Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on well-documented and verifiable provenance to ensure their trustworthiness and usability. However, their ability to systematically assess and assure the quality of this provenance, most crucially whether it properly supports the graph’s information, relies mainly on manual processes that do not scale with size. ProVe aims at remedying this, consisting of a pipelined approach that automatically verifies whether a Knowledge Graph triple is supported by text extracted from its documented provenance. ProVe is intended to assist information curators and consists of four main steps involving rule-based methods and machine learning models: text extraction, triple verbalisation, sentence selection, and claim verification. ProVe is evaluated on a Wikidata dataset, achieving promising results overall and excellent performance on the binary classification task of detecting support from provenance, with 87.5 % accuracy and 82.9 % F1-macro on text-rich sources. The evaluation data and scripts used in this paper are available in GitHub and Figshare.
{"title":"ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources","authors":"Gabriel Amaral, Odinaldo Rodrigues, Elena Simperl","doi":"10.3233/sw-233467","DOIUrl":"https://doi.org/10.3233/sw-233467","url":null,"abstract":"Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on well-documented and verifiable provenance to ensure their trustworthiness and usability. However, their ability to systematically assess and assure the quality of this provenance, most crucially whether it properly supports the graph’s information, relies mainly on manual processes that do not scale with size. ProVe aims at remedying this, consisting of a pipelined approach that automatically verifies whether a Knowledge Graph triple is supported by text extracted from its documented provenance. ProVe is intended to assist information curators and consists of four main steps involving rule-based methods and machine learning models: text extraction, triple verbalisation, sentence selection, and claim verification. ProVe is evaluated on a Wikidata dataset, achieving promising results overall and excellent performance on the binary classification task of detecting support from provenance, with 87.5 % accuracy and 82.9 % F1-macro on text-rich sources. The evaluation data and scripts used in this paper are available in GitHub and Figshare.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135877961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geni Bushati, Sven Carsten Rasmusen, Anelia Kurteva, Anurag Vats, Petraq Nako, A. Fensel
The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies being unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie “disappears”, and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific Internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine-readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation approach and tool help to increase users’ awareness of cookies and data sharing.
{"title":"What is in your cookie box? Explaining ingredients of web cookies with knowledge graphs","authors":"Geni Bushati, Sven Carsten Rasmusen, Anelia Kurteva, Anurag Vats, Petraq Nako, A. Fensel","doi":"10.3233/sw-233435","DOIUrl":"https://doi.org/10.3233/sw-233435","url":null,"abstract":"The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies being unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie “disappears”, and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific Internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine-readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation approach and tool help to increase users’ awareness of cookies and data sharing.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76927534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Svátek, Ondřej Zamazal, Viet Bach Nguyen, J. Ivánek, Ján Kľuka, Miroslav Vacura
When reusing existing ontologies for publishing a dataset in RDF (or developing a new ontology), preference may be given to those providing extensive subcategorization for important classes (denoted as focus classes). The subcategories may consist not only of named classes but also of compound class expressions. We define the notion of focused categorization power of a given ontology, with respect to a focus class and a concept expression language, as the (estimated) weighted count of the categories that can be built from the ontology’s signature, conform to the language, and are subsumed by the focus class. For the sake of tractable initial experiments we then formulate a restricted concept expression language based on existential restrictions, and heuristically map it to syntactic patterns over ontology axioms (so-called FCE patterns). The characteristics of the chosen concept expression language and associated FCE patterns are investigated using three different empirical sources derived from ontology collections: first, the concept expression pattern frequency in class definitions; second, the occurrence of FCE patterns in the Tbox of ontologies; and last, for class expressions generated from the Tbox of ontologies (through the FCE patterns); their ‘meaningfulness’ was assessed by different groups of users, yielding a ‘quality ordering’ of the concept expression patterns. The complementary analyses are then compared and summarized. To allow for further experimentation, a web-based prototype was also implemented, which covers the whole process of ontology reuse from keyword-based ontology search through the FCP computation to the selection of ontologies and their enrichment with new concepts built from compound expressions.
{"title":"Focused categorization power of ontologies: General framework and study on simple existential concept expressions","authors":"V. Svátek, Ondřej Zamazal, Viet Bach Nguyen, J. Ivánek, Ján Kľuka, Miroslav Vacura","doi":"10.3233/sw-233401","DOIUrl":"https://doi.org/10.3233/sw-233401","url":null,"abstract":"When reusing existing ontologies for publishing a dataset in RDF (or developing a new ontology), preference may be given to those providing extensive subcategorization for important classes (denoted as focus classes). The subcategories may consist not only of named classes but also of compound class expressions. We define the notion of focused categorization power of a given ontology, with respect to a focus class and a concept expression language, as the (estimated) weighted count of the categories that can be built from the ontology’s signature, conform to the language, and are subsumed by the focus class. For the sake of tractable initial experiments we then formulate a restricted concept expression language based on existential restrictions, and heuristically map it to syntactic patterns over ontology axioms (so-called FCE patterns). The characteristics of the chosen concept expression language and associated FCE patterns are investigated using three different empirical sources derived from ontology collections: first, the concept expression pattern frequency in class definitions; second, the occurrence of FCE patterns in the Tbox of ontologies; and last, for class expressions generated from the Tbox of ontologies (through the FCE patterns); their ‘meaningfulness’ was assessed by different groups of users, yielding a ‘quality ordering’ of the concept expression patterns. The complementary analyses are then compared and summarized. To allow for further experimentation, a web-based prototype was also implemented, which covers the whole process of ontology reuse from keyword-based ontology search through the FCP computation to the selection of ontologies and their enrichment with new concepts built from compound expressions.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83630655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jason Liartis, Edmund Dervakos, Orfeas Menis-Mastromichalakis, A. Chortaras, G. Stamou
Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.
{"title":"Searching for explanations of black-box classifiers in the space of semantic queries","authors":"Jason Liartis, Edmund Dervakos, Orfeas Menis-Mastromichalakis, A. Chortaras, G. Stamou","doi":"10.3233/sw-233469","DOIUrl":"https://doi.org/10.3233/sw-233469","url":null,"abstract":"Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80802224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present an approach to publish Linked Data on the Web with quantifiable integrity using Web technologies, and in which rational agents are incentivised to contribute to the integrity of the link network. To this end, we introduce self-verifying resource representations, that include Linked Data Signatures whose signature value is used as a suffix in the resource’s URI. Links among such representations, typically managed as web documents, contribute therefore to preserving the integrity of the resulting document graphs. To quantify how well a document’s integrity can be relied on, we introduce the notion of trust scores and present an interpretation based on hubs and authorities. In addition, we present how specific agent behaviour may be induced by the choice of trust score regarding their optimisation, e.g., in general but also using a heuristic strategy called Additional Reach Strategy (ARS). We discuss our approach in a three-fold evaluation: First, we evaluate the effect of different graph metrics as trust scores on induced agent behaviour and resulting evolution of the document graph. We show that trust scores based on hubs and authorities induce agent behaviour that contributes to integrity preservation in the document graph. Next, we evaluate different heuristics for agents to optimise trust scores when general optimisation strategies are not applicable. We show that ARS outperforms other potential optimisation strategies. Last, we evaluate the whole approach by examining the resilience of integrity preservation in a document graph when resources are deleted. To this end, we propose a simulation system based on the Watts–Strogatz model for simulating a social network. We show that our approach produces a document graph that can recover from such attacks or failures in the document graph.
{"title":"Quantifiable integrity for Linked Data on the web","authors":"Christoph H.-J. Braun, Tobias Käfer","doi":"10.3233/sw-233409","DOIUrl":"https://doi.org/10.3233/sw-233409","url":null,"abstract":"We present an approach to publish Linked Data on the Web with quantifiable integrity using Web technologies, and in which rational agents are incentivised to contribute to the integrity of the link network. To this end, we introduce self-verifying resource representations, that include Linked Data Signatures whose signature value is used as a suffix in the resource’s URI. Links among such representations, typically managed as web documents, contribute therefore to preserving the integrity of the resulting document graphs. To quantify how well a document’s integrity can be relied on, we introduce the notion of trust scores and present an interpretation based on hubs and authorities. In addition, we present how specific agent behaviour may be induced by the choice of trust score regarding their optimisation, e.g., in general but also using a heuristic strategy called Additional Reach Strategy (ARS). We discuss our approach in a three-fold evaluation: First, we evaluate the effect of different graph metrics as trust scores on induced agent behaviour and resulting evolution of the document graph. We show that trust scores based on hubs and authorities induce agent behaviour that contributes to integrity preservation in the document graph. Next, we evaluate different heuristics for agents to optimise trust scores when general optimisation strategies are not applicable. We show that ARS outperforms other potential optimisation strategies. Last, we evaluate the whole approach by examining the resilience of integrity preservation in a document graph when resources are deleted. To this end, we propose a simulation system based on the Watts–Strogatz model for simulating a social network. We show that our approach produces a document graph that can recover from such attacks or failures in the document graph.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90173778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Wilson, J. Goonetillake, W. A. Indika, A. Ginige
With the continuous advancement of methods, tools, and techniques in ontology development, ontologies have emerged in various fields such as machine learning, robotics, biomedical informatics, agricultural informatics, crowdsourcing, database management, and the Internet of Things. Nevertheless, the nonexistence of a universally agreed methodology for specifying and evaluating the quality of an ontology hinders the success of ontology-based systems in such fields as the quality of each component is required for the overall quality of a system and in turn impacts the usability in use. Moreover, a number of anomalies in definitions of ontology quality concepts are visible, and in addition to that, the ontology quality assessment is limited only to a certain set of characteristics in practice even though some other significant characteristics have to be considered for the specified use-case. Thus, in this research, a comprehensive analysis was performed to uncover the existing contributions specifically on ontology quality models, characteristics, and the associated measures of these characteristics. Consequently, the characteristics identified through this review were classified with the associated aspects of the ontology evaluation space. Furthermore, the formalized definitions for each quality characteristic are provided through this study from the ontological perspective based on the accepted theories and standards. Additionally, a thorough analysis of the extent to which the existing works have covered the quality evaluation aspects is presented and the areas further to be investigated are outlined.
{"title":"A conceptual model for ontology quality assessment","authors":"R. Wilson, J. Goonetillake, W. A. Indika, A. Ginige","doi":"10.3233/sw-233393","DOIUrl":"https://doi.org/10.3233/sw-233393","url":null,"abstract":"With the continuous advancement of methods, tools, and techniques in ontology development, ontologies have emerged in various fields such as machine learning, robotics, biomedical informatics, agricultural informatics, crowdsourcing, database management, and the Internet of Things. Nevertheless, the nonexistence of a universally agreed methodology for specifying and evaluating the quality of an ontology hinders the success of ontology-based systems in such fields as the quality of each component is required for the overall quality of a system and in turn impacts the usability in use. Moreover, a number of anomalies in definitions of ontology quality concepts are visible, and in addition to that, the ontology quality assessment is limited only to a certain set of characteristics in practice even though some other significant characteristics have to be considered for the specified use-case. Thus, in this research, a comprehensive analysis was performed to uncover the existing contributions specifically on ontology quality models, characteristics, and the associated measures of these characteristics. Consequently, the characteristics identified through this review were classified with the associated aspects of the ontology evaluation space. Furthermore, the formalized definitions for each quality characteristic are provided through this study from the ontological perspective based on the accepted theories and standards. Additionally, a thorough analysis of the extent to which the existing works have covered the quality evaluation aspects is presented and the areas further to be investigated are outlined.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85850377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While the Web of Data in principle offers access to a wide range of interlinked data, the architecture of the Semantic Web today relies mostly on the data providers to maintain access to their data through SPARQL endpoints. Several studies, however, have shown that such endpoints often experience downtime, meaning that the data they maintain becomes inaccessible. While decentralized systems based on Peer-to-Peer (P2P) technology have previously shown to increase the availability of knowledge graphs, even when a large proportion of the nodes fail, processing queries in such a setup can be an expensive task since data necessary to answer a single query might be distributed over multiple nodes. In this paper, we therefore propose an approach to optimizing SPARQL queries over decentralized knowledge graphs, called Lothbrok. While there are potentially many aspects to consider when optimizing such queries, we focus on three aspects: cardinality estimation, locality awareness, and data fragmentation. We empirically show that Lothbrok is able to achieve significantly faster query processing performance compared to the state of the art when processing challenging queries as well as when the network is under high load.
{"title":"Optimizing SPARQL queries over decentralized knowledge graphs","authors":"Christian Aebeloe, Gabriela Montoya, K. Hose","doi":"10.3233/sw-233438","DOIUrl":"https://doi.org/10.3233/sw-233438","url":null,"abstract":"While the Web of Data in principle offers access to a wide range of interlinked data, the architecture of the Semantic Web today relies mostly on the data providers to maintain access to their data through SPARQL endpoints. Several studies, however, have shown that such endpoints often experience downtime, meaning that the data they maintain becomes inaccessible. While decentralized systems based on Peer-to-Peer (P2P) technology have previously shown to increase the availability of knowledge graphs, even when a large proportion of the nodes fail, processing queries in such a setup can be an expensive task since data necessary to answer a single query might be distributed over multiple nodes. In this paper, we therefore propose an approach to optimizing SPARQL queries over decentralized knowledge graphs, called Lothbrok. While there are potentially many aspects to consider when optimizing such queries, we focus on three aspects: cardinality estimation, locality awareness, and data fragmentation. We empirically show that Lothbrok is able to achieve significantly faster query processing performance compared to the state of the art when processing challenging queries as well as when the network is under high load.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84563463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conjunctive Query (CQ) answering is a primary reasoning task over knowledge bases. However, when considering expressive description logics, query answering can be computationally very expensive; reasoners for CQ answering, although heavily optimized, often sacrifice expressive power of the input ontology or completeness of the computed answers in order to achieve tractability and scalability for the problem. In this work, we present a hybrid query answering architecture that combines various services to provide a CQ answering service for OWL. Specifically, it combines scalable CQ answering services for tractable languages with a CQ answering service for a more expressive language approaching the full OWL 2. If the query can be fully answered by one of the tractable services, then that service is used, to ensure maximum performance. Otherwise, the tractable services are used to compute lower and upper bound approximations. The union of the lower bounds and the intersection of the upper bounds are then compared. If the bounds do not coincide, then the “gap” answers are checked using the “full” service. These techniques led to the development of two new systems: (i) RSAComb, an efficient implementation of a new tractable answering service for RSA (role safety acyclic) (ii) ACQuA, a reference implementation of the proposed hybrid architecture combining RSAComb, PAGOdA, and HermiT to provide a CQ answering service for OWL. Our extensive evaluation shows how the additional computational cost introduced by reasoning over a more expressive language like RSA can still provide a significant improvement compared to relying on a fully-fledged reasoner. Additionally, we show how ACQuA can reliably match the performance of PAGOdA, a state-of-the-art CQ answering system that uses a similar approach, and can significantly improve performance when PAGOdA extensively relies on the underlying fully-fledged reasoner.
{"title":"Conjunctive query answering over unrestricted OWL 2 ontologies","authors":"Federico Igne, Stefano Germano, Ian Horrocks","doi":"10.3233/sw-233382","DOIUrl":"https://doi.org/10.3233/sw-233382","url":null,"abstract":"Conjunctive Query (CQ) answering is a primary reasoning task over knowledge bases. However, when considering expressive description logics, query answering can be computationally very expensive; reasoners for CQ answering, although heavily optimized, often sacrifice expressive power of the input ontology or completeness of the computed answers in order to achieve tractability and scalability for the problem. In this work, we present a hybrid query answering architecture that combines various services to provide a CQ answering service for OWL. Specifically, it combines scalable CQ answering services for tractable languages with a CQ answering service for a more expressive language approaching the full OWL 2. If the query can be fully answered by one of the tractable services, then that service is used, to ensure maximum performance. Otherwise, the tractable services are used to compute lower and upper bound approximations. The union of the lower bounds and the intersection of the upper bounds are then compared. If the bounds do not coincide, then the “gap” answers are checked using the “full” service. These techniques led to the development of two new systems: (i) RSAComb, an efficient implementation of a new tractable answering service for RSA (role safety acyclic) (ii) ACQuA, a reference implementation of the proposed hybrid architecture combining RSAComb, PAGOdA, and HermiT to provide a CQ answering service for OWL. Our extensive evaluation shows how the additional computational cost introduced by reasoning over a more expressive language like RSA can still provide a significant improvement compared to relying on a fully-fledged reasoner. Additionally, we show how ACQuA can reliably match the performance of PAGOdA, a state-of-the-art CQ answering system that uses a similar approach, and can significantly improve performance when PAGOdA extensively relies on the underlying fully-fledged reasoner.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81958959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Faria, Emanuel Santos, B. Balasubramani, M. C. Silva, Francisco M. Couto, Catia Pesquita
Ontology matching establishes correspondences between entities of related ontologies, with applications ranging from enabling semantic interoperability to supporting ontology and knowledge graph development. Its demand within the Semantic Web community is on the rise, as the popularity of knowledge graph supporting information systems or artificial intelligence applications continues to increase. In this article, we showcase AgreementMakerLight (AML), an ontology matching system in continuous development since 2013, with demonstrated performance over nine editions of the Ontology Alignment Evaluation Initiative (OAEI), and a history of real-world applications across a variety of domains. We overview AML’s architecture and algorithms, its user interfaces and functionalities, its performance, and its impact. AML has participated in more OAEI tracks since 2013 than any other matching system, has a median rank by F-measure between 1 and 2 across all tracks in every year since 2014, and a rank by run time between 3 and 4. Thus, it offers a combination of range, quality and efficiency that few matching systems can rival. Moreover, AML’s impact can be gauged by the 263 (non-self) publications that cite one or more of its papers, among which we count 34 real-world applications.
{"title":"AgreementMakerLight","authors":"Daniel Faria, Emanuel Santos, B. Balasubramani, M. C. Silva, Francisco M. Couto, Catia Pesquita","doi":"10.3233/sw-233304","DOIUrl":"https://doi.org/10.3233/sw-233304","url":null,"abstract":"Ontology matching establishes correspondences between entities of related ontologies, with applications ranging from enabling semantic interoperability to supporting ontology and knowledge graph development. Its demand within the Semantic Web community is on the rise, as the popularity of knowledge graph supporting information systems or artificial intelligence applications continues to increase. In this article, we showcase AgreementMakerLight (AML), an ontology matching system in continuous development since 2013, with demonstrated performance over nine editions of the Ontology Alignment Evaluation Initiative (OAEI), and a history of real-world applications across a variety of domains. We overview AML’s architecture and algorithms, its user interfaces and functionalities, its performance, and its impact. AML has participated in more OAEI tracks since 2013 than any other matching system, has a median rank by F-measure between 1 and 2 across all tracks in every year since 2014, and a rank by run time between 3 and 4. Thus, it offers a combination of range, quality and efficiency that few matching systems can rival. Moreover, AML’s impact can be gauged by the 263 (non-self) publications that cite one or more of its papers, among which we count 34 real-world applications.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85729717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patrick Lambrix, Rickard Armiento, Huanyu Li, Olaf Hartig, Mina Abd Nikooie Pour, Ying Li
In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology.11 This paper is an extension of (In The Semantic Web – ISWC 2020 – 19th International Semantic Web Conference, Proceedings, Part II (2000) 212–227 Springer) with results from (In ESWC Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing 2021 1–11) and currently unpublished results regarding an application using the ontology.
在材料设计领域，许多材料计算数据存储在不同的异构数据库中，具有不同的数据和访问模型。因此，访问和集成来自不同来源的数据具有挑战性。基于本体的访问和集成缓解了这些问题，本文通过开发材料设计本体来解决计算材料数据库的数据访问和互操作性问题。该本体受到OPTIMADE工作的启发和指导，OPTIMADE旨在使材料数据库具有互操作性，并包括计算材料科学中的许多数据提供者。本文首先介绍了材料设计本体的发展和内容。然后，我们使用基于主题模型的方法为本体提出额外的候选概念。最后，我们通过一个基于本体的材料数据库数据访问和集成系统的概念验证实现展示了材料设计本体的使用本文是(In The Semantic Web - ISWC 2020 -第19届国际语义网会议，论文集，第二部分(2000)212-227 Springer)的扩展，其结果来自(In ESWC研讨会领域本体用于材料和制造行业共享的研究数据管理2021 1-11)和目前未发表的关于使用本体的应用的结果。
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