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.

SOAT: a semi-automatic domain ontology acquisition tool from Chinese corpus

Abstract: In this paper, we focus on the domain ontology acquisition from Chinese corpus by extracting rules designed for Chinese phrases. These rules are noun sequences with part-of-speech tags. Experiments show that this process can construct domain ontology prototypes efficiently and effectively.

Smart Data Integration by Goal Driven Ontology Learning

Abstract: The smart data integration approach is proposed to compose data and knowledge of the different nature, origin, formats and standards. This approach is based on the selective goal driven ontology learning. The automated planning paradigm in a combination with a value of the perfect information approach is proposed to be used for evaluating the knowledge correspondence with the learning goal for the data integration domain. The information model of a document is represented as a supplement to the Partially Observable Markov Decision Process (POMDP) strategy of a domain. It helps to estimate the document a pertinence as the increment of the strategy expected utility. A statistical method for identifying the semantic relations in the natural language texts for their linguistic characteristics is developed. It helps to extract the Ontology Web Language (OWL) predicates from the natural language text using data about sub semantic links. A set of methods and means based on ontology learning was developed to support the smart data integration process. A technology uses the Natural Language Processing software Link Grammar Parser, WordNet Application Programming Interface (API) as well as the OWL API.

Ontology Driven Data Integration in Heterogeneous Networks

Abstract: We propose a layered framework for the integration of syntactically, schematically, and semantically heterogeneous networked data sources Their heterogeneity stems from different models (eg, relational, XML, or RDF), different schemas within the same model, and different terms associated with the same meaning We use a semantic based approach that uses a global ontology to mediate among the schemas of the data sources In our framework, a query is expressed in terms of one of the data sources or of the global ontology and is then translated into subqueries on the other data sources using mappings based on a common vocabulary Metadata representation, global conceptualization, declarative mediation, mapping support, and query processing are addressed in detail in our discussion of a case study

Exploring ontologies for semantic interoperability of data in emergency response

Abstract: Emergency response is a complex activity involving many actors and heterogeneous spatial data. Two of the major challenges are the integration and extraction of these data and their transmission to emergency management actors. Although significant progress has been made regarding the systemic and syntactic heterogeneity of data in this context, semantic heterogeneity remains insufficiently addressed. Here, we discuss the possibility of applying the ontology to resolve semantic heterogeneity in emergency response. We propose a concept for a solution to the semantic interoperability problem in emergency management using an ontology by presenting a case study.

A cooperative approach for composite ontology mapping

Abstract: This paper proposes a cooperative approach for composite ontology mapping. We first present an extended classification of automated ontology matching and propose an automatic composite solution for the matching problem based on cooperation. In our proposal, agents apply individual mapping algorithms and cooperate in order to change their individual results. We assume that the approaches are complementary to each other and their combination produces better results than the individual ones. Next, we compare our model with three state of the art matching systems. The results are promising specially for what concerns precision and recall. Finally, we propose an argumentation formalism as an extension of our initial model. We compare our argumentation model with the matching systems, showing improvements on the results.