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Ontologies, such as UMLS and WordNet, are generally very large, and are normally the source of more specialised and smaller ontologies tailored for a certain application. It is natural that the source ontologies be multiple large ...
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Ontologies, such as UMLS and WordNet, are generally very large, and are normally the source of more specialised and smaller ontologies tailored for a certain application. It is natural that the source ontologies be multiple large ontologies, each of which is extracted, and then merged to create a smaller and tailored ontology for a specific domain. Therefore, extracting sub-ontologies as well as merging them is a primary process. In this paper, we propose sub-ontology extraction and merging, whereby multiple sub-ontologies are extracted from various source ontologies and then these extracted sub-ontologies are merged to form a complete ontology to be used by the user. We use the maximum extraction method to facilitate this. A walkthrough case study using the UMLS meta-thesaurus ontology is also presented.
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Ontologies are widely considered to be the backbone of the Semantic Web. Its importance is being recognized in a multiplicity of research fields and application areas. Ontology building is crucial for the aforementioned issues. Th...
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Ontologies are widely considered to be the backbone of the Semantic Web. Its importance is being recognized in a multiplicity of research fields and application areas. Ontology building is crucial for the aforementioned issues. The main goal of this research is to investigate an effective methodology for collaborative ontology building. A trust-based consensus is proposed to support an efficient solution for conflicts among different viewpoints of participants in the collaborative ontology (CoO) building process. In every cycle of the iterative collaborative process, the ontology is refined and evolved by reaching a trust-based consensus among the participants' viewpoints. The proposed method is applied for collaborative Vietnamese WordNet building. The result is significant in comparison with previous approaches.
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As a knowledge representation tool, ontologies have been widely applied in many fields such as knowledge management and information integration, etc. Ontology measurement is an important challenge in the field of knowledge managem...
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As a knowledge representation tool, ontologies have been widely applied in many fields such as knowledge management and information integration, etc. Ontology measurement is an important challenge in the field of knowledge management in order to manage the development of ontology based systems and reduce the risk of project failure. This paper proposes a generic implementation framework for stable semantic ontology measurement. Through this framework, an ontology will be measured according to its semantic enriched representation model (SERM). The SERM model of an ontology can be used for stably measuring the semantics of the ontology. Then ontology metrics are integrated into the framework to measure candidate ontologies according to its SERM model. The related experiments are made to show that the framework can effectively measure the semantics of ontologies.
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GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling deci...
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GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling decisions for individual domains may need to be responsive to domain-specific criteria, a linguistically-motivated ontology offers a characterization that generalizes across domains because its design criteria are derived independently both of domain and of application. With respect to this mediating role, the use of GUM resembles (and partially predates) the adoption of upper ontologies as tools for mediating across domains and for supporting domain modeling. This paper briefly introduces the ontology, setting out its origins, design principles and applications. The example cases for this special issue are then described, illustrating particularly some of the principal differences and similarities of GUM to non-linguistically motivated upper ontologies.
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This paper presents two contributions to the field of Ontology Evaluation. First, a live catalogue of pitfalls that extends previous works on modeling errors with new pitfalls resulting from an empirical analysis of over 693 ontol...
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This paper presents two contributions to the field of Ontology Evaluation. First, a live catalogue of pitfalls that extends previous works on modeling errors with new pitfalls resulting from an empirical analysis of over 693 ontologies. Such a catalogue classifies pitfalls according to the Structural, Functional and Usability-Profiling dimensions. For each pitfall, we incorporate the value of its importance level (critical, important and minor) and the number of ontologies where each pitfall has been detected. Second, OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies and targeted at newcomers and domain experts unfamiliar with description logics and ontology implementation languages. The tool operates independently of any ontology development platform and is available online. The evaluation of the system is provided both through a survey of users' satisfaction and worldwide usage statistics. In addition, the system is also compared with existing ontology evaluation tools in terms of coverage of pitfalls detected.
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In Semantic web, domain model is an abstract image of a small part of the world. It serves to capture the common understanding of the domain to create a basis for clear communication. This paper performs a study clarifying the dif...
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In Semantic web, domain model is an abstract image of a small part of the world. It serves to capture the common understanding of the domain to create a basis for clear communication. This paper performs a study clarifying the different domain modeling types to help the individual interested by this topic to gain new insights and gudlines. In this paper, after a general introduction about the basis of ontologies and its components, a description of the most common domain modeling schema and a comparison between them has given.
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The reuse of ontologies existing on the Web should be required to speed up ontology construction process. However, current ontology mapping approaches assume that two input ontologies are given not discovered from available semant...
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The reuse of ontologies existing on the Web should be required to speed up ontology construction process. However, current ontology mapping approaches assume that two input ontologies are given not discovered from available semantic data of the web. In this paper, we present a new approach, which integrates ontology selection, mapping, and merging processes in order to minimise human mediation. Our ontology selection mechanism accepts a hand-crafted ontology as query ontology to search already constructed relevant ontologies. In addition, we develop a ranking method based on syntactic and semantic structure of classes to provide the best search result to the user.
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Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software dev...
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Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability. (C) 2018 Elsevier B.V. All rights reserved.
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A foundational ontology contributes to ontology-driven conceptual data modelling and is used to solve interoperability issues among domain ontologies. Multiple foundational ontologies have been developed in recent years, and most ...
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A foundational ontology contributes to ontology-driven conceptual data modelling and is used to solve interoperability issues among domain ontologies. Multiple foundational ontologies have been developed in recent years, and most of them are available in several versions. This has re-introduced the interoperability problem, increased the need for a coordinated and structured comparison and elucidation of modelling decisions, and raised the requirement for software infrastructure to address this. We present here a basic step in that direction with the Repository of Ontologies for MULtiple USes, ROMULUS, which is the first online library of machine-processable, modularised, aligned, and logic-based merged foundational ontologies. In addition to the typical features of a model repository, it has a foundational ontology recommender covering features of six foundational ontologies, tailor-made modules for easier reuse, and a catalogue of mappable and non-mappable elements among the BFO, GFO and DOLCE foundational ontologies.
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Query rewriting is an important technique for answering queries over data described using ontologies. In query rewriting the input, a conjunctive query (CQ) q and an ontology O, is transformed into a new datalog query that capture...
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Query rewriting is an important technique for answering queries over data described using ontologies. In query rewriting the input, a conjunctive query (CQ) q and an ontology O, is transformed into a new datalog query that captures all answers of q over O and any dataset D. This process can be time-consuming as it is of high computational complexity. In many real-world applications, this can be particularly problematic as they involve frequent and relatively small modifications on quite large ontologies. Hence, a drawback of most of modern query rewriting systems is that every time the initial ontology is modified, e.g. when new axioms are added or existing ones removed, they compute a new rewriting from scratch. In this paper, we study the problem of computing a rewriting for a CQ over an ontology that has been modified. We do this by reusing the information obtained by the extraction of some previous rewriting with the goal of performing the least possible computations. We study the problem theoretically, present detailed algorithms for both ontology revision and ontology contraction and finally, present an extensive experimental evaluation using the well-known query rewriting systems Requiem and Rapid.
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