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.

Semi-automatic Ontology Construction based on Patterns

Abstract: This thesis aims to improve the ontology engineering process, by providing better semiautomatic support for constructing ontologies and introducing knowledge reuse through ontology patterns. The thesis introduces a typology of patterns, a general framework of pattern-based semi-automatic ontology construction called OntoCase, and provides a set of methods to solve some specific tasks within this framework. Experimental results indicate some benefits and drawbacks of both ontology patterns, in general, and semi-automatic ontology engineering using patterns, the OntoCase framework, in particular. The general setting of this thesis is the field of information logistics, which focuses on how to provide the right information at the right moment in time to the right person or organisation, sent through the right medium. The thesis focuses on constructing enterprise ontologies to be used for structuring and retrieving information related to a certain enterprise. This means that the ontologies are quite 'light weight' in terms of logical complexity and expressiveness. Applying ontology content design patterns within semi-automatic ontology construction, i.e. ontology learning, is a novel approach. The main contributions of this thesis are a typology of patterns together with a pattern catalogue, an overall framework for semi-automatic patternbased ontology construction, specific methods for solving partial problems within this framework, and evaluation results showing the characteristics of ontologies constructed semiautomatically based on patterns. Results show that it is possible to improve the results of typical existing ontology learning methods by selecting and reusing patterns. OntoCase is able to introduce a general top-structure to the ontologies, and by exploiting background knowledge, the ontology is given a richer structure than when patterns are not applied.

Ontology Based Semantic Search in Holy Quran

Abstract: Holy Quran, due to its unique style and allegorical nature, needs special attention about search and information retrieval issues. Many works have been done to accomplish keyword search from Holy Quran. The main problem in all these works is that these are either static or they does not provide us semantic search. In this paper, we propose that the concepts of ontology of semantic web can be applied for carrying out semantic search in Holy Quran. For this purpose, exploratory search have been done from semantic web field of knowledge. The sample domain ontology, based on living creatures including animals and birds mentioned in Holy Quran, has been developed in protégé ontology editor tool. SPARQL Queries have been run to depict the proper role of ontology. Then certain recommendation for the project of attaining semantic search from all domains and resultantly all text of Holy Quran has been proposed. These recommendations include model and framework including creation of Quranic WordNet, integration, merging and mapping of domain ontologies under the umbrella of upper ontology. This work can be extended to other Islamic knowledge sources like Hadith, Fiqh etc.

A Framework for Ontology Learning and Data-driven Change Discovery

Abstract: In this paper we present Text2Onto, a framework for ontology learning from textual resources. Three main features distinguish Text2Onto from our earlier framework TextToOnto as well as other state-of-the-art ontology learning frameworks. First, by representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic Ontology Model (POM), we remain independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text2Onto and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations of the POM. Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from scratch each time it changes, only selectively updating the POM according to the corpus changes instead. Besides increasing efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.

Ontology Alignment—A Survey with Focus on Visually Supported Semi-Automatic Techniques

Abstract: Semantic technologies are of paramount importance to the future Internet. The reuse and integration of semantically described resources, such as data or services, necessitates the bringing of ontologies into mutual agreement. Ontology alignment deals with the discovery of correspondences between concepts and relations from different ontologies. Alignment provides the key ingredient to semantic interoperability. This paper gives an overview on the state of the art in the field of visually supported semi-automatic alignment techniques and presents recent trends and developments. Particular attention is given to user interfaces and visualization techniques supporting involvement of humans in the alignment process. We derive and summarize requirements for visual semi-automatic alignment systems, provide an overview of existing approaches, and discuss the possibilities for further improvements and future research.

A methodology for engineering ontology acquisition and validation

Abstract: When engineering content is created and applied during the product life cycle, it is often stored and forgotten. Current information retrieval approaches based on statistical methods and keyword matching are not effective in understanding the context of engineering content. They are not designed to be directly applicable to the engineering domain. Therefore, engineers have very limited means to harness and reuse past designs. The overall objective of our research is to develop an engineering ontology (EO)-based computational framework to structure unstructured engineering documents and achieve more effective information retrieval. This paper focuses on the method and process to acquire and validate the EO. The main contributions include a new, systematic, and more structured ontology development method assisted by a semiautomatic acquisition tool. This tool is integrated with Protege ontology editing environment; an engineering lexicon (EL) that represents the associated lexical knowledge of the EO to bridge the gap between the concept space of the ontology and the word space of engineering documents and queries; the first large-scale EO and EL acquired from established knowledge resources for engineering information retrieval; and a comprehensive validation strategy and its implementations to justify the quality of the acquired EO. A search system based on the EO and EL has been developed and tested. The retrieval performance test further justifies the effectiveness of the EO and EL as well as the ontology development method.