Upper ontology

About: Upper ontology is a research topic. Over the lifetime, 9767 publications have been published within this topic receiving 220721 citations. The topic is also known as: top-level ontology & foundation ontology.

Understanding and using the meaning of statements in a bio-ontology: Recasting the Gene Ontology in OWL

Abstract: The bio-ontology community falls into two camps: first we have biology domain experts, who actually hold the knowledge we wish to capture in ontologies; second, we have ontology specialists, who hold knowledge about techniques and best practice on ontology development. In the bio-ontology domain, these two camps have often come into conflict, especially where pragmatism comes into conflict with perceived best practice. One of these areas is the insistence of computer scientists on a well-defined semantic basis for the Knowledge Representation language being used. In this article, we will first describe why this community is so insistent. Second, we will illustrate this by examining the semantics of the Web Ontology Language and the semantics placed on the Directed Acyclic Graph as used by the Gene Ontology. Finally we will reconcile the two representations, including the broader Open Biomedical Ontologies format. The ability to exchange between the two representations means that we can capitalise on the features of both languages. Such utility can only arise by the understanding of the semantics of the languages being used. By this illustration of the usefulness of a clear, well-defined language semantics, we wish to promote a wider understanding of the computer science perspective amongst potential users within the biological community.

Ontology-based computational intelligent multi-agent and its application to CMMI assessment

Abstract: This study presents an ontology-based computational intelligent multi-agent system for Capability Maturity Model Integration (CMMI) assessment. An ontology model is developed to represent the CMMI domain knowledge that will be adopted by the computational intelligent multi-agent. The CMMI ontology is predefined by domain experts, and created by the ontology generating system. The computational intelligent multi-agent comprises a natural language processing agent, an ontological reasoning agent and a summary agent. The multi-agent deals with the evaluation reports from the natural language processing agent, infers the term relation strength between the ontology and the evaluation report, and then summarizes the main sentences of the evaluation report. The summary reports are meanwhile transmitted back to the domain expert, which makes the domain expert further adjust the CMMI ontology. Experimental results indicate that the ontology-based computational intelligent multi-agent can effectively summarize the evaluation reports for the CMMI assessment.

Modelling Multilinguality in Ontologies

Abstract: Multilinguality in ontologies has become an impending need for institutions worldwide with valuable linguistic resources in different natural languages. Since most ontologies are developed in one language, obtaining multilingual ontologies implies to localize or adapt them to a concrete language and culture community. As the adaptation of the ontology conceptualization demands considerable efforts, we propose to modify the ontology terminological layer, and provide a model called Linguistic Information Repository (LIR) that associated to the ontology meta-model allows terminological layer localization.

Semi-automatic Ontology Alignment for Geospatial Data Integration

Abstract: In geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the local databases. Once the alignment between the global ontology and each local ontology is established, users can potentially query hundreds of databases using a single query that hides the underlying heterogeneities. Using our approach, querying can be easily extended to a new data source by aligning a local ontology with the global one. For this purpose, we have designed and implemented a tool to align ontologies. The output of this tool is a set of mappings between concepts, which will be used to produce the queries to the local databases once a query is formulated on the global ontology. To facilitate the user’s task, we propose semi-automatic methods for propagating such mappings along the ontologies. In this paper, we present the principles behind our propagation method, the implementation of the tool, and we conclude with a discussion of interesting cases and proposed solutions.

Text Mining Techniques to Automatically Enrich a Domain Ontology

Abstract: Though the utility of domain ontologies is now widely acknowledged in the IT (Information Technology) community, several barriers must be overcome before ontologies become practical and useful tools. A critical issue is the ontology construction, i.e., the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. Text mining tools may be of great help in this task. The work presented in this paper illustrates the guidelines of SymOntos, ontology management system, and the text mining approach adopted herein to support ontology building. The latter operates by extracting, from the related literature, the prominent domain concepts and the semantic relations among them.