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

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Published: 14 March 2023

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1

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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7

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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8

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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9

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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10

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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Daradkeh, Yousef Ibrahim, and Iryna Tvoroshenko. "Application of an Improved Formal Model of the Hybrid Development of Ontologies in Complex Information Systems." Applied Sciences 10, no. 19 (September 27, 2020): 6777. http://dx.doi.org/10.3390/app10196777.

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Ontologies in artificial intelligence systems are an effective way to represent and integrate knowledge and data. The property of such structures is that any subject area is accurately described in formal language. There is a problem in the research and determination of the adequacy of ontologies under development. The perspective directions are model construction for the development of fuzzy ontologies and also the creation of methods for evaluating adequacy. The achieved results allow one to implement the processes of supporting the development and integration of ontologies of complex systems on the basis of intelligent approaches. The method is proposed to solve the problem of alternative representation and the integration of knowledge and data in artificial intelligence systems. The methodology of improving the model of the hybrid development of fuzzy ontologies is described here; it provides the preliminary modification of models of extensive and intensive progress of ontologies in space and time. The identified features of fuzzy ontology processing allow us to create a procedure for finding and eliminating inadequacies. The software implementation of the application for the integration and presentation of heterogeneous data is carried out. The consumption of Random Access Memory (RAM) for the proposed models is analyzed. The further perspectives of the proposed research are determined in accordance with the principles of classification.

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STUCKENSCHMIDT, HEINER. "Foreword: ontologies for distributed systems." Knowledge Engineering Review 18, no. 3 (September 2003): 193–95. http://dx.doi.org/10.1017/s0269888904000013.

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The benefits of using ontologies have been recognised in many areas such as knowledge and content management, electronic commerce and recently the emerging field of the Semantic Web. These new applications can be seen as a great success of research in ontologies. On the other hand, moving into real application comes with new challenges that need to be addressed on a principled level rather than for specific applications. This special issue will be devoted to less well-explored topics that have come into focus recently as a response to the new problems we face when trying to use ontologies in heterogeneous distributed environments. These environments include the use of ontologies in peer-to-peer and pervasive computing systems.

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GE, YANFENG, YONG YU, XING ZHU, SHEN HUANG, and MIN XU. "OntoVote: a scalable distributed vote-collecting mechanism for ontology drift on a P2P platform." Knowledge Engineering Review 18, no. 3 (September 2003): 257–63. http://dx.doi.org/10.1017/s0269888904000062.

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Ontologies provide potential support for knowledge and content management on a P2P platform. Although we can design ontologies beforehand for an application, it is argued that in P2P environments static or predefined ontologies cannot satisfy the ever-changing requirements of all users. So we propose every user should make proposals for what kind of ontology is the most apt to his need. Collecting all these proposals (or votes) helps the drift of ontologies. This paper introduces OntoVote, a scalable distributed vote-collecting mechanism based on application-level broadcast trees, and describes how OntoVote can be applied to ontology drift on a P2P platform by discussing several problems involved in the voting process.

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Thayasivam, U., and P. Doshi. "Speeding Up Iterative Ontology Alignment using Block-Coordinate Descent." Journal of Artificial Intelligence Research 50 (August 25, 2014): 805–45. http://dx.doi.org/10.1613/jair.4366.

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In domains such as biomedicine, ontologies are prominently utilized for annotating data. Consequently, aligning ontologies facilitates integrating data. Several algorithms exist for automatically aligning ontologies with diverse levels of performance. As alignment applications evolve and exhibit online run time constraints, performing the alignment in a reasonable amount of time without compromising the quality of the alignment is a crucial challenge. A large class of alignment algorithms is iterative and often consumes more time than others in delivering solutions of high quality. We present a novel and general approach for speeding up the multivariable optimization process utilized by these algorithms. Specifically, we use the technique of block-coordinate descent (BCD), which exploits the subdimensions of the alignment problem identified using a partitioning scheme. We integrate this approach into multiple well-known alignment algorithms and show that the enhanced algorithms generate similar or improved alignments in significantly less time on a comprehensive testbed of ontology pairs. Because BCD does not overly constrain how we partition or order the parts, we vary the partitioning and ordering schemes in order to empirically determine the best schemes for each of the selected algorithms. As biomedicine represents a key application domain for ontologies, we introduce a comprehensive biomedical ontology testbed for the community in order to evaluate alignment algorithms. Because biomedical ontologies tend to be large, default iterative techniques find it difficult to produce a good quality alignment within a reasonable amount of time. We align a significant number of ontology pairs from this testbed using BCD-enhanced algorithms. Our contributions represent an important step toward making a significant class of alignment techniques computationally feasible.

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Steigmiller, Andreas, and Birte Glimm. "Pay-As-You-Go Description Logic Reasoning by Coupling Tableau and Saturation Procedures." Journal of Artificial Intelligence Research 54 (December 17, 2015): 535–92. http://dx.doi.org/10.1613/jair.4897.

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Nowadays, saturation-based reasoners for the OWL EL profile of the Web Ontology Language are able to handle large ontologies such as SNOMED very efficiently. However, it is currently unclear how saturation-based reasoning procedures can be extended to very expressive Description Logics such as SROIQ--the logical underpinning of the current and second iteration of the Web Ontology Language. Tableau-based procedures, on the other hand, are not limited to specific Description Logic languages or OWL profiles, but even highly optimised tableau-based reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau- and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance for a wide range of ontologies.

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MARTÍNEZ-CARRERAS, M. ANTONIA, ANDRÉS MUÑOZ, JUAN BOTÍA, and ANTONIO F. GÓMEZ-SKARMETA. "CREATING CONTEXT-AWARE COLLABORATIVE WORKING ENVIRONMENTS." International Journal on Artificial Intelligence Tools 20, no. 01 (February 2011): 195–207. http://dx.doi.org/10.1142/s0218213011000085.

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Context-aware systems are intended for providing services adapted to the needs of people, by taking into account their state and the information related to their environment. One alternative to represent this context information resides in the use of Semantic Web ontologies. They provide a formal vocabulary which allows to easily express and share knowledge. Additionally, several types of automatic knowledge manipulation and reasoning processes become available thanks to the formal features of such ontologies. The inclusion of context information through ontologies in Collaborative Working Environments (CWEs) may bring important benefits to team work inside an organization, such as an automatic selection between different collaborative services according to the team members' preferences and their current state. This paper describes the design and implementation of a context-reasoning system which has been integrated into a CWE architecture to take advantage of context-awareness.

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Cuenca Grau, B., I. Horrocks, Y. Kazakov, and U. Sattler. "Modular Reuse of Ontologies: Theory and Practice." Journal of Artificial Intelligence Research 31 (February 22, 2008): 273–318. http://dx.doi.org/10.1613/jair.2375.

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In this paper, we propose a set of tasks that are relevant for the modular reuse of ontologies. In order to formalize these tasks as reasoning problems, we introduce the notions of conservative extension, safety and module for a very general class of logic-based ontology languages. We investigate the general properties of and relationships between these notions and study the relationships between the relevant reasoning problems we have previously identified. To study the computability of these problems, we consider, in particular, Description Logics (DLs), which provide the formal underpinning of the W3C Web Ontology Language (OWL), and show that all the problems we consider are undecidable or algorithmically unsolvable for the description logic underlying OWL DL. In order to achieve a practical solution, we identify conditions sufficient for an ontology to reuse a set of symbols ``safely''---that is, without changing their meaning. We provide the notion of a safety class, which characterizes any sufficient condition for safety, and identify a family of safety classes--called locality---which enjoys a collection of desirable properties. We use the notion of a safety class to extract modules from ontologies, and we provide various modularization algorithms that are appropriate to the properties of the particular safety class in use. Finally, we show practical benefits of our safety checking and module extraction algorithms.

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Kollia, I., and B. Glimm. "Optimizing SPARQL Query Answering over OWL Ontologies." Journal of Artificial Intelligence Research 48 (October 30, 2013): 253–303. http://dx.doi.org/10.1613/jair.3872.

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The SPARQL query language is currently being extended by the World Wide Web Consortium (W3C) with so-called entailment regimes. An entailment regime defines how queries are evaluated under more expressive semantics than SPARQL's standard simple entailment, which is based on subgraph matching. The queries are very expressive since variables can occur within complex concepts and can also bind to concept or role names. In this paper, we describe a sound and complete algorithm for the OWL Direct Semantics entailment regime. We further propose several novel optimizations such as strategies for determining a good query execution order, query rewriting techniques, and show how specialized OWL reasoning tasks and the concept and role hierarchy can be used to reduce the query execution time. For determining a good execution order, we propose a cost-based model, where the costs are based on information about the instances of concepts and roles that are extracted from a model abstraction built by an OWL reasoner. We present two ordering strategies: a static and a dynamic one. For the dynamic case, we improve the performance by exploiting an individual clustering approach that allows for computing the cost functions based on one individual sample from a cluster. We provide a prototypical implementation and evaluate the efficiency of the proposed optimizations. Our experimental study shows that the static ordering usually outperforms the dynamic one when accurate statistics are available. This changes, however, when the statistics are less accurate, e.g., due to nondeterministic reasoning decisions. For queries that go beyond conjunctive instance queries we observe an improvement of up to three orders of magnitude due to the proposed optimizations.

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Zhang, Fu, and Z. M. Ma. "Representing and Reasoning About XML with Ontologies." Applied Intelligence 40, no. 1 (July 11, 2013): 74–106. http://dx.doi.org/10.1007/s10489-013-0446-4.

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Settas, Dimitrios L., Sulayman K. Sowe, and Ioannis G. Stamelos. "Detecting similarities in antipattern ontologies using semantic social networks: implications for software project management." Knowledge Engineering Review 24, no. 3 (September 2009): 287–307. http://dx.doi.org/10.1017/s0269888909990075.

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AbstractOntology has been recently proposed as an appropriate formalism to model software project management antipatterns, in order to encode antipatterns in a computer understandable form and introduce antipatterns to the Semantic Web. However, given two antipattern ontologies, the same entity can be described using different terminology. Therefore, the detection of similar antipattern ontologies is a difficult task. In this paper, we introduce a three-layered antipattern semantic social network, which involves the social network, the antipattern ontology network and the concept network. Social Network Analysis (SNA) techniques can be used to assist software project managers in finding similar antipattern ontologies. For this purpose, SNA measures are extracted from one layer of the semantic social network to another and this knowledge is used to infer new links between antipattern ontologies. The level of uncertainty associated with each new link is represented using Bayesian Networks (BNs). Furthermore, BNs address the issue of quantifying the uncertainty of the data collected regarding antipattern ontologies for the purposes of the conducted analysis. Finally, BNs are used to augment SNA by taking into account meta-information in their calculations. Hence, other knowledge not included in the social network can be used in order to search the social network for further inference. The benefits of using an antipattern semantic social network are illustrated using an example community of software project management antipattern ontologies.

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Lembo, Domenico, Daniele Pantaleone, Valerio Santarelli, and Domenico Fabio Savo. "Drawing OWL 2 ontologies with Eddy the editor." AI Communications 31, no. 1 (February 1, 2018): 97–113. http://dx.doi.org/10.3233/aic-180751.

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Chukkapalli, Sai Sree Laya, Sudip Mittal, Maanak Gupta, Mahmoud Abdelsalam, Anupam Joshi, Ravi Sandhu, and Karuna Joshi. "Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem." IEEE Access 8 (2020): 164045–64. http://dx.doi.org/10.1109/access.2020.3022763.

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23

Eiter, Thomas, Michael Fink, and Daria Stepanova. "Computing Repairs of Inconsistent DL-Programs over EL Ontologies." Journal of Artificial Intelligence Research 56 (July 27, 2016): 463–515. http://dx.doi.org/10.1613/jair.5047.

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Description Logic (DL) ontologies and non-monotonic rules are two prominent Knowledge Representation (KR) formalisms with complementary features that are essential for various applications. Nonmonotonic Description Logic (DL) programs combine these formalisms thus providing support for rule-based reasoning on top of DL ontologies using a well-defined query interface represented by so-called DL-atoms. Unfortunately, interaction of the rules and the ontology may incur inconsistencies such that a DL-program lacks answer sets (i.e., models), and thus yields no information. This issue is addressed by recently defined repair answer sets, for computing which an effective practical algorithm was proposed for DL-Lite A ontologies that reduces a repair computation to constraint matching based on so-called support sets. However, the algorithm exploits particular features of DL-Lite A and can not be readily applied to repairing DL-programs over other prominent DLs like EL. compared to DL-Lite A , in EL support sets may neither be small nor only few support sets might exist, and completeness of the algorithm may need to be given up when the support information is bounded. We thus provide an approach for computing repairs for DL-programs over EL ontologies based on partial (incomplete) support families. The latter are constructed using datalog query rewriting techniques as well as ontology approximation based on logical difference between EL-terminologies. We show how the maximal size and number of support sets for a given DL-atom can be estimated by analyzing the properties of a support hypergraph, which characterizes a relevant set of TBox axioms needed for query derivation. We present a declarative implementation of the repair approach and experimentally evaluate it on a set of benchmark problems; the promising results witness practical feasibility of our repair approach.

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Mohammed, Phaedra, and Permanand Mohan. "CONTEXTUALIZING LEARNING OBJECTS USING ONTOLOGIES." Computational Intelligence 23, no. 3 (August 2007): 339–55. http://dx.doi.org/10.1111/j.1467-8640.2007.00309.x.

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25

Jiang, Chao, and Xingsi Xue. "A uniform compact genetic algorithm for matching bibliographic ontologies." Applied Intelligence 51, no. 10 (March 12, 2021): 7517–32. http://dx.doi.org/10.1007/s10489-021-02208-6.

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Schwitter, Rolf. "CREATING AND QUERYING FORMAL ONTOLOGIES VIA CONTROLLED NATURAL LANGUAGE." Applied Artificial Intelligence 24, no. 1-2 (February 2, 2010): 149–74. http://dx.doi.org/10.1080/08839510903448700.

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KALFOGLOU, YANNIS, and MARCO SCHORLEMMER. "Ontology mapping: the state of the art." Knowledge Engineering Review 18, no. 1 (January 2003): 1–31. http://dx.doi.org/10.1017/s0269888903000651.

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Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

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Raikov, Alexander N. "Subjectivity of Explainable Artificial Intelligence." Russian Journal of Philosophical Sciences 65, no. 1 (June 25, 2022): 72–90. http://dx.doi.org/10.30727/0235-1188-2022-65-1-72-90.

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The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals.

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Swartout, W., and A. Tate. "Ontologies." IEEE Intelligent Systems and their Applications 14, no. 1 (January 1999): 18–19. http://dx.doi.org/10.1109/mis.1999.747901.

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VEALE, TONY, and YANFEN HAO. "A context-sensitive framework for lexical ontologies." Knowledge Engineering Review 23, no. 1 (March 2008): 101–15. http://dx.doi.org/10.1017/s0269888907001270.

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AbstractHuman categorization is neither a binary nor a context-free process. Rather, the criteria that govern the use and recognition of certain concepts may be satisfied to different degrees in different contexts. In light of this reality, the idealized, static structure of a lexical-ontology like WordNet appears both excessively rigid and unduly fragile when faced with real texts that draw upon different contexts to communicate different world-views. In this paper we describe a syntagmatic, corpus-based approach to redefining the concepts of a lexical-ontology like WordNet in a functional, gradable and context-sensitive fashion. We describe how the most diagnostic properties of concepts, on which these functional definitions are based, can be automatically acquired from the Web, and demonstrate how these properties are more predictive of how concepts are actually used and perceived than properties derived from other sources (such as WordNet itself).

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García, Félix, Francisco Ruiz, Coral Calero, Manuel F. Bertoa, Antonio Vallecillo, Beatriz Mora, and Mario Piattini. "Effective use of ontologies in software measurement." Knowledge Engineering Review 24, no. 1 (March 2009): 23–40. http://dx.doi.org/10.1017/s0269888909000125.

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AbstractOntologies are frequently used in the context of software and technology engineering. These can be grouped into two main categories, depending on whether they are used to describe the knowledge of a domain (domain ontologies) or whether they are used as software artifacts in software development processes. This paper presents some experiences and lessons learnt from the effective use of an ontology for Software Measurement, called software measurement ontology (SMO). The SMO was developed some years ago as a result of a thorough analysis of the software measurement domain. Its use as a domain ontology is presented first, a description of how the SMO can serve as a conceptual basis for comparing international standards related to software measurement. Second, the paper describes several examples of the applications of SMO as a software artifact. In particular, we show how the SMO can be instantiated to define a data quality model for Web portals, and also how it can be used to define a Domain-Specific Language (DSL) for measuring software entities. These examples show the significant role that ontologies can play as software artifacts in the realm of model-driven engineering and domain-specific modeling.

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SCHLENOFF, CRAIG, STEPHEN BALAKIRSKY, MIKE USCHOLD, RON PROVINE, and SCOTT SMITH. "Using ontologies to aid navigation planning in autonomous vehicles." Knowledge Engineering Review 18, no. 3 (September 2003): 243–55. http://dx.doi.org/10.1017/s0269888904000050.

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This paper explores the hypothesis that ontologies can be used to improve the capabilities and performance of on-board route planning for autonomous vehicles. We name a variety of general benefits that ontologies may provide, and list numerous specific ways that ontologies may be used in different components of our chosen infrastructure: the 4D/RCS system architecture developed at NIST. Our initial focus is on simple roadway driving scenarios where the controlled vehicle encounters objects in its path. Our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with the different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to avoid the object. We describe our current experiments and plans for future work.

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Zhou, Yujiao, Bernardo Cuenca Grau, Yavor Nenov, Mark Kaminski, and Ian Horrocks. "PAGOdA: Pay-As-You-Go Ontology Query Answering Using a Datalog Reasoner." Journal of Artificial Intelligence Research 54 (November 11, 2015): 309–67. http://dx.doi.org/10.1613/jair.4757.

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Answering conjunctive queries over ontology-enriched datasets is a core reasoning task for many applications. Query answering is, however, computationally very expensive, which has led to the development of query answering procedures that sacrifice either expressive power of the ontology language, or the completeness of query answers in order to improve scalability. In this paper, we describe a hybrid approach to query answering over OWL 2 ontologies that combines a datalog reasoner with a fully-fledged OWL 2 reasoner in order to provide scalable `pay-as-you-go' performance. The key feature of our approach is that it delegates the bulk of the computation to the datalog reasoner and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. Furthermore, although our main goal is to efficiently answer queries over OWL 2 ontologies and data, our technical results are very general and our approach is applicable to first-order knowledge representation languages that can be captured by rules allowing for existential quantification and disjunction in the head; our only assumption is the availability of a datalog reasoner and a fully-fledged reasoner for the language of interest, both of which are used as `black boxes'. We have implemented our techniques in the PAGOdA system, which combines the datalog reasoner RDFox and the OWL 2 reasoner HermiT. Our extensive evaluation shows that PAGOdA succeeds in providing scalable pay-as-you-go query answering for a wide range of OWL 2 ontologies, datasets and queries.

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Sleeman, Jennifer, Tim Finin, and Anupam Joshi. "Entity Type Recognition for Heterogeneous Semantic Graphs." AI Magazine 36, no. 1 (March 25, 2015): 75–86. http://dx.doi.org/10.1609/aimag.v36i1.2569.

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We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

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Konev, B., M. Ludwig, D. Walther, and F. Wolter. "The Logical Difference for the Lightweight Description Logic EL." Journal of Artificial Intelligence Research 44 (August 17, 2012): 633–708. http://dx.doi.org/10.1613/jair.3552.

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We study a logic-based approach to versioning of ontologies. Under this view, ontologies provide answers to queries about some vocabulary of interest. The difference between two versions of an ontology is given by the set of queries that receive different answers. We investigate this approach for terminologies given in the description logic EL extended with role inclusions and domain and range restrictions for three distinct types of queries: subsumption, instance, and conjunctive queries. In all three cases, we present polynomial-time algorithms that decide whether two terminologies give the same answers to queries over a given vocabulary and compute a succinct representation of the difference if it is non- empty. We present an implementation, CEX2, of the developed algorithms for subsumption and instance queries and apply it to distinct versions of Snomed CT and the NCI ontology.

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Analyti, Anastasia, Grigoris Antoniou, Carlos Viegas Damásio, and Ioannis Pachoulakis. "A framework for modular ERDF ontologies." Annals of Mathematics and Artificial Intelligence 67, no. 3-4 (March 2013): 189–249. http://dx.doi.org/10.1007/s10472-013-9350-1.

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Androutsopoulos, I., G. Lampouras, and D. Galanis. "Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System." Journal of Artificial Intelligence Research 48 (November 22, 2013): 671–715. http://dx.doi.org/10.1613/jair.4017.

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We present NaturalOWL, a natural language generation system that produces texts describing individuals or classes of OWL ontologies. Unlike simpler OWL verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like NaturalOWL, one can publish information in OWL on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of NaturalOWL, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent llinguistic resources are available, NaturalOWL produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.

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Madanska, Sebiha, Stanimir Stoyanov, and Asya Stoyanova-Doycheva. "Digitalization of Bulgarian Residential Architecture in and around the Bulgarian Revival Period – Typological Groups and Houses." Cultural and Historical Heritage: Preservation, Presentation, Digitalization 7, no. 2 (2021): 94–106. http://dx.doi.org/10.26615/issn.2367-8038.2021_2_008.

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The article presents the Bulgarian Revival architecture in the context of Informatics, and in particular of the Artificial Intelligence. Ontological engineering is dealing with the semantic modeling of real-world concepts and the relations between them under the influence of semantic axioms and machine-readable judgments. Ontologies that are described in the article were developed to be included in the repository with multiple ontologies for Bulgarian cultural and historical heritage. Keywords: Оntology; Semantic Мodelling; Protégé; Revival Аrchitecture; Intelligent Tourist Guide

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Florrence, Merlin. "Building a multilingual ontology for education domain using monto method." Computer Science and Information Technologies 1, no. 2 (July 1, 2020): 47–53. http://dx.doi.org/10.11591/csit.v1i2.p47-53.

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Ontologies are emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Knowledge based information retrieval are widely used in the domain like Education, Artificial Intelligence, Healthcare and so on. It is important to provide multilingual information of those domains to facilitate multi-language users. In this paper, we propose a MOnto (Multilingual Ontology) methodology to develop multilingual ontology applications for education domain. New algorithms are proposed for merging and mapping multilingual ontologies.

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Staab, S., R. Studer, H. P. Schnurr, and Y. Sure. "Knowledge processes and ontologies." IEEE Intelligent Systems 16, no. 1 (January 2001): 26–34. http://dx.doi.org/10.1109/5254.912382.

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Fensel, D., D. L. McGuiness, E. Schulten, Wee Keong Ng, Ge Peng Lim, and Guanghao Yan. "Ontologies and electronic commerce." IEEE Intelligent Systems 16, no. 1 (January 2001): 8–14. http://dx.doi.org/10.1109/mis.2001.1183337.

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van Harmelen, F., I. Horrocks, P. Clark, P. F. Patel-Schneider, M. Uschold, M. C. Rousset, J. Hendler, and G. Schreiber. "Ontologies' KISSES in standardization." IEEE Intelligent Systems 17, no. 2 (March 2002): 70–79. http://dx.doi.org/10.1109/mis.2002.999223.

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Martinez-Romero, Marcos, Jose M. Vazquez-Naya, Juan R. Rabunal, Salvador Pita-Fernandez, Ramiro Macenlle, Javier Castro-Alvarino, Leopoldo Lopez-Roses, et al. "Artificial Intelligence Techniques for Colorectal Cancer Drug Metabolism: Ontologies and Complex Networks." Current Drug Metabolism 11, no. 4 (May 1, 2010): 347–68. http://dx.doi.org/10.2174/138920010791514289.

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Breuker, Joost, André Valente, and Radboud Winkels. "Legal Ontologies in Knowledge Engineering and Information Management." Artificial Intelligence and Law 12, no. 4 (December 2004): 241–77. http://dx.doi.org/10.1007/s10506-006-0002-1.

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Despres, Sylvie, and Sylvie Szulman. "Merging of legal micro-ontologies from European directives." Artificial Intelligence and Law 15, no. 2 (February 10, 2007): 187–200. http://dx.doi.org/10.1007/s10506-007-9028-2.

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García, Roberto, Rosa Gil, and Jaime Delgado. "A web ontologies framework for digital rights management." Artificial Intelligence and Law 15, no. 2 (February 1, 2007): 137–54. http://dx.doi.org/10.1007/s10506-007-9032-6.

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Ogunniye, Gideon, and Nadin Kokciyan. "A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things." Journal of Artificial Intelligence Research 76 (January 6, 2023): 163–92. http://dx.doi.org/10.1613/jair.1.14000.

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People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements.

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Dubrovsky, David I., A. R. Efimov, Vladimir E. Lepskiy, and B. B. Slavin. "The Fetish of Artificial Intelligence." Russian Journal of Philosophical Sciences 65, no. 1 (June 25, 2022): 44–71. http://dx.doi.org/10.30727/0235-1188-2022-65-1-44-71.

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The article presents grounds for defining the fetish of artificial intelligence (AI). We highlight the fundamental differences of AI from all earlier technological advances, as they are primarily related to its introduction into the human cognitive sphere and generating fundamentally new uncontrollable consequences for society. We provide solid evidence that the leaders of the globalist project are the main beneficiaries of the AI fetish. This is clearly manifested in the works of philosophers who are close to major technology corporations and their mega-projects. We suggest considering the problem of how to use the capabilities of AI to overcome the growing international conflicts and the global crisis. The focus is on the problem of agency, which solution from the standpoint of an anthropomorphic approach to AI is fraught with serious negative consequences. Endowing AI with agency, responsibility is implicitly removed from the person who uses the technology, and the established legislative practice is also destroyed. We present AI as an agent endowed with a set of invariant generalized qualities that is similar to natural subjects. These qualities include: the ability to deliberation, reflexivity, communication and elements of sociability. Such a representation of AI as an agent (pseudo-subject) is consistent with the principle of distributed control in biology and psychology, which was called the principle of a dual subject. In combination with the systems of principles and ontologies specified in the concept of post-nonclassical cybernetics of self-developing environments, this will allow the use of AI as a means of social innovation, while maintaining control over AI technologies. This will also help to pose and solve the problem of integrating formations of artificial and natural intelligence while maintaining the basic qualities of carriers of natural intelligence.

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Humm, Bernhard G., Hermann Bense, Michael Fuchs, Benjamin Gernhardt, Matthias Hemmje, Thomas Hoppe, Lukas Kaupp, et al. "Machine intelligence today: applications, methodology, and technology." Informatik Spektrum 44, no. 2 (February 18, 2021): 104–14. http://dx.doi.org/10.1007/s00287-021-01343-1.

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AbstractMachine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning.

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Rehman, Zobia, and Claudiu V. Kifor. "Applications of Ontologies in Knowledge Management Systems." ACTA Universitatis Cibiniensis 65, no. 1 (December 1, 2014): 78–80. http://dx.doi.org/10.1515/aucts-2015-0014.

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Abstract Enterprises are realizing that their core asset in 21st century is knowledge. In an organization knowledge resides in databases, knowledge bases, filing cabinets and peoples' head. Organizational knowledge is distributed in nature and its poor management causes repetition of activities across the enterprise. To get true benefits from this asset, it is important for an organization to “know what they know”. That’s why many organizations are investing a lot in managing their knowledge. Artificial intelligence techniques have a huge contribution in organizational knowledge management. In this article we are reviewing the applications of ontologies in knowledge management realm

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