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.

Inside Theory-Aware and Standards-Compliant Authoring System

Abstract: In their paper [14], Bourdeau and Mizoguchi foresaw a framework for ontology-based intelligent systems. Although it took longer years than their expectation, the ontology they have been developing is now released for evaluation with the help of the second author. Ontology building is a labor-intensive process and it is rarely perfect. Our enterprise is not an exception. The current ontology is still very preliminary because it has been completely reconstructed from the existing one with a few new ideas. So, we hope the readers be generous when they read the ontology. The ontology presented here is not a light-weight ontology but a heavyweight ontology. It is built based on philosophical consideration of all the concepts necessary for understanding learning, instruction and instructional design. Although it is full of axioms, the Hozo GUI which is based on a frame structure makes it easier to read it. However, the readers are expected to have basic knowledge of ontology and preferably be aware of the theory of role and of the Hozo way of role representation. Papers [6][7] would be helpful to grasp what we are doing with this ontology. The prototype system named SMARTIES is a totally ontology-aware system which fully utilizes the merits of ontology computationally as well as conceptually. It is so preliminary that it cannot be open to public, though you can get a rough idea of what it is from the papers.

View integration of behavior in object-oriented databases

Abstract: Database schemas are often not defined by a single person but by several future users of the database who define their possibly different views on the proposed database schema. These views are collected and integrated during the subsequent design step of view integration . In object-oriented databases, view integration must handle the integration of the structure and the behavior of objects. Whereas integration of object structure has been treated in the realm of semantic data models in the past, integration of object behavior has received little attention so far. Behavior is usually defined at two levels of detail: by activities, which correspond to methods in object-oriented languages, and by object life-cycles, which represent the overall behavior of objects during their life time. This paper presents an approach to integrate object life-cycles in object-oriented database schemas using a notation based on Petri nets. We will introduce different methods of integration depending on identified semantic correspondences between the object types in the views.

A Harmony based Adaptive Ontology Mapping Approach

Abstract: Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. Ontology mapping is critical to achieve semantic interoperability in the WWW. Nowadays most ontology mapping approaches integrate multiple individual matchers to explore both linguistic and structure similarity of different ontologies. Thus how to effectively aggregating different similarities is pervasive in ontology mapping. In current aggregation methods, people either have to manually set parameters in aggregation function or need "ground truth" in advance for machine learning based parameter optimization. Both of them have limitation. In this paper, we propose a measure harmony, which is the normalized number of mapping pair that suggests an unambiguous one-to-one mapping, and a harmony based adaptive ontology mapping approach, which can automatically adjust parameters of three kinds of similarities (i.e., edit distance based similarity, profile similarity and structure similarity) in aggregation functions according to different mapping tasks without given any ground truth. Experimental results show the harmony is indeed a good estimator of the performance (i.e., f-measure) of different similarities, and the harmony based adaptive aggregation method outperforms all other existing aggregation methods on OAEI benchmark tests.