Lexical Knowledge Extraction: an Effective Approach to Schema and Ontology Matching

This paper’s aim is to examine what role Lexical Knowledge Extraction plays in data integration as well as ontology engineering.Data integration is the problem of combining data residing at distributed heterogeneous sources, and providing the user with a unified view of these data; a common and important scenario in data integration are structured or semi-structure data sources described by a schema.Ontology engineering is a subfield of knowledge engineering that studies the methodologies for building and maintaining ontologies. Ontology engineering offers a direction towards solving the interoperability problems brought about by semantic obstacles, such as the obstacles related to the definitions of business terms and software classes. In these contexts where users are confronted with heterogeneous information it is crucial the support of matching techniques. Matching techniques aim at finding correspondences between semantically related entities of different schemata/ontologies.Several matching techniques have been proposed in the literature based on different approaches, often derived from other fields, such as text similarity, graph comparison and machine learning.This paper proposes a matching technique based on Lexical Knowledge Extraction: first, an Automatic Lexical Annotation of schemata/ontologies is performed, then lexical relationships are extracted based on such annotations.Lexical Annotation is a piece of information added in a document (book, online record, video, or other data), that refers to a semantic resource such as WordNet. Each annotation has the property to own one or more lexical descriptions. Lexical annotation is performed by the Probabilistic Word Sense Disambiguation (PWSD) method that combines several disambiguation algorithms.Our hypothesis is that performing lexical annotation of elements (e.g. classes and properties/attributes) of schemata/ontologies makes the system able to automatically extract the lexical knowledge that is implicit in a schema/ontology and then to derive lexical relationships between the elements of a schema/ontology or among elements of different schemata/ontologies.The effectiveness of the method presented in this paper has been proven within the data integration system MOMIS

Discovering Semantically Similar Associations (SeSA) for Complex Mappings between Conceptual Models

Abstract. There is an increasing demand for discovering meaningful relation-ships, i.e., mappings, between conceptual models for interoperability. Current solutions have been focusing on the discovery of correspondences between el-ements in different conceptual models. However, a complex mapping associating a structure connecting a set of elements in one conceptual model with a structure connecting a set of elements in another conceptual model is required in many cases. In this paper, we propose a novel technique for discovering semantically similar associations (SeSA) for constructing complex mappings. Given a pair of conceptual models, we create a mapping graph by taking the cross product of the two conceptual model graphs. Each edge in the mapping graph is assigned a weight based on the semantic similarity of the two elements encoded by the edge. We then turn the problem of discovering semantically similar associations (SeSA) into the problem of finding shortest paths in the mapping graph. We ex-periment different combinations of values for element similarities according to the semantic types of the elements. By choosing the set of values that have the best performance on controlled mapping cases, we apply the algorithm on test conceptual models drawn from a variety of applications. The experimental re-sults show that the proposed technique is effective in discovering semantically similar associations (SeSA).

A gauss function based approach for unbalanced ontology matching

Ontology matching, aiming to obtain semantic correspon-dences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the appli-cations cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community. In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight on-tology which represents the local domain knowledge, we ex-tract a“similar ” sub-ontology from the corresponding heavy-weight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly cal-culate the influence values between concepts. In addition, we perform an extensive experiment to verify the effective-ness and efficiency of our proposed approach by using OAEI 2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time

Analysis of knowledge transformation and merging techniques and implementations

scharffe2007bDealing with heterogeneity requires finding correspondences between ontologies and using these correspondences for performing some action such as merging ontologies, transforming ontologies, translating data, mediating queries and reasoning with aligned ontologies. This deliverable considers this problem through the introduction of an alignment life cycle which also identifies the need for manipulating, storing and sharing the alignments before processing them. In particular, we also consider support for run time and design time alignment processing

A multi-matching technique for combining similarity measures in ontology integration

Ontology matching is a challenging problem in many applications, and is a major issue for interoperability in information systems. It aims to find semantic correspondences between a pair of input ontologies, which remains a labor intensive and expensive task. This thesis investigates the problem of ontology matching in both theoretical and practical aspects and proposes a solution methodology, called multi-matching . The methodology is validated using standard benchmark data and its performance is compared with available matching tools. The proposed methodology provides a framework for users to apply different individual matching techniques. It then proceeds with searching and combining the match results to provide a desired match result in reasonable time. In addition to existing applications for ontology matching such as ontology engineering, ontology integration, and exploiting the semantic web, the thesis proposes a new approach for ontology integration as a backbone application for the proposed matching techniques. In terms of theoretical contributions, we introduce new search strategies and propose a structure similarity measure to match structures of ontologies. In terms of practical contribution, we developed a research prototype, called MLMAR - Multi-Level Matching Algorithm with Recommendation analysis technique, which implements the proposed multi-level matching technique, and applies heuristics as optimization techniques. Experimental results show practical merits and usefulness of MLMA

Discovering the Semantics of Relational Tables Through Mappings

Abstract. Many problems in Information and Data Management require a semantic account of a database schema. At its best, such an account consists of formulas expressing the relationship (“mapping”) between the schema and a formal conceptual model or ontology (CM) of the domain. In this paper we describe the underlying principles, algorithms, and a prototype tool that finds such semantic mappings from relational tables to ontologies, when given as input simple correspondences from columns of the tables to datatype properties of classes in an ontology. Although the algorithm presented is necessarily heuristic, we offer formal results showing that the answers returned by the tool are “correct ” for relational schemas designed according to standard Entity-Relationship techniques. To evaluate its usefulness and effectiveness, we have applied the tool to a number of public domain schemas and ontologies. Our experience shows that significant effort is saved when using it to build semantic mappings from relational tables to ontologies