The papers have been classified according to the structure and level of the ontologies. Furthermore, the Information Technology (IT) used in conjunction with ontology was identified, as well as the way ontologies and IT can act as a means of facilitating the organizational learning process. It was observed that although ontologies are rather important, a very few number of researches have applied ontologies in the organizational learning processes. In a general way, ontologies and IT encourage the sharing of knowledge and formalization. [Copyright &y& Elsevier]

Highlights • The enhanced Word2vec with ontology knowledge achieved an F1-score of 40.7%. • Increasing size of the training data will increase extraction quality. • The weights affect the word embedding for keyphrase extraction. Abstract A large amount of unstructured textual data about geoscience structures and minerals is buried in geoscience documents and is unused. Automatic keyphrase extraction provides opportunities to leverage this wealth of data for analysis and knowledge discovery. However, keyphrase extraction remains a complicated task, and the performance of state-of-the-art approaches is still low. Automatic discovery of high-quality and meaningful keyphrases requires the application of useful knowledge and suitable techniques. Seeing both challenges and opportunities in the situation described above, this paper proposes an ontology and enhanced word embedding-based (OEWE) methodology for automatic keyphrase extraction from geoscience documents. We first develop a quantitative analysis for keyphrase extraction evaluation based on conditional probability and the naive Bayesian model, which is valuable when human-annotated keyphrases are not available. The domain ontology is then performed on a multiway tree to enrich the domain-specific knowledge on certain concepts and relationships in a domain. Simultaneously, word2vec, a model of a word distribution using deep learning, is updated by applying the geological ontology, and it links domain background information and identifies infrequent but representative keyphrases. We use two homemade geoscience datasets to evaluate the performance of OEWE. We compare our method with frequency, term frequency-inverse document frequency (TF-IDF), TextRank and rapid automatic keyword extraction (RAKE), finding that our method achieves average F1 scores of 30.1% and 40.7% on two manually annotated datasets. [ABSTRACT FROM AUTHOR]

Highlights • A semantic approach to represent and validate Big Data analytics is proposed. • An OWL Ontology and SWRL rules are developed for reasoning in workflow design. • The proposal is validated with two real-world (traffic) and academic cases study. • Obtained semantized data successfully recommends and validate Big Data tasks. • We provide actual Big Data practitioners with software to enhance their analytics. Abstract Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated. [ABSTRACT FROM AUTHOR]

Abstract: Knowledge intensive service processes should be managed in a human-oriented way since human workers who naturally undertake complex operations of an intellectual nature in the processes are the most valuable resources for service-providing companies. For the human-oriented management, a role-based approach has been widely applied. The foremost step in the role-based human process management is to assign relevant roles to appropriate humans. The role assignment task is also highly dependent on the ability of humans to consider information about roles and humans. In order to manage the role assignment in a human-oriented way, this paper presents two fundamental principles upon which the assignment task is carried out. First, roles should not be assigned until they need to be activated; second, the authority for the role assignment should be distributed to participants in the processes. In order to support the principles, the information should be explicitly and formally described to enable all the participants to easily understand and utilize it. Consequently, this paper proposes a methodology for managing the role assignment in a human-oriented way by adopting an ontological approach which can precisely describe the information. From the proposed methodology, human workers can be notified which roles are triggered to be activated and wait to be assigned. Moreover, they can be provided appropriate candidate humans when carrying out the assignment task. The contribution of this paper is to suggest a role assignment methodology that not only lightens the assignment workload by providing the capability of identifying a small set of appropriate candidates but also enables the efficient assignment by streamlining the assignment task. [Copyright &y& Elsevier]

Abstract: In recent years the widespread application of outsourcing to help improve finance and maintain competitiveness is growing rapidly, and has been accompanied by a growth of academic research in this field. Most of the outsourcing literature has focused primarily on the social and cultural impacts, the organisational and management perspectives of outsourcing and very few tools have been developed to assist managers in making outsourcing decisions. Incorrect decision-making in outsourcing can lead to devastating consequences for their business. To guide decision makers in managing the complexity of outsourcing a holistic approach to outsourcing has been developed by taking business, information and organisational perspectives (HABIO). The purpose of this paper is to demonstrate how HABIO can be applied to help managers evaluate the impact of their decision-making by providing them with a set of ‘what-if’ scenarios, which are enacted by intelligent agents. The case example, discussed in this paper, demonstrates that HABIO can be applied effectively using a multi-agent system to analyse the outsourcing decision for a call centre sector in the retail industry. A prototype of the multi-agent system is presented where intelligent agents are endowed with specific domain ontologies, appropriate to solve a specific problem, and with a reasoning mechanism to achieve specific goals and to collaborate together to produce a set of “what-if scenarios” to the outsourcing managers. [Copyright &y& Elsevier]

Abstract: Computer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application. [Copyright &y& Elsevier]

Abstract: Environmental impact assessment (EIA) analyses the effects of human activity, ecosystem integrity and the quality of the environmental services that can be provided by them. This analysis must be done prior to project execution in order to have a preventive nature. The application of new technologies in EIA needs an adequate structure of the knowledge, so a lot of problems appear because depending of the country, different terminologies are used. This paper describes a proposal of ontology for EIA to establish a conceptual framework and the building process. In addition, we have developed a friendly web interface in order to provide easy access to the knowledge and the possibility to suggest changes in the ontology to environmental experts. The objectives are gathering and providing the EIA terminology and facilitating structuring and the development of EIA methodologies. Finally, the paper describes how the ontology can be used in EIA applications with knowledge mobilization. [ABSTRACT FROM AUTHOR]

Abstract: Semantic heterogeneity should be dealt with for supporting automated information sharing process between information systems in distributed environments. To do so, traditional approaches have been based on explicit mapping between ontologies obtained from human experts of the particular domain. However, the manual tasks are very expensive, so that it is difficult to obtain ontology mappings between all possible pairs of the information systems. Thereby, in this paper, we propose a semantic distributed system to make the existing mapping information sharable and exchangeable. It means that the proposed system can collect the existing mappings and aggregate them. Consequently, we can estimate the ontology mappings in an indirect manner. In particular, this paper focuses on query propagation on the distributed networks. Once we have found the indirect mapping between the systems, the queries can be efficiently transformed to automatically exchange knowledge between heterogeneous information systems. [ABSTRACT FROM AUTHOR]

Abstract: With the growing popularity of Internet technology, information is increasing in a geometric-progressively manner. How to find advantage information to meet user queries in the information torrent of Internet has become the first goal of lots of scholars. This paper focused on developing an ontology-supported information integration and recommendation system for scholars. Not only can it rapidly integrate specific domain documents, but also it can extract important information from them by information integration and recommendation ranking. The core technologies adopted in this study included: ontology-supported webpage crawler, webpage classifier, information extractor, information recommender, and a user integration interface. The preliminary experiment outcomes proved both the webpage crawler and classifier in the core technology can achieve an excellent precision rate of webpage treatment and the reliability and validation measurements of the whole system performance can also achieve the high-level outcomes of information recommendation. Further, this paper also discussed and examined the advantages and shortcomings of the construction of a recommendation system with different approaches and accordingly provided the design philosophy of customized services for recommendation systems. [Copyright &y& Elsevier]

This paper proposed the techniques of ontology and linguistics to develop a fully-automatic annotation technique, coupling with an automatic ontology construction method, could play a key role in the development of Semantic Portals. An ontology-supported portal architecture: OntoPortal was proposed according to this technique, in which three internal components Portal Interface, Semantic Portal, and OntoCrawler was integrated to rapidly and precisely collect information on Internet and capture true user’s intention and accordingly provide high-quality query answers to meet the user requests. This paper also demonstrated the OntoPortal prototype which defined how a semantic portal is interacting with the user by providing five different types of interaction patterns such as including keyword search, synonym search, POS (Part-of-Speech)-constrained keyword search, natural language query, and semantic index search. The preliminary experiment outcomes proved the technology proposed in this paper to be able to really up-rise the precision and recall rates of webpage searching and accordingly showed that it can indeed retrieve better semantic-directed information to meet user requests. [Copyright &y& Elsevier]

Over the years, many Artificial Intelligence (AI) approaches have dealt with the diagnosis problem and its application in complex environments such as medical domains. Model-Based Reasoning (MBR) is one of the approaches that traditionally have tried to solve this problem thanks to its capacity for modelling and reasoning. The consideration of the temporal dimension in these domains is a challenging topic in MBR, especially if temporal imprecision is taken into account. Unfortunately, despite there being many successful MBR systems, there are still two fundamental problems in their development at the aforementioned domains: (1) the degree of dependency between the model used and the domain; and (2) the reutilization of the systems when the domain changes. First this paper proposes a set of basic requirements for the design of Knowledge-Based Systems that will help to solve the problem of temporal diagnosis for environments of high conceptual complexity. From these principles and through a deep analysis of the various approaches present in AI we establish a generic framework that addresses both goals by integrating MBR and ontologies for domain knowledge representation in order to describe a intermediate model representation to facilitate the low dependency between the model and the application domain. Finally, this paper demonstrates the use of the framework by developing a diagnosis system within a real medical environment (Intensive Care Unit) with a step-by-step description of the process, from the architecture through to implementation. [Copyright &y& Elsevier]

Abstract: The problem of service sharing in a Grid environment arises from the heterogeneity of ontologies. The discovery and description of Grid services based on the heterogeneous ontologies might give rise to misunderstanding of the contents. In order to reduce and ultimately to remove the misunderstanding, a domain-specific ontology should be shared among concerned parties, and the abilities of Grid services should be discovered and described based on that shared ontology. However, since the ontology evolves as time goes by, the shared ontology for the Grid services should have a flexible infrastructure that has an ability to reflect the changes in ontologies. This paper proposes a flexible ontology management approach for discovery and description of Grid service capabilities supporting ontology evolution whose goal is to enhance the interoperability among Grid services. In this approach, concepts and descriptions in an ontology are defined independently, and they are connected by relationships. In addition, the relationships are updated based on real-time evaluations of ontology users in order to flexibly support ontology evolution. A bottom-up ontology evolution means such environment that allows ontology users to evaluate impact factors of concepts in an ontology and that results of the evaluation are reflected to the modification of the ontology. The contribution of this paper is to suggest the ontology management framework that not only enables semantic discovery and description of a Grid service capability but also supports a bottom-up ontology evolution based on the users’ evaluations. [Copyright &y& Elsevier]

Abstract: Two agents previously unknown to each other cannot communicate by exchanging concepts (nodes of their own ontology): they need to use a common communication language. If they do not use a standard protocol, most likely they use a natural language. The ambiguities of it, and the different concepts the agents possess, give rise to imperfect understanding among them: How closely concepts in ontology OA map 1 [1] OA and OB are the ontologies of agents A (the talker or sender) and B (the listener), in the rest of the paper. to which of OB? Can we measure these mismatches? Given a concept from ontology OA, a method is provided to find the most similar concept in OB, and to measure the similarity between both concepts. The paper also gives an algorithm to gauge du(A, B), the degree of understanding that agent A has about the ontology of B. The procedures use word comparison, since no agent (except the Very Wise Creature, VWC) can measure du directly. Examples are given. [Copyright &y& Elsevier]

Abstract: Developing a personalized, user-centric system is one of today''s challenging issues in ubiquitous network-based systems, especially personalized reminder systems. Such a personalized reminder system has to identify the user''s current needs dynamically and proactively based on the user''s current context, such as location and current activity. However, need identification methodologies and their feasible architectures for personalized reminder systems have so far been rare. Hence, this paper aims to propose a proactive need identification mechanism by applying agent and semantic web technologies for a personalized reminder system, which is one of the supporting systems for a robust ubiquitous service support environment. We revisit associationism in order to understand a buyer''s need identification process, and we adopt the process as ‘purchase based on association’ to implement a personalized reminder system. Based on this approach, we have shown how an agent-based semantic web service system can be used to realize a personalized reminder system which identifies a buyer''s need autonomously. We have created a prototype system, NAMA (Need Aware Multi-Agent), to demonstrate the feasibility of the methodology and of the mobile settings framework that we propose in this paper. NAMA embeds a Bluetooth-based location-tracking module and identifies what users are currently looking at through their mobile devices. Based on these capabilities, NAMA considers the context, user profile with preferences, and information about currently available services to discover the user''s current needs and then link the user to a set of services, which are implemented as web services. [Copyright &y& Elsevier]

Message filtering is one of the techniques that can help achieve scalability of wireless communication and, eventually, mobile commerce. The core of message filtering is filtering decision-making: to make decisions as to whether a specific message should be filtered or not. The filtering decision is multicriteria decision making in nature, such as analytic hierarchy process (AHP) which ranks a set of alternatives and the choice of a preferred one. Under context-aware situations, however, we are concerned that the number of contextual states is too big, resulting in an AHP model that is too big to compute efficiently. Moreover, in such a situation, user preferences on filtering are dynamic or temporal, according to the change of contexts. Hence, an idea of context-aware message filtering is proposed in this paper. However, to handle a large set of AHP models, each of which fits a specific contextual state, is still difficult. Hence, in this paper, we propose a methodology for intelligent context-aware message filtering by applying a case-based reasoning that learns to adapt to the inexact matching and AHP. Moreover, a default AHP model is generated from the user''s personal preference ontology. The default AHP model is refined according to the user''s feedback to revise the case base. [Copyright &y& Elsevier]

This paper is a detailed case study of building Code Tutor, a Web-based intelligent tutoring system (ITS) in the domain of radio communications. It is ontologically founded and was built using CLIPS and Java-based expert system tools, latest integrated graphical CASE tools for software analysis and design, and Java servlets. In Code Tutor, Apache HTTP Server stores and serves static HTML pages, and Apache JServ Java package enables dynamic interpretation of user defined servlet classes and generation of active HTML pages. XML technology is used to generate files that Code Tutor uses to provide recommendations to the learners. Such a rich palette of integrated advanced technologies has greatly alleviated the system design and implementation, and has also led to interesting solutions of a number of problems common to many ITSs. The paper describes these solutions and useful design decisions, and discusses several practical issues related to architectures of intelligent Web-based applications. [Copyright &y& Elsevier]

• 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]

The correlated exploitation of disparate and heterogeneous data sources is important to the efficacy of many analytics tasks. Currently in application domains of major interest, such as in the maritime and aviation domains, available technology provides real time surveillance data from moving entities, which together with archival static data, can be processed in an integrated way to detect complex events and support decision making. The variety of data in disparate sources, the heterogeneity of data formats, as well as the volume of data, make data retrieval, integration, and especially reasoning with these data, challenging tasks. This paper presents an ontology-based distributed framework that addresses conjunctively these challenges: Data retrieval, integration and reasoning with data from heterogeneous static or regularly updated data sources. The proposed OBDAIR framework provides the means to support building scalable data-driven domain-specific applications that support decision-making and problem-solving. This is achieved by processing large volumes of heterogeneous data close to the sources, supporting knowledge generation in a distributed/decentralized but still unified manner. OBDAIR integrates modular ontology representation frameworks and ontology-based data access frameworks: This article presents an instantiation of OBDAIR using the modular ontology representation framework E − SHIQ , and the Ontop ontology-based access system. This OBDAIR instance has been evaluated at recognising important complex events in the maritime domain using real-world data. Experiments show the potential of OBDAIR to detect complex events in large geographic areas with computational efficiency. [ABSTRACT FROM AUTHOR]

In knowledge discovery, experts frequently need to combine knowledge from different domains to get new insights and derive new conclusions. Intelligent systems should support the experts in the search for relationships between concepts from different domains, where huge amounts of possible combinations require the systems to be efficient but also sufficiently general, open and interactive to enable the experts to creatively guide the discovery process. The paper proposes a cross-domain literature mining methodology that achieves this functionality by combining the functionality of two complementary text mining tools: clustering and topic ontology creation tool OntoGen and cross-domain bridging terms exploration tool CrossBee. Focusing on outlier documents identified by OntoGen contributes to the efficiency, while CrossBee allows for flexible and user-friendly bridging concepts exploration and identification. The proposed approach, which is domain independent and can support cross-domain knowledge discovery in any field of science, is illustrated on a biomedical case study dealing with Alzheimer’s disease, one of the most threatening age-related diseases, deteriorating lives of numerous individuals and challenging the ageing society as a whole. By applying the proposed methodology to Alzheimer’s disease and gut microbiota PubMed articles, we have identified Nitric oxide synthase (NOS) as a potentially valuable link between these two domains. The results support the hypothesis of neuroinflammatory nature of Alzheimer’s disease, and is indicative for the quest for identifying strategies to control nitric oxide-associated pathways in the periphery and in the brain. By addressing common mediators of inflammation using literature-based discovery, we have succeeded to uncover previously unidentified molecular links between Alzheimer’s disease and gut microbiota with a multi-target therapeutic potential. [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]

Platform as a service (PaaS) is one of the Cloud computing services that provide a computing platform in the Cloud, allowing customers to develop, run, and manage web applications without the complexity of building and maintaining the infrastructure. The primary disadvantage for an SME to enter the emerging PaaS market is the possibility of being locked into a certain platform, mostly provided by the market's giants. The PaaSport project focuses on facilitating SMEs to deploy business applications on the best-matching Cloud PaaS offering and to seamlessly migrate these applications on demand, via a thin, non-intrusive Cloud-broker, in the form of a Cloud PaaS Marketplace. PaaSport enables PaaS provider SMEs to roll out semantically interoperable PaaS offerings, by annotating them using a unified PaaS semantic model that has been defined as an OWL ontology. In this paper we focus on the recommendation algorithm that has been developed on top of the ontology, for providing the application developer with recommendations about the best-matching Cloud PaaS offering. The algorithm consists of: a) a matchmaking part, where the functional parameters of the application are taken into account to rule out inconsistent offerings, and b) a ranking part, where the non-functional parameters of the application are considered to score and rank offerings. Τhe algorithm is extensively evaluated showing linear scalability to the number of offerings and application requirements. Furthermore, it is extensible upon future semantic model extensions, because it is agnostic to domain specific concepts and parameters, using SPARQL template queries. [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]

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]

Online jobs search through popular websites are quite beneficial having served for many years as a prominent tool for job seekers and employers alike. In spite of their valuable utility in linking employers with potential employees, the search process and technology utilized by job search websites have not kept pace with the rapid changes in computing capability and machine intelligence. The Information retrieval techniques utilized by these websites rely primarily on variants of manually entered search queries with some advanced similarity metrics for ranking search results. Advancements in machine intelligence techniques have enabled programmatic extraction of pertinent information about the job seeker and job postings without active user input. To this end, we developed a resume matching system, RésuMatcher, which intelligently extracts the qualifications and experience of a job seeker directly from his/her résumé, and relevant information about the qualifications and experience requirements of job postings. Using a novel statistical similarity index, RésuMatcher returns results that are more relevant to the job seekers experience, academic, and technical qualifications, with minimal active user input. Our method provides up to a 34% improvement over existing information retrieval methods in the quality of search results. In addition however, RésuMatcher requires minimal active user input to search for jobs, compared to traditional manual search-based methods prevalent today. These improvements, we hypothesize, will lead to more relevant job search results and a better overall job search experience for job seekers. As an alternative to the fragmented organization-centric job application process, job recruitment websites offered the promise of simplifying and streamlining the job search process. However, these websites offer limited functionality using generic and simplistic information retrieval methods, which being non-domain lead to a poor and frustrating search experience. In this paper, we present RésuMatcher, a personalized job-résumé matching system, which offers a novel statistical similarity index for ranking relevance between candidate résumés and a database of available jobs. In our experiments we show that our method offers a 37.44% improvement over existing information retrieval methods in the quality of matches returned. [ABSTRACT FROM AUTHOR]

In Case Based Reasoning the representation of a case and the similarity measures are two difficult steps in the conception of a system. Often, these steps are developed to resolve one kind of problem. However, in some of them such as recovery treatment processes generation, it is necessary for the system to be able to modify and adapt the representation of a case and the similarity measures with respect of the context and also the kind of solutions proposed. In this paper, authors introduce a new method to represent cases with a flexibility based on a structure in a connectionist model. This flexibility is needed due to the complexity of cases, the number of possible options and to ensure the durability of the system. In a second main contribution, authors introduce a method for the selection of source cases using abstraction, conceptualisation and inference mechanisms. Finally, authors test their system in a CBR developed on SWI-Prolog with different problems. The CBR is applied to find new recovery processes and try to estimate the new upgraded product generated. [ABSTRACT FROM AUTHOR]

Competences represent an interesting pedagogical support in many processes like diagnosis or recommendation. From these, it is possible to infer information about the progress of the student to provide help targeted both, trainers who must make adaptive tutoring decisions for each learner, and students to detect and correct their learning weaknesses. For the correct development of any of these tasks, it is important to have a suitable student model that allows the representation of the most significant information possible about the student. Additionally, it would be very advantageous for this modeling to incorporate mechanisms from which it would be possible to infer more information about the student's state of knowledge. To facilitate this goal, in this paper a new approach to develop an adaptive competence-based recommender system is proposed. We present a methodological development guide as well as a set of ontological and non-ontological resources to develop and adapt the prototype of the proposed recommender system. A modular flexible ontology network previously built for this purpose has been extended, which is responsible for recording the instructional design and student information. Furthermore, we describe a case study based on a first aid learning experience to assess the prototype with the proposed methodology. We highlight the relevance of flexibility and adaptability in learning modeling and recommendation processes. In order to promote improvement in the personalized learning of students, we present a Recommender System prototype taking advantages of ontologies, with a methodological guide, a broad taxonomy of recommendation criteria and the nature of competences. Future lines of research lines, including a more comprehensive evaluation of the system, will allow us to demonstrate in depth its adaptability according to the characteristics of the student, flexibility and extensibility for its integration in various environments and domains. • Competences are an interesting pedagogical support for diagnosis or recommendation. • An adaptive Competence-based Recommender System prototype is presented. • Methodological guide to develop and adapt to multiple environments and domains. • Student modeling, ontology network and non-monotonic diagnosis, is the system core. • Ontology network and diagnosis rules, previously built, are extended and adapted. [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]

Good knowledge would be expected to help a knowledge-based algorithm more than bad knowledge. In this research, the precise effect of good versus bad knowledge on evolutionary algorithms is explored. The testable hypothesis of this paper is that good knowledge will have a significant effect on the evolutionary mutation process, whereas bad knowledge will have no significant effect. A knowledge-guided evolutionary algorithm is developed where ontologies, representing knowledge, are applied to the mutation process. Bad knowledge is represented as a randomly generated ontology, while good knowledge is represented by ontologies constructed with domain knowledge and following a formal ontology development process. Decision trees are evolved to solve a classification problem. Fitness is classification accuracy. The experiment is replicated over 2 data-sets from different domains with one being time-series, financial data and the other being wine data. As hypothesized, poorly constructed, or bad knowledge, has no effect while good knowledge is shown to have a significant effect. Bad knowledge, being random in character in these experiments, has understandably no impact on an already random mutation process. However, employing knowledge to guide the mutation process significantly constrains the traversal of the search space. Employing knowledge in an evolutionary algorithm has the potential to increase the efficiency and accuracy of evolutionary algorithms. [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]

The Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users. [ABSTRACT FROM AUTHOR]

A software product line (SPL) is a set of software systems that have a particular set of common features and that satisfy the needs of a particular market segment or mission. Feature modeling is one of the key activities involved in the design of SPLs. The feature diagram produced in this activity captures the commonalities and variabilities of SPLs. In some complex domains (e.g., ubiquitous computing, autonomic systems and context-aware computing), it is difficult to foresee all functionalities and variabilities a specific SPL may require. Thus, Dynamic Software Product Lines (DSPLs) bind variation points at runtime to adapt to fluctuations in user needs as well as to adapt to changes in the environment. In this context, relying on formal representations of feature models is important to allow them to be automatically analyzed during system execution. Among the mechanisms used for representing and analyzing feature models, description logic (DL) based approaches demand to be better investigated in DSPLs since it provides capabilities, such as automated inconsistency detection, reasoning efficiency, scalability and expressivity. Ontology is the most common way to represent feature models knowledge based on DL reasoners. Previous works conceived ontologies for feature modeling either based on OWL classes and properties or based on OWL individuals. However, considering change or evolution scenarios of feature models, we need to compare whether a class-based or an individual-based feature modeling style is recommended to describe feature models to support SPLs, and especially its capabilities to deal with changes in feature models, as required by DSPLs. In this paper, we conduct a controlled experiment to empirically compare two approaches based on each one of these modeling styles in several changing scenarios (e.g., add/remove mandatory feature, add/remove optional feature and so on). We measure time to perform changes, structural impact of changes (flexibility) and correctness for performing changes in our experiment. Our results indicate that using OWL individuals requires less time to change and is more flexible than using OWL classes and properties. These results provide insightful assumptions towards the definition of an approach relying on reasoning capabilities of ontologies that can effectively support products reconfiguration in the context of DSPL. [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]

Over the last few years, we have experienced a steady growth in e-commerce. This growth introduces many problems for services that want to aggregate product information and offerings. One of the problems that aggregation services face is the matching of product categories from different Web shops. This paper proposes an algorithm to perform this task automatically, making it possible to aggregate product information from multiple Web sites, in order to deploy it for search, comparison, or recommender systems applications. The algorithm uses word sense disambiguation techniques to address varying denominations between different taxonomies. Path similarity is assessed between source and candidate target categories, based on lexical relatedness and structural information. The main focus of the proposed solution is to improve the disambiguation procedure in comparison to an existing state-of-the-art approach, while coping with product taxonomy-specific characteristics, like composite categories, and re-examining lexical similarity and similarity aggregation in this context. The performance evaluation based on data from three real-world Web shops demonstrates that the proposed algorithm improves the benchmarked approach by 62% on average F 1 -measure. [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]

Providing alert communication in emergency situations is vital to reduce the number of victims. However, this is a challenging goal for researchers and professionals due to the diverse pool of prospective users, e.g. people with disabilities as well as other vulnerable groups. Moreover, in the event of an emergency situation, many people could become vulnerable because of exceptional circumstances such as stress, an unknown environment or even visual impairment (e.g. fire causing smoke). Within this scope, a crucial activity is to notify affected people about safe places and available evacuation routes. In order to address this need, we propose to extend an ontology, called SEMA4A (Simple EMergency Alert 4 [for] All), developed in a previous work for managing knowledge about accessibility guidelines, emergency situations and communication technologies. In this paper, we introduce a semi-automatic technique for knowledge acquisition and modeling on accessible evacuation routes. We introduce a use case to show applications of the ontology and conclude with an evaluation involving several experts in evacuation procedures. [ABSTRACT FROM AUTHOR]

As many software development teams have started to adopt agile methods, a vast amount of valuable experiences have been reported on in both academic and industrial knowledge bases. This information has been used through various approaches to guide and help practitioners finding suitable practices for their software development projects. Nevertheless, not many of these approaches could gather the available experiences to make them systematically reusable and help practitioners understanding agile practices in depth. To the best of our knowledge, only one ontology has been created to solve this problem; some limitations related to its quality and usability make it nevertheless unqualified to serve the intended purpose. The aim of this paper is to build an expert system (i.e. an evidence-based tool) to ease agile practices adoption by efficiently and effectively providing information on them. Firstly, we improve the concepts and relationships in the aforementioned ontology and theoretically validate it using a large data-set of agile practices adoption experiences collected through a Systematic Literature Review (SLR). Secondly, we develop a supporting tool having a friendly Graphical User Interface (GUI) allowing to use the ontology as a concrete agile practice knowledge provider. Finally, we empirically validate the enhanced ontology and evaluate the supporting tool using a survey with agile experts. Our supporting tool can help practitioners to decide what practice to adopt, how to adopt it, how to solve practical issues, etc. The ontology and the tool materialize our contribution to the field of systematic agile practices adoption. • Successful agile practice adoption requires an in depth understanding of its content. • Existing reports on agile practice adoption experiences are essentially unexploitable. • An ontology is used as an efficient solution to recycle practices adoption experience. • A corpus-based approach and a survey are used for validating the ontology. • An expert system supports practitioners querying knowledge on practice adoption. [ABSTRACT FROM AUTHOR]

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]

Highlights: [•] This paper argues the need for economics knowledge sharing. [•] We demonstrate how economics knowledge sharing can be effectively achieved. [•] Three search functions are implemented based on OEKSS. [•] We propose instrumental variable recommendation algorithm (IVRA). [•] IVRA provides a systematic way to find instrumental variables in OEKSS. [Copyright &y& Elsevier]

Abstract: Organizational goals serve as the most important achievement target in every organization. Even though some researchers have developed the concept of the organization goals, but structuring the organization goals model is always questionable by the way it is being used. In this paper, we propose ontology to develop a unified model for the organization goals structure. We review the recent literature on the organization modelling and ontology development as an effort to evaluate the organization goals using a metrics for the achievement of the organization goals. We suggest that the metrics is important to identify the relevant organization data in relation to the organization goals conformance. In order to achieve this purpose, we investigate various associated concepts and organize the literature based on the organization goals, organization ontology and metrics model. We observe our proposed models are important for domain experts and entrepreneurs to evaluate the relevant organization data and to assist them in decision making. In summary, the contribution of this survey may serve as a first step in understanding the evaluation of the organization data for the achievement of the organization goals. [Copyright &y& Elsevier]