Topic
Ontology-based data integration
About: Ontology-based data integration is a research topic. Over the lifetime, 11065 publications have been published within this topic receiving 216888 citations.
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TL;DR: OntoMerge, an online system for ontology merging and automated reasoning, can implement ontology translation with inputs and outputs in OWL or other web languages.
Abstract: Ontologies are a crucial tool for formally specifying the vocabulary and relationship of concepts used on the Semantic Web. In order to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. Ontology translation is required when translating datasets, generating ontology extensions, and querying through different ontologies. OntoMerge, an online system for ontology merging and automated reasoning, can implement ontology translation with inputs and outputs in OWL or other web languages. Ontology translation can be thought of in terms of formal inference in a merged ontology. The merge of two related ontologies is obtained by taking the union of the concepts and the axioms defining them, and then adding bridging axioms that relate their concepts. The resulting merged ontology then serves as an inferential medium within which translation can occur. Our internal representation, Web-PDDL, is a strong typed first-order logic language for web application. Using a uniform notation for all problems allows us to factor out syntactic and semantic translation problems, and focus on the latter. Syntactic translation is done by an automatic translator between Web-PDDL and OWL or other web languages. Semantic translation is implemented using an inference engine (OntoEngine) which processes assertions and queries in Web-PDDL syntax, running in either a data-driven (forward chaining) or demand-driven (backward chaining) way.
207 citations
07 Nov 2003
TL;DR: A Semantic Web portal, called OntoKhoj that is designed to simplify the Ontology Engineering process and allow agents and ontology engineers to retrieve trustworthy, authoritative knowledge, and expedite the process of ontology engineering through extensive reuse of ontologies is proposed.
Abstract: The goal of the next generation Web is to build virtual communities, wherein software agents and people can work in cooperation by sharing knowledge. To achieve this goal, the emerging Semantic Web community has proposed ontologies to express knowledge in a machine understandable way. The process of building and maintaining ontologies, which is known as Ontology Engineering, presents unique challenges. These challenges are related to lack of trustworthy and authoritative knowledge sources and absence of a centralized repository to locate ontologies to be reused. In this paper, we propose a Semantic Web portal, called OntoKhoj that is designed to simplify the Ontology Engineering process. The methodology in developing OntoKhoj is based on algorithms used for searching, aggregating, ranking and classifying ontologies in Semantic Web. The proposed OntoKhoj would 1) allow agents and ontology engineers to retrieve trustworthy, authoritative knowledge, and 2) expedite the process of ontology engineering through extensive reuse of ontologies. We have implemented the OntoKhoj portal and further validated our system on the real ontological data in the Semantic Web.
207 citations
TL;DR: The proposed approach focuses on how to support information autonomy that allows the individual team members to keep their own preferred languages or information models rather than requiring them all to adopt standardized terminology.
206 citations
01 Jan 2010
TL;DR: The General Formal Ontology (GFO) as discussed by the authors is a foundational ontology integrating objects and processes, which includes categories of objects (3D objects) as well as processes (4D entities) and both are integrated into one coherent framework.
Abstract: The current chapter presents an overview about the current stage of the foundational ontology GFO. GFO (General Formal Ontology). GFO is a foundational ontology integrating objects and processes. It is being developed by the research group Onto-Med (Ontologies in Medicine) at the University of Leipzig. Unique selling properties of GFO are the following: it includes categories of objects (3D objects) as well as of processes (4D entities) and both are integrated into one coherent framework. GFO presents a multi-categorial approach by admitting universals, concepts, and symbol structures and their interrelations. GFO adopts categories pertaining to levels of reality, and it is designed to support interoperability by principles of ontological mapping and reduction. GFO contains several novel ontological modules, in particular, a module for functions and a module for roles. GFO is designed for applications, firstly in medical, biological, and biomedical areas, but also in the fields of economics and sociology.
206 citations
27 May 2012
TL;DR: This paper presents a semi-automatic approach to map structured sources to ontologies in order to build semantic descriptions (source models) and provides a graphical user interface that allows a user to interactively refine the models.
Abstract: Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semi-automatic approach to map structured sources to ontologies in order to build semantic descriptions (source models). Since the precise mapping is sometimes ambiguous, we also provide a graphical user interface that allows a user to interactively refine the models. The resulting source models can then be used to convert data into RDF with respect to a given ontology or to define a SPARQL end point that can be queried with respect to an ontology. We evaluated the overall approach on a variety of sources and show that it can be used to quickly build source models with minimal user interaction.
206 citations