Ontology matching is a crucial issue in semantic web and data interoperability. In this paper, we describe a core word based method for measuring similarity from the semantic level of ontology entities. In ontology, most of labels of entities are compound words rather than single meaningful words. However, the main meaning usually is represented by one word, which is called core word. The core ...

Schema/ontology matching is a critical problem in many application domains, such as, semantic web, schema/ontology integration, data warehouses, e-commerce, catalog matching, etc. Many diverse solutions to the matching problem have been proposed so far. In this paper we present a taxonomy of schema-based matching techniques that builds on the previous work on classifying schema matching approac...

This paper presents the alignment results of Lily on SEALS platform for the ontology alignment contest OAEI 2011. Lily is an ontology matching system. In OAEI 2011, Lily submited the results for three matching tasks on the SEALS platform: benchmark, anatomy, conference. The specific techniques used by Lily are introduced. The matching results of Lily are also discussed.

Recently, the number of ontology matching techniques and systems has increased significantly. This makes the issue of their evaluation and comparison more severe. One of the challenges of the ontology matching evaluation is in building large scale evaluation datasets. In fact, the number of possible correspondences between two ontologies grows quadratically with respect to the numbers of entiti...

Ontology matching produces correspondences between entities of two ontologies. The OMReasoner is unique in that it creates an extensible framework for combination of multiple individual matchers, and reasons about ontology matching by using description logic reasoner. It handles ontology matching in semantic level and makes full use of the semantic part of OWL-DL instead of structure. This pape...

We present the OAEI 2012 evaluation results for the matching system GOMMA developed at the University of Leipzig. The original application focus of GOMMA has been the life science domain but as a generic tool it can also match ontologies from other areas. It could thus participate in all OAEI tracks running on the SEALS platform. GOMMA supports several methods for efficiently matching large ont...

A digital library is a type of information retrieval (IR) system. The existing methodologies generally have problems on keyword-searching. We proposed model to solve the problem by using concept-based approach (ontology) and metadata case base. This consists identifying domain concepts in user’s query applying expansion them. system aims at contributing an improved relevance results retrieved f...

Due to the high heterogeneity of ontologies, a combination of many methods is necessary to discover correctly the semantic correspondences between their elements. An ontology matching system can be seen as a collection of several matching components, which implements a specific method dealing with a specific heterogeneity type like the terminological, structural and semantic matchers. In this p...

Ontology Matching is a task needed in various applications, for example for comparison or merging purposes. In literature, many algorithms solving the matching problem can be found, but most of them do not consider instances at all. Mappings are determined by calculating the string-similarity of labels, by recognizing linguistic word relations (synonyms, subsumptions etc.) or by analyzing the (...