Academic literature on the topic 'Ontologies (artificial intelligence)'

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Journal articles on the topic "Ontologies (artificial intelligence)"

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Lazarre, Warda, Kaladzavi Guidedi, Samdalle Amaria, and Kolyang. "Modular Ontology Design: A State-of-Art of Diseases Ontology Modeling and Possible Issue." Revue d'Intelligence Artificielle 36, no. 3 (June 30, 2022): 497–501. http://dx.doi.org/10.18280/ria.360319.

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The evolution of ontological engineering leaded authors to use some techniques of software engineering to design ontologies. Are obtained from these techniques the monolithic or modularized Ontologies. When is difficult to reuse some concepts of monolithic ontologies, modularized Ontologies facilitate ontology management, understandability and reuse. This paper aims to survey on ontology modularization techniques and their contribution in biomedical ontologies design. Modularization reposed on appropriated techniques and some challenges related to ontology reused, scalable querying, collaborative authoring, and distributed reasoning. For most of disease ontologies, more especially ontologies which reused IDO, these challenges are not considered, and most of them are implemented with OWL language and the novel mode to construct ontology’s purpose is to facilitate reuse and interoperability of ontologies ensured by modularization.
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Deagustini, Cristhian Ariel D., Maria Vanina Martinez, Marcelo A. Falappa, and Guillermo R. Simari. "Datalog+- Ontology Consolidation." Journal of Artificial Intelligence Research 56 (August 30, 2016): 613–56. http://dx.doi.org/10.1613/jair.5131.

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Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.
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Zhang, Fu, Jingwei Cheng, and Zongmin Ma. "A survey on fuzzy ontologies for the Semantic Web." Knowledge Engineering Review 31, no. 3 (April 22, 2016): 278–321. http://dx.doi.org/10.1017/s0269888916000059.

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AbstractOntology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains,in this paper,we provide a comprehensive overview of fuzzy ontologies. In detail, wefirstintroduce fuzzy ontologies from the most common aspects such asrepresentation(including categories, formal definitions, representation languages, and tools of fuzzy ontologies),reasoning(including reasoning techniques and reasoners), andapplications(the most relevant applications about fuzzy ontologies). Then,the other important issueson fuzzy ontologies, such asconstruction,mapping,integration,query,storage,evaluation,extension, anddirections for future research, are also discussed in detail. Also, we make somecomparisons and analysesin our whole review.
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Mars, Nicolaas J. I. "Comparison of implemented ontologies." Knowledge Engineering Review 10, no. 1 (March 1995): 67–68. http://dx.doi.org/10.1017/s026988890000727x.

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A number of groups developing knowledge-based systems have found (or at least posited) that the design and representation of a limitative set of concepts and relations, a so-called ontology, can contribute to sharing and reusing knowledge bases. However, very few descriptions of implemented ontologies have appeared in the literature. No comparison of competing proposals is available, let alone an empirical determination of the benefits of using an ontology. There is no accepted method for designing and building such ontologies, nor is it clear how ontologies can best be evaluated.
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MAYFIELD, JAMES. "Ontologies and text retrieval." Knowledge Engineering Review 17, no. 1 (March 2002): 71–75. http://dx.doi.org/10.1017/s026988890200036x.

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Analogues to much of today's work in ontologies have existed for centuries in text retrieval. The use of controlled vocabularies, or thesauri, has been fundamental to document indexing in library science. Thesauri serve several purposes, including:[bull ] Knowledge organisation A thesaurus provides a hierarchy of concepts that organises domain-specific knowledge.[bull ] Terminology normalisation By selecting a unique word or phrase to represent each domain concept, then linking synonymous terms to it, a thesaurus enforces terminological consistency.[bull ] Query expansion A thesaurus facilitates the addition of terms to a query by providing explicit hierarchical and lateral relationships among terms.These properties serve to mediate the information flow from indexer to user. Thesauri thus serve many of the same functions for people that ontologies are designed to serve for software agents. As automated retrieval has developed over the decades since the inception of computer processing of text, many techniques have been introduced to apply this typically manual work to the automated arena (see Soergel (1985) for an introduction to library information systems, also Anderson and Pélrez-Carballo (2001a, 2001b) for a summary of the intersection of human and machine indexing).
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Andreasen, Troels, and Henrik Bulskov. "Conceptual querying through ontologies." Fuzzy Sets and Systems 160, no. 15 (August 2009): 2159–72. http://dx.doi.org/10.1016/j.fss.2009.02.019.

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Toro, Carlos, Cesar Sanín, Edward Szczerbicki, and Jorge Posada. "REFLEXIVE ONTOLOGIES: ENHANCING ONTOLOGIES WITH SELF-CONTAINED QUERIES." Cybernetics and Systems 39, no. 2 (February 19, 2008): 171–89. http://dx.doi.org/10.1080/01969720701853467.

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Alejandro Gómez, Sergio, Carlos Iván Chesñevar, and Guillermo Ricardo Simari. "REASONING WITH INCONSISTENT ONTOLOGIES THROUGH ARGUMENTATION." Applied Artificial Intelligence 24, no. 1-2 (February 2, 2010): 102–48. http://dx.doi.org/10.1080/08839510903448692.

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Batet, Montserrat, David Sánchez, Aida Valls, and Karina Gibert. "Semantic similarity estimation from multiple ontologies." Applied Intelligence 38, no. 1 (May 26, 2012): 29–44. http://dx.doi.org/10.1007/s10489-012-0355-y.

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RANGANATHAN, ANAND, ROBERT E. McGRATH, ROY H. CAMPBELL, and M. DENNIS MICKUNAS. "Use of ontologies in a pervasive computing environment." Knowledge Engineering Review 18, no. 3 (September 2003): 209–20. http://dx.doi.org/10.1017/s0269888904000037.

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Ontologies are entering widespread use in many areas such as knowledge and content management, electronic commerce and the Semantic Web. In this paper we show how the use of ontologies has helped us overcome some important problems in the development of pervasive computing environments. We have integrated ontologies and Semantic Web technology into our pervasive computing infrastructure. Our investigations have shown that Semantic Web technology can be integrated into our CORBA-based infrastructure to augment several important services. This work suggests a number of requirements for future research in the development of ontologies, reasoners, languages and interfaces.
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Dissertations / Theses on the topic "Ontologies (artificial intelligence)"

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Tamma, Valentina A. M. "An ontology model supporting multiple ontologies for knowledge sharing." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250548.

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Gandon, Fabien. "Distributed artificial intelligence and knowledge management : ontologies and multi-agent systems for a corporate semantic web." Nice, 2002. http://www.theses.fr/2002NICE5773.

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This work concerns multi-agents systems for the management of a corporate semantic web based on an ontology. It was carried out in the context of the European project CoMMA focusing on two application scenarios: support technology monitoring activities and assist the integration of a new employee to the organisation. Three aspects were essentially developed in this work: the design of a multi-agents architecture supporting both scenarios, and the organisational top-down approach followed to identify the societies, the roles and the interactions of agents. The construction of the ontology O'CoMMA and the structuring of a corporate memory exploiting semantic Web technologies. The design and implementation of the sub-societies of agents dedicated to the management of the annotations and the ontology and of the protocols underlying these groups of agents, in particular techniques for distributing annotations and queries between the agents
Ce travail concerne les systèmes multi-agents pour la gestion d'un web sémantique d'entreprise basé sur une ontologie. Il a été effectué dans le cadre du projet Européen CoMMA se focalisant sur deux scénarios d'application: l'assistance aux activités de veille technologique et l'aide à l'insertion d'un nouvel employé dans une organisation. Trois aspects ont essentiellement été développés dans ce travail: la conception d'une architecture multi-agents assistant les deux scénarios, et l'approche organisationnelle descendante adoptée pour identifier les sociétés, les rôles et les interactions des agents. La construction de l'ontologie O'CoMMA et la structuration de la mémoire organisationnelle en exploitant les technologies du Web sémantique. La conception et l'implantation (a) des sous-sociétés d'agents chargées de la maintenance des annotations et de l'ontologie et (b) des protocoles supportant ces deux groupes d'agents, en particulier des techniques pour la distribution des annotations et des requêtes entre les agents
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Bate, Andrew. "Consequence-based reasoning for SRIQ ontologies." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6b35e7d0-199c-4db9-ac8a-7f78256e5fb8.

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Description logics (DLs) are knowledge representation formalisms with numerous applications and well-understood model-theoretic semantics and computational properties. SRIQ is a DL that provides the logical underpinning for the semantic web language OWL 2, which is the W3C standard for knowledge representation on the web. A central component of most DL applications is an efficient and scalable reasoner, which provides services such as consistency testing and classification. Despite major advances in DL reasoning algorithms over the last decade, however, ontologies are still encountered in practice that cannot be handled by existing DL reasoners. Consequence-based calculi are a family of reasoning techniques for DLs. Such calculi have proved very effective in practice and enjoy a number of desirable theoretical properties. Up to now, however, they were proposed for either Horn DLs (which do not support disjunctive reasoning), or for DLs without cardinality constraints. In this thesis we present a novel consequence-based algorithm for TBox reasoning in SRIQ - a DL that supports both disjunctions and cardinality constraints. Combining the two features is non-trivial since the intermediate consequences that need to be derived during reasoning cannot be captured using DLs themselves. Furthermore, cardinality constraints require reasoning over equality, which we handle using the framework of ordered paramodulation - a state-of-the-art method for equational theorem proving. We thus obtain a calculus that can handle an expressive DL, while still enjoying all the favourable properties of existing consequence-based algorithms, namely optimal worst-case complexity, one-pass classification, and pay-as-you-go behaviour. To evaluate the practicability of our calculus, we implemented it in Sequoia - a new DL reasoning system. Empirical results show substantial robustness improvements over well-established algorithms and implementations, and performance competitive with closely related work.
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Marinica, Claudia. "Association Rule Interactive Post-processing using Rule Schemas and Ontologies - ARIPSO." Phd thesis, Université de Nantes, 2010. http://tel.archives-ouvertes.fr/tel-00912580.

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This thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases (KDD), more precisely the Association Rule Mining technique, and Knowledge Engineering (KE) with a main interest in knowledge representation languages developed around the Semantic Web. In Data Mining, the usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered rules. Experiments show that rules become almost impossible to use when their number exceeds 100. At the same time, nuggets are often represented by those rare (low support) unexpected association rules which are surprising to the user. Unfortunately, the lower the support is, the larger the volume of rules becomes. Thus, it is crucial to help the decision maker with an efficient technique to reduce the number of rules. To overcome this drawback, several methods have been proposed in the literature such as itemset concise representations, redundancy reduction, filtering, ranking and post-processing. Even though rule interestingness strongly depends on user knowledge and goals, most of the existing methods are generally based on data structure. For instance, if the user looks for unexpected rules, all the already known rules should be pruned. Or, if the user wants to focus on specific family of rules, only this subset of rules should be selected. In this context, we address two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires defining an adapted formalism to express user knowledge with accuracy and flexibility such as ontologies in the Semantic Web. Second, the interactivity with the user allows a more iterative mining process where the user can successively test different hypotheses or preferences and focus on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose a new rule-like formalism, called Rule Schema, which allows the user to define his/her expectations regarding the rules through ontology concepts. Second, ontologies allow the user to express his/her domain knowledge by means of a high semantic model. Last, the user can choose among a set of Operators for interactive processing the one to be applied over each Rule Schema (i.e. pruning, conforming, unexpectedness, . . . ). (ii) A new post-processing approach, called ARIPSO (Association Rule Interactive Post-processing using rule Schemas and Ontologies), which helps the user to reduce the volume of the discovered rules and to improve their quality. It consists in an interactive process integrating user knowledge and expectations by means of the proposed model. At each step of ARIPSO, the interactive loop allows the user to change the provided information and to reiterate the post-processing phase which produces new results. (iii) The implementation in post-processing of the proposed approach. The developed tool is complete and operational, and it implements all the functionalities described in the approach. Also, it makes the connection between different elements like the set of rules and rule schemas stored in PMML/XML files, and the ontologies stored in OWL files and inferred by the Pellet reasoner. (iv) An adapted implementation without post-processing, called ARLIUS (Association Rule Local mining Interactive Using rule Schemas), consisting in an interactive local mining process guided by the user. It allows the user to focus on interesting rules without the necessity to extract all of them, and without minimum support limit. In this way, the user may explore the rule space incrementally, a small amount at each step, starting from his/her own expectations and discovering their related rules. (v) The experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a real-life and large questionnaire database concerning customer satisfaction. For ARIPSO, the experimentation was carried out in complete cooperation with the domain expert. For different scenarios, from an input set of nearly 400 thousand association rules, ARIPSO filtered between 3 and 200 rules validated by the expert. Clearly, ARIPSO allows the user to significantly and efficiently reduce the input rule set. For ARLIUS, we experimented different scenarios over the same questionnaire database and we obtained reduced sets of rules (less than 100) with very low support.
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Marinica, Claudia. "Association Rule Interactive Post-processing using Rule Schemas and Ontologies : aripso." Phd thesis, Nantes, 2010. https://archive.bu.univ-nantes.fr/pollux/show/show?id=90a57cc4-245f-420d-ac2b-f9ad7929e0f7.

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Cette thèse s'inscrit à la confluence de deux domaines actifs de recherche: l'Extraction de Connaissances à partir des Données - la fouille de Règles
This thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases - Association Rule Mining technique, and Knowledge Engineering - representation languages of Semantic Web. The usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered rules. To overcome this drawback, several methods have been proposed in the literature such as itemset concise representations, redundancy reduction, filtering, ranking and post-processing, and most of them are based on data structure. However, rule interestingness strongly depends on user knowledge and goals. In this context, it is crucial to help the user with an efficient technique to reduce the number of rules while keeping interesting ones. This work addresses two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires an accurate and flexible formalism to express user knowledge such as ontologies in the Semantic Web. The second one proposes a more iterative mining process allowing the user to explore the rule space incrementally focusing on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose to represent user domain knowledge by means of ontologies. Second, we develop a new formalism, called "Rule Schema", which allows the user to define his/her expectations throughout ontology concepts. Last, we suggest the user a set of "mining Operators" to be applied over Rule Schemas. (ii) A new post-processing approach, ARJPSO. Lt allows the user to reduce the volume of the discovered rules by keeping only the interesting rules. ARIPSO is an interactive process integrating user knowledge by means of the proposed model. The interactive loop allows at each step the user to change the provided information and to reiterate the post-processing phase. (iii) The implementation in post-processing of ARJPSO. The developed tool is complete and operational, and it implements all the functionalities described in the approach. An alternative implementation, without post-processing, was proposed (ARLIUS). It consists in an interactive local mining process. (iv) An experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a large real-life database; for ARJPSO, the experimentation was carried out in complete cooperation with the domain expert. From an input set of nearly 400 thousand rules, for different scenarios, ARIPSO filtered between 3 and 200 rules validated by the expert
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Gherasim, Toader. "Détection de problèmes de qualité dans les ontologies construites automatiquement à partir de textes." Phd thesis, Université de Nantes, 2013. http://tel.archives-ouvertes.fr/tel-00982126.

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La démocratisation de l'utilisation des ontologies dans des domaines très variés a stimulé le développement d'approches proposant différents degrés d'automatisation du processus de construction d'une ontologie. Cependant, malgré le réel intérêt de ces approches, parfois les résultats obtenus peuvent être d'une faible qualité. L'objectif des travaux présentés dans cette thèse est de contribuer à l'amélioration de la qualité des ontologies construites automatiquement à partir de textes. Nos principales contributions sont : (1) une démarche pour la comparaison des approches, (2) une typologie des problèmes qui affectent la qualité les ontologies, et (3) une première réflexion sur l'automatisation de la détection des problèmes. Notre démarche de comparaison des approches comporte trois étapes complémentaires : (1) sur la base de leur degré de complétude et d'automatisation ; (2) puis sur la base de leurs caractéristiques techniques et fonctionnelles, et (3) expérimentalement par comparaison de leurs résultats avec une ontologie construite manuellement. La typologie proposée organise les problèmes de qualité selon deux dimensions : les erreurs versus les situations indésirables et les aspects logiques versus les aspects sociaux. Notre typologie contient 24 classes de problèmes qui recouvrent, en les complétant, les problèmes décrits dans la littérature. Pour la détection automatique nous avons recensé quelques unes des méthodes existantes pour chaque problème de notre typologie et nous avons mis en évidence les problèmes qui semblent encore ouverts. Et, nous avons proposé une heuristique pour un problème qui apparaît fréquemment dans nos expérimentations (étiquettes polysémiques).
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Fortineau, Virginie. "Contribution à une modélisation ontologique des informations tout au long du cycle de vie du produit." Phd thesis, Ecole nationale supérieure d'arts et métiers - ENSAM, 2013. http://pastel.archives-ouvertes.fr/pastel-01064598.

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Les travaux de recherche de cette thèse portent sur la modélisation sémantique des informations industrielles, dans une approche og cycle de vie fg , de gestion des informations. Dans ce type d'approche, lever le verrou lié à l'interopérabilité des modèles d'information est une condition sine qua non à un échange d'information sans perte de flux sémantique. Jusqu'alors, des méthodes d'unification étaient envisagées, reposant sur l'utilisation mutuelle de modèles standards, tels que la norme STEP par exemple. Cependant, l'unification fait face à des limites en termes d'expressivité des modèles, de rigidité, et de perte de sémantique. Afin de lever ces limites, les paradigmes de modélisation évoluent et se tournent vers les ontologies d'inférence, outils du web sémantique.Dans cette thèse, nous proposons un cadre de modélisation sémantique général et une méthodologie de mise en place de ce cadre, qui reposent sur l'utilisation d'ontologies d'inférence. L'application du cadre de modélisation à un cas d'étude industriel, issu de l'ingénierie nucléaire (plus particulièrement l'expression et l'exécution des règles métier), permet alors d'évaluer les apports et limites des ontologies en tant que paradigme de modélisation. Les limites les plus importantes que nous identifions sur l'Open World Assumption, le manque de langage de règles performant et le manque d'outils d'implémentation robustes pour des applications à large échelle. Le développement d'un démonstrateur pour le cas d'étude industriel permet finalement de tendre vers une solution mixte, où les ontologies sont utilisées localement, afin d'en exploiter les divers avantages de manière optimale.
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Aimé, Xavier. "Gradients de prototypicalité, mesures de similarité et de proximité sémantique : une contribution à l'Ingénierie des Ontologies." Phd thesis, Université de Nantes, 2011. http://tel.archives-ouvertes.fr/tel-00660916.

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En psychologie cognitive, la notion de prototype apparaît de manière centrale dans les représentations conceptuelles. Dans le cadre de nos travaux, nous proposons d'introduire cette notion au sein des activités relevant de l'Ingénierie des Ontologies et de ses modèles de représentation. L'approche sémiotique que nous avons développée est fondée sur les trois dimensions d'une conceptualisation que sont l'intension (les propriétés), l'expression (les termes), et l'extension (les instances). Elle intègre, en sus de l'ontologie, des connaissances supplémentaires propres à l'utilisateur (pondération des propriétés, corpus, instances). Pratiquement, il s'agit de pondérer les liens "is-a", les termes et les instances d'une hiérarchie de concepts, au moyen de gradients de prototypicalité respectivement conceptuelle, lexicale et extensionnelle. Notre approche a été mise en oeuvre dans un système industriel de gestion documentaire et de recherche d'information pour la société Tennaxia - société de veille juridique dans le domaine de l'Environnement. Elle a conduit au développement d'une ontologie du domaine Hygiène-Sécurité-Environnement, et de deux applications logicielles : l'application TooPrag dédiée au calcul des différents gradients de prototypicalité, et le moteur de Recherche d'Information sémantique Theseus qui exploite les gradients de prototypicalité. Nous avons enfin étendu notre approche à la définition de deux nouvelles mesures sémantiques, en nous inspirant des lois de similarité et de proximité de la théorie de la perception : Semiosem, une mesure de similarité, et Proxem, une mesure de proximité.
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Chniti, Amina. "Gestion des dépendances et des interactions entre Ontologies et Règles Métier." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2013. http://tel.archives-ouvertes.fr/tel-00820671.

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Vu la rapidité de l'évolution des connaissances des domaines, la maintenance des systèmes d'information est devenue de plus en plus difficile à gérer. Afin d'assurer une flexibilité de ces systèmes, nous proposons une approche qui permet de représenter les connaissances des domaines dans des modèles de représentation des connaissances plutôt que de les coder, dans un langage de programmation informatique, dans l'application du domaine. Ceci assurerait une meilleure flexibilité des systèmes d'information, faciliterait leur maintenance et permettrait aux experts métier de gérer eux même l'évolution des connaissances de leur domaine. Pour cela, nous proposons une approche qui permet d'intégrer des ontolo- gies et des règles métier. Les ontologies permettent de modéliser les connais- sances d'un domaine. Les règles permettent aux experts métier de définir et d'automatiser, dans un langage naturel contrôlé, des décisions du métier en se fondant sur les connaissances représentées dans l'ontologie. Ainsi, les règles dépendent des entités modélisées dans l'ontologie. Vu cette dépendance, il est nécessaire d'étudier l'impact de l'évolution des ontologies sur les règles. Pour cela, nous proposons l'approche MDR (Modéliser - Détecter - Réparer) qui permet de modéliser des changements d'ontologies, de détecter les problèmes de cohérence qu'ils peuvent causer sur les règles métier et de proposer des solutions pour réparer ces problèmes. L'approche proposée est une approche orientée experts métier et est fondée sur les systèmes de gestion des règles métier.
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Gong, Jian, and 龔劍. "Managing uncertainty in schema matchings." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46076116.

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Books on the topic "Ontologies (artificial intelligence)"

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Obrst, Leo Joseph, Terry Janssen, and W. Ceusters. Ontologies and semantic technologies for intelligence. Amsterdam, Netherlands: IOS Press, 2010.

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service), SpringerLink (Online, ed. On the Mathematics of Modelling, Metamodelling, Ontologies and Modelling Languages. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Ontology representation: Design patterns and ontologies that make sense. Amsterdam: IOS Press, 2009.

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Fensel, Dieter. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Ontology-driven software development. Heidelberg: Springer, 2013.

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Pan, Jeff Z. Ontology-Driven Software Development. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Pompeu, Casanovas, Biasiotti Maria Angela, Fernández-Barrera Meritxell, and SpringerLink (Online service), eds. Approaches to Legal Ontologies: Theories, Domains, Methodologies. Dordrecht: Springer Science+Business Media B.V., 2011.

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Gargouri, Faiez. Ontology theory, management and design: Advanced tools and models. Hershey, PA: Information Science Reference, 2010.

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Nogueras, Iso Javier, Zarazaga-Soria F. Javier, and SpringerLink (Online service), eds. Terminological Ontologies: Design, Management and Practical Applications. Boston, MA: Springer Science+Business Media, LLC, 2010.

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Casellas, Núria. Legal ontology engineering: Methodologies, modelling trends, and the ontology of professional judicial knowledge. Dordrecht: Springer, 2011.

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Book chapters on the topic "Ontologies (artificial intelligence)"

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Voronkov, Andrei. "Inconsistencies in Ontologies." In Logics in Artificial Intelligence, 19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11853886_3.

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Galitsky, Boris, Dmitry Ilvovsky, and Elizaveta Goncharova. "Relying on Discourse Trees to Extract Medical Ontologies from Text." In Artificial Intelligence, 215–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86855-0_15.

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Peñaloza, Rafael. "Introduction to Probabilistic Ontologies." In Reasoning Web. Declarative Artificial Intelligence, 1–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60067-9_1.

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Calvanese, Diego. "Query Answering over Description Logic Ontologies." In Logics in Artificial Intelligence, 1–17. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11558-0_1.

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Papatheodorou, Christos, and Alexandra Vassiliou. "Discovering Ontologies for e-Learning Platforms." In Advances in Artificial Intelligence, 576–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752912_72.

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Bienvenu, Meghyn, Michel Leclère, Marie-Laure Mugnier, and Marie-Christine Rousset. "Reasoning with Ontologies." In A Guided Tour of Artificial Intelligence Research, 185–215. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-06164-7_6.

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Dartigues, Christel, and Parisa Ghodous. "Product Data Exchange Using Ontologies." In Artificial Intelligence in Design ’02, 617–37. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-0795-4_29.

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Knorr, Matthias. "On Combining Ontologies and Rules." In Reasoning Web. Declarative Artificial Intelligence, 22–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95481-9_2.

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Valls, Aida, Antonio Moreno, and Joan Borràs. "Preference Representation with Ontologies." In Multicriteria Decision Aid and Artificial Intelligence, 77–99. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118522516.ch4.

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Ensan, Faezeh, and Weichang Du. "Aspects of Inconsistency Resolution in Modular Ontologies." In Advances in Artificial Intelligence, 84–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68825-9_9.

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Conference papers on the topic "Ontologies (artificial intelligence)"

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"Ontologies for Authoring, or Authoring Ontologies?" In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004330503640369.

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Bettahar, Fathia, Claude Moulin, and Jean-Paul A. Barthes. "Ontologies supporting eGovernment Services." In 2005 Purtuguese Conference on Artificial Intelligence. IEEE, 2005. http://dx.doi.org/10.1109/epia.2005.341274.

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Zhang, S., Y. Sun, Y. Peng, and X. Wang. "A Practical Tool for Uncertainty in OWL Ontologies." In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.674-007.

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Bienvenu, Meghyn, Quentin Manière, and Michaël Thomazo. "Cardinality Queries over DL-Lite Ontologies." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/248.

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Ontology-mediated query answering (OMQA) employs structured knowledge and automated reasoning in order to facilitate access to incomplete and possibly heterogeneous data. While most research on OMQA adopts (unions of) conjunctive queries as the query language, there has been recent interest in handling queries that involve counting. In this paper, we advance this line of research by investigating cardinality queries (which correspond to Boolean atomic counting queries) coupled with DL-Lite ontologies. Despite its apparent simplicity, we show that such an OMQA setting gives rise to rich and complex behaviour. While we prove that cardinality query answering is tractable (TC0) in data complexity when the ontology is formulated in DL-Lite-core, the problem becomes coNP-hard as soon as role inclusions are allowed. For DL-Lite-pos-H (which allows only positive axioms), we establish a P-coNP dichotomy and pinpoint the TC0 cases; for DL-Lite-core-H (allowing also negative axioms), we identify new sources of coNP complexity and also exhibit L-complete cases. Interestingly, and in contrast to related tractability results, we observe that the canonical model may not give the optimal count value in the tractable cases, which led us to develop an entirely new approach based upon exploring a space of strategies to determine the minimum possible number of query matches.
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Ceylan, İsmail İlkan, Thomas Lukasiewicz, Rafael Peñaloza, and Oana Tifrea-Marciuska. "Query Answering in Ontologies under Preference Rankings." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/131.

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We present an ontological framework, based on preference rankings, that allows users to express their preferences between the knowledge explicitly available in the ontology. Using this formalism, the answers for a given query to an ontology can be ranked by preference, allowing users to retrieve the most preferred answers only. We provide a host of complexity results for the main computational tasks in this framework, for the general case, and for EL and DL-Lite_core as underlying ontology languages.
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Jakob, Stefan, Alexander Jahl, Harun Baraki, and Kurt Geihs. "Generating Commonsense Ontologies with Answer Set Programming." In 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010191905380545.

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Annane, Amina, Mouna Kamel, and Nathalie Aussenac-Gilles. "Comparing Business Process Ontologies for Task Monitoring." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008978706340643.

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Korotkiy, Maksym. "On the Effect of Ontologies on Quality of Web Applications." In 2005 Purtuguese Conference on Artificial Intelligence. IEEE, 2005. http://dx.doi.org/10.1109/epia.2005.341276.

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Bienvenu, Meghyn, Quentin Manière, and Michaël Thomazo. "Answering Counting Queries over DL-Lite Ontologies." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/223.

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Ontology-mediated query answering (OMQA) is a promising approach to data access and integration that has been actively studied in the knowledge representation and database communities for more than a decade. The vast majority of work on OMQA focuses on conjunctive queries, whereas more expressive queries that feature counting or other forms of aggregation remain largely unexplored. In this paper, we introduce a general form of counting query, relate it to previous proposals, and study the complexity of answering such queries in the presence of DL-Lite ontologies. As it follows from existing work that query answering is intractable and often of high complexity, we consider some practically relevant restrictions, for which we establish improved complexity bounds.
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Carral, David, Irina Dragoste, and Markus Krötzsch. "Detecting Chase (Non)Termination for Existential Rules with Disjunctions." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/128.

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The restricted chase is a sound and complete algorithm for conjunctive query answering over ontologies of disjunctive existential rules. We develop acyclicity conditions to ensure its termination. Our criteria cannot always detect termination (the problem is undecidable), and we develop the first cyclicity criteria to show non-termination of the restricted chase. Experiments on real-world ontologies show that our acyclicity notions improve significantly over known criteria.
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