Ontology matching plays an important role in the integration of heterogeneous data sources that are described by ontologies. In order to find correspondences between entities of different ontologies, a matching system has to be built. CroMatcher is an ontology matching system that consists of several string and structural basic matchers. As individual basic matcher computes similarity between e...

This paper presents the results obtained by LPHOM (Linear Program for Holistic Ontology Matching) system in the OAEI 2016 campaign. This is the first participation of our system in the OAEI campaigns. It has participated in four tracks (Benchmark, Anatomy, Conference, and Multifarm). We report here a general discussion on the results and on the future improvements. 1 Presentation of the system ...

QODI is an ontology-based data integration system (OBDI) comprising both ontology mapping and query reformulation methods. QODI is distinguished in that the ontology mapping algorithm dynamically determines a partial mapping specific to the reformulation of each query. The mapping algorithm decomposes a query into a set of paths, and compares the sets of paths with a similar decomposition of a ...

This chapter studies ontology matching : the problem of finding the semantic mappings between two given ontologies. This problem lies at the heart of numerous information processing applications. Virtually any application that involves multiple ontologies must establish semantic mappings among them, to ensure interoperability. Examples of such applications arise in myriad domains, including e-c...

In this paper, we describe an approach to schema and data integration for meta-search engines. The integration of heterogeneous, distributed information from the Web is a complicated task, especially the task of schema/data matching and integration. During the matching and integration process, we need to handle syntactic, semantic and structural heterogeneity between multiple information source...

Ontology matching produces mapping relations between elements of two ontologies and it is a basic problem in geographical information integration. Currently, most existing studies rely on all kinds of semantic similarity between the semantic entities to measure ontology mapping relations. However, these measures are not sufficient due to only detecting equivalence relation of compared ontologie...

The problem of (semi-)automatically computing mappings between independently developed ontologies is usually referred to as the ontology matching problem. A number of sophisticated ontology matching systems have been developed in the last years [7, 26]. Ontology matching systems, however, rely on lexical and structural heuristics and the integration of the input ontologies and the mappings may ...

In this paper we propose an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), i.e. metamodel-assisted compact MOEA/D, to efficiently solve the ontology meta-matching problem. Particularly, we dedicate to determine the optimal ontology alignment by tuning four different basic similarity measures, i.e. Syntactic Measure, Linguistic Measure, Taxonomy-based Measure an...

Relating ontologies is very important for many ontology-based applications and more important in open environments like the semantic web. The relations between ontology entities can be obtained by ontology matching and represented as alignments. Hence, alignments must be taken into account in ontology management. This chapter establishes the requirements for alignment management. After a brief ...

The information retrieval systems that are present nowadays are mainly based on full text matching of keywords or topic based classification. This matching of keywords often returns a large number of irrelevant information and this does not meet the user’s query requirement. In order to solve this problem and to enhance the search using semantic environment, a technique named ontology is implem...