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In today's energy-dependent world, ensuring the safety and efficiency of lithium-ion batteries is crucial. Early representation of anomalies becomes essential for optimizing performance, reducing disruptions, and prolonging battery lifetime in electric vehicle applications. This objective necessitates the integration of data from distributed and heterogeneous sources, a challenge traditionally tackled by semantic web technologies. In response, this paper introduces an ontology-based model that capitalizes on representing anomalies in lithium-ion batteries. Ontologies play a vital role in representing knowledge in a machine-interpretable format. Our approach enriches sensor data with contextual information, employing structured concepts, rules, and semantics specifically designed for representing anomalies in lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Ontology Alignment (OA) is a complex, demanding and error-prone task, requiring the intervention of domain and Semantic Web experts. Automating the alignment process thus becomes a must-do, especially when involving large datasets, to at least produce a first input for human experts. Automated ontology alignment could benefit from the outstanding language ability of Large Language Models (LLMs), which could implicitly provide the background knowledge that has been the Achilles' heel of traditional alignment systems. However, this requires a correct evaluation of the performance of LLMs and understanding the best way to incorporate them into more specific tools. In this paper, we show that a naive prompting approach on the popular GPT-4 model could face several problems when transferred to real-world use cases. To this end, we replicated the methods of Norouzi et al. (2023), applied to the OAEI 2022 conference track, on a reference alignment between a pair of datasets (reduced versions of two popular thesauri: European Commission's EuroVoc and TESEO, from the Italian Senate of the Republic), which has never been tested in OAEI evaluation campaigns. This reference alignment has several features common to real-world use cases: it is has a larger size than those considered in the study we replicated, it is not published online and is therefore not subject to data contamination and it involves relations between concepts that are more complex than simple equivalence. The replicated methods achieved a significantly lower performance on our reference alignment than on the OAEI 2022 conference track, suggesting that size, data contamination, and semantic complexity need to be considered when using LLMs for the alignment task. [ABSTRACT FROM AUTHOR]

User-centred systems need to incorporate solutions to make them easier to use. Based on a domain ontology, our system has an extensive knowledge base that is populated by a semantic web platform that we have built. This knowledge base needs to be queried on demand by users due to the limitations of the search engine integrated into the semantic web platform. However, this task is not trivial for a human being due to the complexity of the schemas making up the knowledge base and the inability to perform SPARQL queries to obtain answers. In this paper, we propose an approach based on skills questions that we have defined with domain experts and that have helped validate our domain ontology. This approach allows users to ask their questions in natural language to obtain an answer. To achieve our goal, we used natural language processing tools, in particular Spacy's pre-trained French pipeline. The TF-IDF weighting method was used to calculate the similarity score between two sentences. [ABSTRACT FROM AUTHOR]

The increasing volume of data in various environments such as IoT and the need to maintain data privacy and security have led to the development of usage control models. Usage control policies are models that enable fine-grained access control over data by enforcing restrictions on how users can use the data. Semantic mechanisms, on the other hand, use context and meaning to identify potential security threats and prevent them from accessing sensitive information. Although not widely explored, merging these two techniques could create an efficient mechanism to help ensure the confidentiality, integrity, and availability of critical data and resources. This paper aims to encourage this research path by proposing a translation model that converts usage control rules into SWRL. In particular, we consider during our approach the notions of contexţ permission and prohibition. The proposition is validated by constructing a multi-layer proof of concept that use ontologies and OWL for implementing the translation model. Furthermore, to ascertain the practicality of our approach, a time processing evaluation is conducted, and the results are found to be satisfactory. [ABSTRACT FROM AUTHOR]

Nowadays, many health centers use Hospital Information Systems (HIS) for the daily management of center activities such as patient data management. In Burkina Faso, many HIS are used for patient data management but these systems cannot cooperate because the data sources are often heterogeneous. In order to guarantee a better diagnosis in patient management, physicians need to access these data sources in a unique way through queries. For better analysis of a patient's situation, the health worker may want to access multiple data sources that do not belong to the same health center. This is only possible if the HIS involved interoperate. In a previous work, we proposed an interoperability architecture based on semantic web services. This solution has the advantage of not modifying the current organization of health centers. Indeed, for a complex query, a composition of web services is a solution to satisfy different needs. In this paper, we detail our approach for composing semantic web services. In our approach, the functionalities of each HIS application will be implemented by a web service semantically annotated by an ontology. An ontology-based mediation service is used to enrich the physician query and the MiniCon algorithm to create a composite web service. The composite web service is executed to return the requested data. [ABSTRACT FROM AUTHOR]

Breast cancer is a disease that primarily affects women, but it can also affect men, although in a much smaller percentage. Recently, doctors have made great strides in this trend of early detection and treatment of breast cancer to reduce the number of deaths caused by this serious disease. Moreover, researchers are analyzing massive amounts of sophisticated medical data using a combination of statistical and machine learning approaches to help clinicians predict breast cancer. In the presented work, an ontological model based on the decision tree algorithm capable of reliably predicting breast cancer has been demonstrated. The method consists of extracting rules from the decision tree algorithm that distinguish between malignant and benign breast cancer patients, and then implementing these rules in the ontological reasoner via the Semantic Web Rule Language (SWRL). The results indicated that the ontological model achieved the highest prediction accuracy of 97.10%. [ABSTRACT FROM AUTHOR]

Semantic digital technologies are as useful as they are difficult to implement. During its existence, the concept of the Semantic Web has still found very limited application and extremely slow development. The reasons for this are serious differences in the structure of hypertext and the Semantic Web as a superstructure above it, and the rules that circumscribed the development of the internet. Semantic search engines still do not implement semantic search in its pure form: various data organization schemes and data visualization formats make it a little easier to analyze facts, add them to databases, but do not make knowledge bases out of the latter, do not allow working with facts as with knowledge. In addition, the user interfaces of such systems tend to be either very complex or counter-intuitive. Nevertheless, there is every reason to believe that with the use of modern technologies, the ideas of semantization of the web and its content can already be improved and gradually implemented: for this it is necessary to get rid of some false expectations of semantic technologies, to direct efforts towards the development of web native formats of semantic markup and try to take advantage of the increasingly common voice input of household devices. [ABSTRACT FROM AUTHOR]

The automatic safety review of substation design model greatly depends on the formal representation of the safety constraints residing in the building codes written in natural language. In order to overcome the difficulties arising from the hard coding and semantic web approaches, the visual coding method has been studied for representing safety constraints. Nevertheless, very little research explores the formal representation of safety constraints from the query intent viewpoint. Therefore, seven basic query intents are categorized, and simultaneously their mapping with Gremlin query segments is also defined. Then, the visually coding framework is also developed to simplify the representation of query intents. The case study implies that the visual coding of query intents provides an effective and explicit vehicle to describe the safety constraints. In this way, the natural language safety constraints can be automatically converted into Gremlin codes, and moreover the declarative manner of integrating Gremlin queries not only further save time on manually sequencing multiple query tasks, but also can optimize the traversal of graph database. [ABSTRACT FROM AUTHOR]

Knowledge engineering has a vital role in advancing the semantic web, in which ontologies play a key role in data interoperability and integration. One of the key issues in ontology engineering is how to handle the subsequent updates in the ontologies. A number of concerns need to be considered while working on the ontology change, such as, tracking ontology versions and heterogeneity issues. Ontology change management has been partially addressed by different researchers in overlapping research areas. However, a concrete description of the problem and its related concerns are still not available in the literature. Our work aims to present an overview of ontology change management and its concerns. We point up the need for modularization in ontology change management based on its advantages in the context of ontology reuse from different contextual viewpoints. For this purpose, we propose a protege plugin for reusing OWL modules, and allowing a safe/clean manual integration and reuse of different ontology modules. [ABSTRACT FROM AUTHOR]

In social media, human-generated web data from real-world events have become exponentially complex due to the chaotic and spontaneous features of natural language. This may create an information overload for the information consumers, and in turn not easily digest a large amount of information in a limited time. To tackle this issue, we propose to use Complex Event Processing (CEP) and semantic web reasoners to disentangle the human-generated data and present users with only relevant and important data. However, one of the key obstacles is that the human-generated data can have no structured meaning sometimes even for the speaker, hindering the output of the CEP. Therefore, in order to adapt to the CEP inputs, we present two different techniques that allow for the discrimination and digestion of value of human-generated data. The first one relies on the Variable Sharing Property that was developed for relevance logics, while the second one is based on semantic equivalence and natural language processing. The results can be given to CEP for further semantic reasoning and generate digested information for users. [ABSTRACT FROM AUTHOR]

Buildings are the largest energy consumers in Europe and are responsible for approximately 40% of EU energy consumption and 36% of the greenhouse gas emissions in Europe. Two-thirds of the building consumption is for residential buildings. To achieve energy efficiency, buildings are being integrated with IoT devices through the use of smart IoT services. For instance, a smart space heating service reduces energy consumption by dynamically heating apartments based on indoor and outdoor temperatures. The W3C recommends the use of the Web of Things (WoT) standard to enable IoT interoperability on the Web. However, in the context of a smart building, the ability to search and discover building metadata and IoT devices available in the WoT ecosystems remains a challenge due to the limitation of the current WoT Discovery, which only includes a directory containing only IoT devices metadata without including building metadata. Integrating the IoT device's metadata with building metadata in the same directory can provide better discovery capabilities to the IoT services providers. In this paper, we integrate building metadata into the W3C WoT Discovery through the construction of a Building Description JSON-LD file. This Building Description is integrated into the W3C WoT Discovery and based on the domOS Common Ontology (dCO) to achieve semantic interoperability in smart residential buildings for the WoT IoT ecosystem within the Horizon 2020 domOS project. This integration results in a Thing and Building Description Directory. dCO integrates the SAREF core ontology with the Thing Description ontology, devices, and building metadata. We have implemented and validated the WoT discovery on top of a WoT Thing and Building Description Directory. The WoT Discovery implementation is also made available for the WoT community. [ABSTRACT FROM AUTHOR]

An increasing attention is paid to fault detection in manufacturing. Researches are carried out in order to improve the quality and productivity in such systems. Machine Learning (ML) techniques are often used for fault detection tasks. Besides, ontology and semantic web technologies have a great potential to represent, organize and reuse the expert knowledge. In this paper, a Knowledge-based fault detection method for manufacturing processes is proposed, relying on Ontology and Machine Learning techniques. The approach is iterative in the sense that new faults can be detected by ML and added as new knowledge into the ontology periodically. It eases fault detection in industrial contexts, where faults are generally rare. Experiments conducted with seven fault-detection oriented UCI datasets have shown the effectiveness of our proposal. It is composed of a real-time classifier with a reject option, to enable the detection of new defects based on the existing knowledge described in the ontology. When new defect signature is discovered, it is added into the ontology as new knowledge through a semantic mapping. As a result, we have shown the ability of the proposed architecture to detect new faults and to increase the overall accuracy as new faults are included in the ontology. It also conducts to an evolving ontology that will be used in further research to support a generalization process to enable the detection of known defects in new contexts. [ABSTRACT FROM AUTHOR]

Morocco has launched several e-government programs to develop efficient public e-services which meet the needs of users. However, while digitizing public services processes for Moroccan administrations, the designers of information systems record the entered data in databases, without being too interested in the meaning of the entries made by citizens nor on the benefit of exchanged information between public administrations. This causes inconsistency and lack of interoperability between public administration systems. Moreover, the systems require information collection and shared knowledge based on a common vocabulary of public services in Morocco. For this purpose, this paper presents an ontology model based on the 5w1h Method for defining the public services domain. This PSOM ontology is based on the referential defined by the Moroccan Ministry of Administration Reform and Public service. Indeed, we have collected the different concepts related to this field and all possible relationships between them in order to ensure interoperability between all government entities. [ABSTRACT FROM AUTHOR]

Tuberculosis is an infectious disease that is among the top 10 causes of death in the world and Brazil ranks in the top 30 high TB burden countries. In this scenario, data integration and sharing are crucial to the construction of efficient and effective evidence-based decision-making tools and to enable data-driven research. Through a socio-technical approach, this work proposes a computational infrastructure composed of a functional and semantic interoperability layer and security mechanisms to integrate national level health information systems towards a patient-centered unified database to provide a broad view of a patient across several isolated databases. The CMIID, a medical information identifier for data harmonization, developed by the University of Porto, was used for the linkage of patients across these health information systems and to perform records anonymization for privacy of personal health information. Through the integration of such systems, it is possible to gather, summarize and visualize TB data in a single system, which can be useful for health professionals and managers. Therefore, this work sought to promote the integration of disparate systems and the availability of data to support decision-making and research, which are fundamental for improving the quality of TB services in Brazil. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) gains increasing attention as a method of implementing training systems in different domains, in particular, when real training is potentially dangerous for the trainees or the environment, or requires expensive equipment. The essential element of professional training is domain-specific knowledge, which can be represented using the semantic web approach. It enables reasoning as well as complex queries against the representation of training scenarios, which can be valuable for teaching purposes. However, the available methods and tools for creating VR training systems do not use semantic knowledge representation. Currently, the creation, modification, and management of training scenarios require skills in programming and computer graphics. Hence, they are unavailable to domain experts without expertise in IT. In this paper, we propose an ontology-based representation and a method of modeling VR training scenarios. In our approach, trainees' activities, potential mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We illustrate the approach by modeling VR training scenarios for electrical operators of high-voltage installations. [ABSTRACT FROM AUTHOR]

With the recent developments of the Internet of Things and its integration in the web environment, the Web of Things and the real-time data submissions to Reasoners are enabled. However, the data that are fed to the Reasoners are often inconsistent. This can be possibly caused by the malfunction of certain Internet of Things device or by human errors. The data consistency issue is becoming more complex in the Web of Things network. This paper, therefore, proposes a new data processing model to tackle the inconsistent data, so that the processed data can be further used in Reasoners. The data processing model introduces an oversimplification of the Shramko-Wansing sixteen-valued trilattice, which is an extension of Belnap's four-valued bilattice to assign the data classical truth-values. A preliminary implementation is demonstrated to validate the proposed model. The result shows that our model can avoid system collapse when contradictory outputs exist. [ABSTRACT FROM AUTHOR]

The acquisition and maintenance of non-technical skills by the pilots are fundamental factors for the prevention of aviation accidents. The aviation authorities are promoting that air crew training be carried out through simulator sessions using scenarios specifically designed to develop and assess the global performance of pilots in such skills. When designing custom flight training scenarios, choosing the correct events and conditions from the myriad of possible combinations with respect to their potential utility in training specific competencies is a costly task that depends entirely on highly specialized expert knowledge. In this paper, we present EBTOnto, an OWL DL ontology that allows to formalize this knowledge and other useful data from real cases, laying the foundations for a semantic knowledge base of scenarios for airline pilots training. Previous advances in this matter and possible applications of this system are reviewed. EBTOnto is built on top of a source validated by experts, the Evidence-Based Training Implementation Guide by the International Air Transport Association, and then checked using an automatic reasoner and a database of 37,568 aviation safety incidents, extracted from the widely regarded Aviation Safety Reporting System by the U.S. National Aeronautics and Space Administration. The results suggest that it is possible to classify real aviation scenarios in terms of non-technical competencies and filter useful incident reports for design and enrichment of these training scenarios. EBTOnto opens up new possibilities for interoperability between incident databases and training organizations, and smoothes the path to represent, share and generate custom simulation training scenarios for pilots based on real data. [ABSTRACT FROM AUTHOR]

Ontologies are formal systems of concepts used to describe numerous domains of interest. Ontologies are usually very expressive, but it comes at a price of computationally expensive reasoning over them. In our previous work we discussed the possible performance benefits that can be obtained by decomposing an ontology into contexts. While the benefits are appealing, we discovered that, in our case, the main obstacle against using contextual versions of ontologies was the necessity of performing the costly process of their decomposition with the participation of human experts. In this paper we discuss the possibility of using OntoClean method for streamlining and at least partial automation of suggesting a decomposition of an ontology into contexts. We present a hypothesis about how to build a structure of contexts, and verify this hypothesis against several ontologies used in state-of-the-art research. The ontologies have been obtained by us in the process that uses elements of Systematic Literature Review. The final assessment of the method has been performed by human experts, during interviews, and we present the details of their evaluation in the paper. [ABSTRACT FROM AUTHOR]

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules. [ABSTRACT FROM AUTHOR]

Su-Field analysis, as one of TRIZ analytical tool for solving inventive problems, can be used to improve the performance of the technical system effectively. Generally, choosing an appropriate inventive standard is critical to solving inventive problems efficiently and accurately. However, these standards are summarized and categorized based on the enormous amount of patents in different domains, and they are built in the high level of abstraction, independently of the specific application field, making their use require much more technical knowledge than other TRIZ tools. In order to facilitate the use of inventive standards, especially for capturing the uncertainty or imprecision depicted in the standards, a rule-based heuristic methodology is proposed in this paper. Firstly, Su-Field analysis ontology and fuzzy analysis ontology are built to represent the precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) inference and fuzzy inference are performed for generating heuristic concept solution. Finally, a prototype is developed and the resolution of the case of the "Auguste Piccard's Stratostat" in prototype is elaborated in detail. [ABSTRACT FROM AUTHOR]

This paper presents a visualising tool to explore the population of an Ontology, obtained through the processes of automatic migration and text information extraction. It was developed in the context of EPISA project, a R&D project that aims to represent the Portuguese National Archives records information in CIDOC-CRM, an ontology developed for museums. The tool allows the migration process developers to visualise the instances and their properties, and to debug the migration process and the migration representation model, or to explore the Archives by final users. It uses modeling and reasoners OWL-API with SPARQL-DL queries to obtain the exploration results. [ABSTRACT FROM AUTHOR]

The development of special mathematics capable of directly taking into account the dynamics of the problem domain, as it turns out, is a non-trivial task. Its very formulation in a refined form and the fixation of the most important features cause noticeable complications in the target formalism, significantly complicating the development of software. A constructive solution to this problem is given, obtained using the original functor-as-object construction. The concept of semantic viralization is introduced. It is expected that the obtained computational model has a high innovative potential for the development of information systems designed for intensive data exchange. [ABSTRACT FROM AUTHOR]

The prediction of cancer treatment side-effects requires the capturing of complex biophysical therapy parameters and the integration of different medical knowledge elements. In relation with radiotherapy, it is widely observed that the uncontrolled processes or undefined radiation therapy dose can decline the state of treatment. Precisely, the inability to manage the flow of available information, usually provided in heterogeneous formats, made it complicated to oversee and predict risks and effects of a prescribed treatment protocol. We think that, the optimization of knowledge representation and modelling in the context of evidence-based medicine can support the automated prediction of risks and side effects in oncotherapy. The following manuscript describes our methodology used for the design of a bladder cancer treatment side effects ontology embedded with evidence-based semantic rules and queries. Treatment knowledge is represented along with a particular consideration to the modelling of its referred risks and side effects. Our ontology model helps in improving the streamlining of medical practices and clinical decision-making. Within our semantic web approach, better strategies are applied for treatment selection with reference to possible side effects. Our ontology depicts real world scenario of developing treatment-related side effects. Furthermore, it is a clinical decision support system founding tool that highlights treatments efficiency. Our model shares treatment knowledge, facts and effects. Moreover, it includes medical evidence and incorporates a semantic rule base for systemic prediction results. [ABSTRACT FROM AUTHOR]

This paper investigates possibilities to visually support the collaborative design process of Enterprise Knowledge Graphs for Business Application Systems. Starting with a description of the concept of the Social Network of Business Objects (SoNBO), we show how the SoNBO Visualiser supports the visualisation of a company-specific ontology and the resulting knowledge graph. Our previous research showed that existing visualisation tools from the Semantic Web are unsuited for the human involvement that is required in the design of an ontology for a SoNBO. In order to address the lack of a tool, the SoNBO Visualiser was developed using a Design Science Research approach. This paper aims to provide a methodological as well as a technical contribution: The method provides support for the visualisation of the ontology (on two levels) and facilitates the involvement of domain experts (employees) without technical knowledge in the design process. The complementary technical solution connects heterogeneous source systems in a simple configuration process and allows to visualise the ontology and the resulting knowledge graph. [ABSTRACT FROM AUTHOR]

Process modeling has a broad range of applications, varying from business and system engineering, via artifact design, up to natural process modeling utilized in natural sciences. Over the last decades, various sophisticated languages and frameworks have been developed to support process modeling. The current paper discusses an approach to process modeling, which is, in contrast to many existing solutions, intended for the integrated process and object modeling. Furthermore, it is designed to be a lightweight approach with only a few constructs, which, however, permit the representation of processes from various perspectives. The developed solution provides an abstract language-independent model (ontology), partial formalization in first-order logic as well as a Web Ontology Language (OWL) implementation. [ABSTRACT FROM AUTHOR]

Ontologies provide engineers and developers with an unambiguous, verifiable, and expandable knowledge base related to a certain domain. Every project that requires control over consistent knowledge, which is especially relatable when using artificial intelligence with datasets increasing in size every second, would reap benefits from adding ontologies to the equation. It is a powerful asset enabling the development of a project with integrity between platforms or teams. Unfortunately, the cost of entry for a developer into the ontology engineering area is high, as it has been proven over the last decades that developing an ontology is a complex, collaborative task, which requires the support of an adequate methodology as well as software tools. The current paper's objective is twofold. First, it provides a survey on the methodology and software tools used for the creation of the ontology, its maintenance and collaboration. The paper investigates how the tools evolved over the years and what trends have emerged. Second, as the result of the analysis conducted, we show that current solutions have deficiencies and a technological debt; therefore, we present our plan to build a modern tool that uses state-of-the-art technology. [ABSTRACT FROM AUTHOR]

Ontologies are a key element of the Semantic Web. They aim to capture basic knowledge by providing appropriate terms and formal relationships between them, so that they can be used in a machine-processable manner. Accordingly they enable automatic aggregation and practical use as well as unexpected reuse of distributed data sources. Ontologies may come from many different sources, pursuing different goals and quality criteria. However, performed manually ontology construction is a very complex and tedious task, thus many methods proposed offer automatic or semi-automatic way for ontology construction. Many of the methods have their own, specific features. Therefore, this paper proposes an extensive knowledge-based approach covering the domain of ontology learning methods from text. This work aims to collect the knowledge of available approaches for ontology learning and the prominent differences between them, drawing on best practices in ontology engineering. The proposed approach refers to methods and aims to enrich knowledge in the field of ontology learning (OL). In this paper, the author's ontology contains a set of various types of methods with main techniques used, and the necessary features in the miscellaneous approaches. The proposed an extensive knowledge-based approach uses a reasoning mechanism based on competency questions for individual approaches to determine their ontology learning method profiles. The validation stage has also been carried out. At the same time, it is an extension of the previous study in the form of a repository of knowledge about OL tools. In addition, the combination of both ontologies: tools and methods aim to provide a more efficient OL solution from text. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet of Things (IoT) and the huge growth of valuable data produced by decentralising information processing along global apparel supply chain have led to a persuasive appeal for a semantic approach to integrating distributed data facilities in the field of self-determining collaborating logistics services. This paper describes a framework, Apparel Business Decentralised Data Integration (ABDDI), which exploits knowledge representation techniques and languages (e.g. Description Logics – DLs) to annotate relevant business activities, movements of products within the manufacturing network to provide value-added services. More specifically the paper discusses the DLs formalisms, which are used for knowledge representation in a decidable fragment of First Order Logic; and ALN (D) (Attributive Language with unqualified Number restrictions and concrete Domains) related issues. The paper presents an algorithm to demonstrate the DLs based entity concept similarity assessment to facilitate semantic web service. Finally, a business scenario is used to present some of the knowledge representation formalisms and concept similarity assessment in ABDDI. [ABSTRACT FROM AUTHOR]

India is rich with its culture and heritage. It is known for its traditional medicinal system and it is mentioned even in the ancient Vedas and other scriptures also. In India, Traditional Medical system includes Ayurveda, yoga, siddha, Unani, and homeopathy. Biomedical Text Mining (BioTM) is aiming at the extraction of novel, non-trivial information from the large amounts of biomedical related documents. This unstructured bio medical document holds greater knowledge about medical diagnostics, treatment, and prevention. Ontology plays a vital role in deep understanding of information. It is the building block of the Semantic Web and considers it's important on the semantic clarity of concepts and entities. The main objective is to search the most relevant content from this huge set of text by understanding the meaning of conceptual terms. We proposed Ontology based Concept Extraction and Classification in which the domain ontology and semantic document description was used to improve classification accuracy. The results show that the classification accuracy of proposed algorithm outperforms than the other existing supervised machine learning algorithm. To further prove the efficiency of the model, experiments were conducted by giving different queries and the results are compared with other existing methods. The results show that the content retrieved by the proposed model was most relevant when compared with existing system. [ABSTRACT FROM AUTHOR]

In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language. [ABSTRACT FROM AUTHOR]

Most car manufacturers predict that in the first half of the next decade there will be fully autonomous vehicles on our roads. Such vehicles would have to communicate in order to mitigate problems caused by single-viewpoint approach. So there are a lot of researches and developments when it comes to communication layer of V2X (Vehicle-to-Everything), but there is still a lot to be done when it comes to data layer of this communication. This is why we propose using Semantic Web Technologies (SWT) to fill in gaps within data layer of V2X communication. By using SWT (Semantic Web Technologies) and Linked data, we plan to interconnect various data sources, in order to provide homogeneous way for connected autonomous vehicles (CAV) to access relevant information. Such information is currently contained in three distinctive type of sources. These are: Geo-stationary Static data sources (Maps, City models), Geostationary Dynamic data sources (IoT devices) and Non-geostationary Dynamic sources (Vehicles). Using SWT, our goal is to develop ontology(s), in such a way that in-vehicle algorithms can extract and process information about environment they are in, while taking into account available network bandwidth. [ABSTRACT FROM AUTHOR]

Semantic Web vision is to have machines interpret and understand the content of Web documents. There is a need to convert the existing Web of documents into an understandable format, which could be done by automatic semantic annotation. Annotation could be performed using a set of tools provided with general and domain-specific ontologies. The aim of this paper is to present a generic semantic annotation framework of Arabic text using deep learning models. The framework produces annotations using different output formats for a given set of Arabic documents and ontologies. With a prototype of the framework, the initial evaluation shows a promising performance using different public Arabic word embedding models with different vectorization and matching techniques. [ABSTRACT FROM AUTHOR]

Massive Open Online Course (MOOC) is a form of online education that provides great learning capabilities. Semantic Web technologies is an appropriate mechanism for personalization in MOOC platforms. The aim of this paper is to find out how Semantic Web is used to facilitate personalization in modern MOOC platforms. The paper describes state-of-the-art MOOC platforms from the position of personalization and Semantic Web features. We defined five personalization and five Semantic Web criteria as well as 20 MOOC platforms to review. The personalization criteria includes a personalized learning path, personalized navigation, recommendation system, personalized assessment, and personalized feedback. The Semantic Web criteria includes ontology, Resource Description Framework (RDF), Web Ontology Language (OWL), SPARQL Protocol and RDF Query Language (SPARQL), and Linked Data. The results show that most of the platforms support personalized feedback. Half of the platforms has personalized learning path tools. One third of the platforms allow personalized assessment. Three platforms recommend learning materials, and one platform allows personalized navigation. The selected platforms have poor Semantic Web technologies and standards support: three platforms use ontologies and none of the platforms supports other criteria: RDF, OWL, SPARQL, and Linked Data. Personalization tools are supported better than Semantic Web tools. Most of the platforms have no support for Semantic Web criteria. This means that currently Semantic Web is not used for personalization in the reviewed MOOC platforms. [ABSTRACT FROM AUTHOR]

In this paper, the semantic complex event processing based on the metagraph approach and its usage in metagraph multiagent system are discussed. The metagraph model is a kind of complex graph model with emergence. The key element of the metagraph model is the metavertex, which makes the definition of metagraph holonic – a metavertex may include a number of lower-level elements and in turn, may be included in a number of higher-level elements. For metagraph transformation, the metagraph agents are proposed. The distinguishing feature of the metagraph agent is its homoiconicity, which means that it can be a data structure for itself. This is due to the fact that the metagraph agent may be represented as a set of metagraph fragments. Thus, the metagraph agent can change the structure of itself or the structure of the other metagraph agents. Changing the structure of a metagraph agent is a form of agent learning. The metagraph multiagent system based on semantic complex event processing using metagraph description of semantic complex event. [ABSTRACT FROM AUTHOR]

Web-data has seen an exponential rise in the past few years. With the increase in the data on the web, the process of associating entities with required knowledge becomes extremely difficult. Linking entities not only becomes a tedious task but also requires the right association of knowledge with the right techniques. With the development of the Semantic Web in recent times, semantic strategies are required to represent, reason and link entities. In this paper, an entity linking approach that rightly associates personalities has been proposed. The proposed algorithm encompasses role-based chunking along with a fragmented parse tree generation. The proposed strategy performs Entity Linking by JSON fragmented parse tree generation and recommends the entities based on the semantic score generated by computing the concept similarity. The knowledge is supplied by a role-based Ontology modeled for various famous personalities. An accuracy of 89.77% is achieved for role-based entity linking which is much better and reliable than the existing strategies, especially when a large number of trials were conducted for the Indian Context. [ABSTRACT FROM AUTHOR]

Although tuberculosis is a curable disease and, in most cases, with low cost drugs, its mortality still is a global concern. This facts turns our attention to management issues and the difficulties related to retrieving data of interest that are powdered on many applications. This work presents a semantic web approach to achieve functional and semantic interoperability between two applications in State of São Paulo that contain tuberculosis data. By combining a theoretical-practical development, the geolocalization tool created is a proof of concept that could help managers to take strategic decisions and develop better health policies by showing the distribution of tuberculosis cases across the state. This work stands out the importance of working in solutions that could improve the quality of data in health field and daily activities of health professionals. [ABSTRACT FROM AUTHOR]

Epidemiological surveillance of Tuberculosis (TB) requires a strong integration of different health services, programs and levels of care. The deepening and broadening of data management techniques must be constantly carried out to increase the integrality of healthcare. Otherwise, knowledge extraction and clinical and administrative decision-making processes are significantly hampered, directly affecting the management and quality of health services. Thus, this work aims to establish a computerized decision support system capable of collecting, integrating and sharing TB health data in Brazilian Unified Public Health System. Also, it will allow the monitoring of infected patients and the visualization of consolidated information of regular TB and its resistant variants for health professionals and managers. The data will be made available from heterogeneous, disconnected and unstructured sources by combining traditional web services, Semantic Web resources and security algorithms. A solid knowledge base applied to epidemiological surveillance, health information governance and clinical support will be enabled to integrate the multiple areas of TB patients care, as well as to support the creation of more accurate operational and diagnostics models. [ABSTRACT FROM AUTHOR]

Ontologies are one of the fundamental elements of the Semantic Web, and they have gained a lot of popularity and recognition because they are viewed as the answer to the need for interoperable semantics in modern information systems. The intermingling of techniques in areas such as natural language processing, information retrieval, machine learning, data mining, and knowledge representation provide a lot of possibilities for development of ontology learning approaches. A rise in focus on the ability to cope with the scale of Web data required for ontology learning forces the potential growth of cross-language research, emphasizing the automatic or semi-automatic generation of the tools dedicated to text mining and information extraction. This paper presents the integration of ontology learning tools from text in the knowledge repository to incorporate the applied techniques and outputs of an ontology learning algorithm into the one complex multifunctional solution. The proposed knowledge repository covers various applicability of existing techniques of learning ontologies from text, and offers competency question-based reasoning mechanism for individuals to specify their profiles of ontology learning tools. The validation stage is also provided in the form of applied reasoning. [ABSTRACT FROM AUTHOR]

Automatic Service Composition is a research direction aimed at facilitating the usage of atomic web services. Particularly, the goal is to build workflows of services that solve specific queries, which cannot be resolved by any single service from a known repository. Each of these services is described independently by their providers that can have no interaction with each other, therefore some common standards have been developed, such as WSDL, BPEL, OWL-S. Our proposal is to use such standards together with JSON-LD to model a next level of semantics, mainly based on binary relations between parameters of services. Services relate to a public ontology to describe their functionality. Binary relations can be specified between input and/or output parameters in service definition. The ontology includes some relations and inference rules that help to deduce new relations between parameters of services. To our knowledge, it is for the first time that parameters are matched not only based on their type, but on a more meaningful semantic context considering such type of relations. This enables the automation of a large part of the reasoning that a human person would do when manually building a composition. Moreover, the proposed model and the composition algorithm can work with multiple objects of the same type, a fundamental feature that was not possible before. We believe that the poor model expressiveness is what is keeping service composition from reaching large-scale application in practice. [ABSTRACT FROM AUTHOR]

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of anomalies on production lines is a crucial concern for manufacturers. Recently, data mining techniques have been applied to the manufacturing domain for predicting occurrence time of anomalies, such as the moment of machinery failure. However, existing predictive maintenance approaches have been limited to the prediction of the time of occurrence of machinery failures, while lacking the capability for identifying the criticality of the failures. This may lead to inappropriate maintenance plans and strategies. In this context, in this paper, we introduce a novel ontology-based approach to facilitate predictive maintenance in industry. The proposed approach is a combination use of fuzzy clustering and semantic technologies, where fuzzy clustering techniques are used to learn the criticality of failures based on machine historical data, and semantic technologies use the results of fuzzy clustering to predict the time of failures and the criticality of them. As results, a domain ontology for modeling predictive maintenance knowledge is developed, and a set of Semantic Web Rule Language (SWRL) predictive rules are proposed to reason about the time and criticality of machinery failures. A case study on a real-world industrial data set is followed to evaluate the usefulness and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the coming era of Industry 4.0, more assets and machines in plants are equipped with sensors which collect big amount of data for effective on-line equipment condition monitoring. Monitoring equipment conditions can not only reduce unplanned downtime by early detection of relevant situations like anomalies but also avoid unnecessary routine maintenance. For the detection of these situations it is necessary to integrate distributed, heterogeneous data sources and data streams. In this context, semantic web technologies are increasingly considered as key technologies to improve data integration. However, they are mainly used for data that is assumed not to change very often in time. In order to tackle this issue, stream reasoning combines reasoning and stream processing methods. Such a combination enables the processing of dynamic and heterogeneous data continuously produced from a large number of sources and implementing real-time services. This paper presents an approach that uses stream reasoning to identify in real time certain situations that lead to potential failures. Early detection enables to choose the most appropriate decision to avoid the interruption of manufacturing processes. In order to achieve this, data collected from sensors are enriched with contextual information. The use of stream reasoning allows the integration of data from different data sources, with different underlying meanings, different temporal resolutions as well as the processing of these data in real time. [ABSTRACT FROM AUTHOR]

In order to realize the vision of Semantic Web, which is a Web of things instead of Web of documents, there is a need to convert existing Web of documents into Semantic content that could be processed by machines. Semantic annotation tool could be used to perform this task through using common and public ontologies. Due to exponential growth and the huge size of Web sources, there is a need to have a fast and automatic Semantic annotation of Web documents. The aim of this paper is to investigate the use of word embeddings from deep learning algorithms to semantically annotate the Arabic Web documents. To enhance the performance of the Semantic annotation, we utilized the complex morphological structure of Arabic words. Moreover, evaluating the performance of the proposed framework requires selecting a set of domain ontologies with relevant and annotated related documents. The proposed framework produces Semantic annotations for these documents by using different standard output formats. The initial results show a promising performance that will support the research in the Semantic Web with respect to Arabic language. [ABSTRACT FROM AUTHOR]

Most car manufacturers predict that in the first half of the next decade there will be fully autonomous vehicles on our roads. Such vehicles would have to communicate in order to mitigate problems caused by single-viewpoint approach. So there are a lot of researches and developments when it comes to communication layer of V2X (Vehicle-to-Everything), but there is still a lot to be done when it comes to data layer of this communication. This is why we propose using Semantic Web Technologies (SWT) to fill in gaps within data layer of V2X communication. By using SWT (Semantic Web Technologies) and Linked data, we plan to interconnect various data sources, in order to provide homogeneous way for connected autonomous vehicles (CAV) to access relevant information. Such information is currently contained in three distinctive type of sources. These are: Geo-stationary Static data sources (Maps, City models), Geostationary Dynamic data sources (IoT devices) and Non-geostationary Dynamic sources (Vehicles). Using SWT, our goal is to develop ontology(s), in such a way that in-vehicle algorithms can extract and process information about environment they are in, while taking into account available network bandwidth. [ABSTRACT FROM AUTHOR]

Abstract The main analyses in Social Network Analysis field are focusing on measuring the frequency of social interactions that are modeled only with numbers; 0 or 1 on the graph adjacency matrix. Therefore, ontologies have drawn attention to empower and enhance the Social Network Analysis with semantics. By exploring theses semantics, besides we better view and analyze the interactions between students and teachers inside the Learning Management System, the network graph structure and analyses change. In this paper, we propose a methodology to build Moodle's OWL (Web Ontology Language) ontology for Social Network Analysis ends. The proposed ontology gives explicit meaning to the relationships inside Moodle instead of modeling them as nodes and edges with implicit semantics like the case in classical Social Network Analysis. The semantics of relationships influences the graph structure and statistics by making some nodes more central and influential and other nodes less central and influential. The built ontology is basically made by exploring the UML (Unified Modeling Language) class diagram that we created from Moodle Mount Orange School and is our main metadata source. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of IOT technology, the requirement of effective and accurate retrieval of domain knowledge is growing. Automatically extract various information of expert from the massive web pages and generate a dynamic and wholeness profile model are important for knowledge base. However, the obvious differences in structure and content semantics of web pages between any two websites shows traditional web crawler are hard to understand the semantic of the web page and extract the critical information of expert. Therefore, a six-dimension expert profile model was introduced and then a sequence tagging method with LSTM-CRF model was proposed to automatically extract rich semantic information basing on organization structure, meaning of words and attributes of experts. The results of the experiment on test data sets illustrated that the precision rate and recall rate about the job experience and research field of experts are 67.8%, 66.6% and 82.4%, 79.6%, respectively. In addition, the overall average F value about some obvious features of expert, such as name, title, email, achievement, etc., reaches 82.5%, which is better than the results by MEMM and LSTM algorithms. [ABSTRACT FROM AUTHOR]

Abstract Data exchange in health information systems must be carefully planned and needs to be protected from unauthorized access due to sensibility of stored content. Security aspects like authentication, authorization and encryption must be considered in this context. The main goal of this article is to present the implementation of security mechanisms to a semantic API that allows data extraction from a regional health information system designed to create notifications and to follow patients diagnosed with Tuberculosis. Data semantically tagged will be mapped individually to several access levels. It will be showed how external systems can connect, authenticate and retrieve only authorized data that are classified in the scope of its maximum access level. [ABSTRACT FROM AUTHOR]

Abstract Mental illness represent a large part of public health problems worldwide. Additionally, mental health managers need to make decisions based on complex data from dispersed sources with low levels of standardization and integration. This generally hinders knowledge extraction and its use as a reference to provide useful indicators and reports for efficient decision making. In this context, the interoperability between Information Systems is an essential characteristic to establish the capacity of communication, exchange and reuse of information for the generation of knowledge. In order to reduce the data complexity and to assist in the data interpretation, Semantic Web technology can be used. It classifies the stored information with metadata and give them meaning and thematic through the use of ontologies. Therefore, given the importance of mental health and the need for quality data, this article aims to specify and develop an ontology capable of consuming, integrating, analyzing and making available data of a regional mental healthcare network. [ABSTRACT FROM AUTHOR]