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.

Product design and manufacturing process based ontology for manufacturing knowledge reuse

Abstract: This paper presents an effective product design and manufacturing process based ontology for manufacturing knowledge reuse. While a number of related efforts exist in the literature, there lacks a granular, interconnected product design and manufacturing process based ontology that can lead to greater industry adoption and knowledge reuse. In particular, the proposed ontology leverages an established industry standard to connect product design and manufacturing process knowledge in a logical and effective manner. The resulting effort is a general ontological framework that can be widely employed by the manufacturing industry. Additionally, the feasibility of implementing the ontology enabled knowledge reuse framework is demonstrated through a real-world case study.

Building a comprehensive ontology to refine video concept detection

Abstract: Recent research has discovered that leveraging ontology is an effective way to facilitate semantic video concept detection. As an explicit knowledge representation, a formal ontology definition usually consists of a lexicon, properties, and relations. In this paper, we present a comprehensive representation scheme for video semantic ontology in which all the three components are well studied. Specifically, we leverage LSCOM to construct the concept lexicon, describe concept property as the weights of different modalities which are obtained manually or by data-driven approach, and model two types of concept relations (i.e., pairwise concept correlation and hierarchical relation). In contrast with most existing ontologies which are only focused on one or two components for domain-specific videos, the proposed ontology is more comprehensive and general. To validate the effectiveness of this ontology, we further apply it to video concept detection. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared to existing key approaches to video concept detection.

Enriching ontologies with multilingual information

Abstract: This paper presents a novel approach to ontology localization with the objective of obtaining multilingual ontologies. Within the ontology development process, ontology localization has been defined as the activity of adapting an ontology to a concrete linguistic and cultural community. Depending on the ontology layers-terminological and/or conceptual-involved in the ontology localization activity, three heterogeneous multilingual ontology metamodels have been identified, of which we propose one of them. Our proposal consists in associating the ontology metamodel to an external model for representing and structuring lexical and terminological data in different natural languages. Our model has been called Linguistic Information Repository (LIR). The main advantages of this modelling modality rely on its flexibility by allowing (1) the enrichment of any ontology element with as much linguistic information as needed by the final application, and (2) the establishment of links among linguistic elements within and across different natural languages. The LIR model has been designed as an ontology of linguistic elements and is currently available in Web Ontology Language (OWL). The set of lexical and terminological data that it provides to ontology elements enables the localization of any ontology to a certain linguistic and cultural universe. The LIR has been evaluated against the multilingual requirements of the Food and Agriculture Organization of the United Nations in the framework of the NeOn project. It has proven to solve multilingual representation problems related to the establishment of well-defined relations among lexicalizations within and across languages, as well as conceptualization mismatches among different languages. Finally, we present an extension to the Ontology Metadata Vocabulary, the so-called LexOMV, with the aim of reporting on multilinguality at the ontology metadata level. By adding this contribution to the LIR model, we account for multilinguality at the three levels of an ontology: data level, knowledge representation level and metadata level.

Technologies for integrating biological data

Abstract: The process of building a new database relevant to some field of study in biomedicine involves transforming, integrating and cleansing multiple data sources, as well as adding new material and annotations. This paper reviews some of the requirements of a general solution to this data integration problem. Several representative technologies and approaches to data integration in biomedicine are surveyed. Then some interesting features that separate the more general data integration technologies from the more specialised ones are highlighted.

A Semi-automatic Method to Ontology Design by Using FCA

Abstract: Ontology design is a complex and time-consuming process. It is extremely difficult for human experts to discover ontology from given data or texts. This paper presents a semi-automatic method for ontology extraction and design. The method is based on Formal Concept Analysis and a Horn clause model of a concept lattice. Inputs to the technique are domain-specific texts or data. After transformations, resulting domain-specific ontology is represented as a set of rules and facts according to Horn clause model of concept lattice based ontology representation. Ontology designer is given this initial ontology expression for further extension by adding concepts and relationships (part-of, related to, etc) by using a rule language based on Horn clauses. Validation of ontology is done by logical inference.