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.

FOIS introduction: Ontology---towards a new synthesis

Abstract: This introduction to the Second International Conference on Formal Ontology and Information Systems presents a brief history of ontology as a discipline spanning the boundaries of philosophy and information science. We sketch some of the reasons for the growth of ontology in the information science field, and offer a preliminary stocktaking of how the term 'ontology' is currently used. We conclude by suggesting some grounds for optimism as concerns the future collaboration between philosophical ontologists and information scientistsPhilosophical ontology is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. Philosophical ontology takes many forms, from the metaphysics of Aristotle to the object-theory of Alexius Meinong. The term 'ontology' (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Gockel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. Its first occurrence in English as recorded by the OED appears in Bailey's dictionary of 1721, which defines ontology as 'an Account of being in the Abstract'Regardless of its name, what we now refer to as philosophical ontology has sought the definitive and exhaustive classification of entities in all spheres of being. It can thus be conceived as a kind of generalized chemistry. The taxonomies which result from philosophical ontology have been intended to be definitive in the sense that they could serve as answers to such questions as: What classes of entities are needed for a complete description and explanation of all the goings-on in the universe? Or: What classes of entities are needed to give an account of what makes true all truths? They have been designed to be exhaustive in the sense that all types of entities should be included, including also the types of relations by which entities are tied together.

Distance learning techniques for ontology similarity measuring and ontology mapping

Abstract: Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

System for ontology-based semantic matching in a relational database system

Abstract: The method for processing data in a relational database wherein ontology data that specifies terms and relationships between pairs of said terms expressed in an OWL document is stored in the database, database queries that include a semantic matching operator are formed which identify the ontology data and further specify a stated relationship between two input terms, and the query is executed to invoke the semantic matching operator to determine if the two input terms are related by the stated relationship by consulting said ontology data.

Formalizing Ontology Alignment and its Operations with Category Theory

Abstract: An ontology alignment is the expression of relations between different ontologies. In order to view alignments independently from the language expressing ontologies and from the techniques used for finding the alignments, we use a category-theoretical model in which ontologies are the objects. We introduce a categorical structure, called V-alignment, made of a pair of morphisms with a common domain having the ontologies as codomain. This structure serves to design an algebra that describes formally what are ontology merging, alignment composition, union and intersection using categorical constructions. This enables combining alignments of various provenance. Although the desirable properties of this algebra make such abstract manipulation of V-alignments very simple, it is practically not well fitted for expressing complex alignments: expressing subsumption between entities of two different ontologies demands the definition of non-standard categories of ontologies. We consider two approaches to solve this problem. The first one extends the notion of V-alignments to a more complex structure called W-alignments: a formalization of alignments relying on “bridge axioms.” The second one relies on an elaborate concrete category of ontologies that offers high expressive power. We show that these two extensions have different advantages that may be exploited in different contexts (viz., merging, composing, joining or meeting): the first one efficiently processes ontology merging thanks to the possible use of categorical institution theory, while the second one benefits from the simplicity of the algebra of V-alignments.

Flexible Semantic-Based Service Matchmaking and Discovery

Abstract: Automated techniques and tools are required to effectively locate services that fulfill a given user request in a mobility context. To this purpose, the use of semantic descriptions of services has been widely motivated and recommended for automated service discovery under highly dynamic and context-dependent requirements. Our aim in this work is to propose an ontology-based hybrid approach where different kinds of matchmaking strategies are combined together to provide an adaptive, flexible and efficient service discovery environment. The approach, in particular, exploits the semantic knowledge about the business domain provided by a domain ontology underlying service descriptions, and the semantic organization of services in a service ontology, at different levels of abstraction.