Semantic matching

2013

Semantic Web Information Management, 2009

We view matching as an operation that takes two graph-like structures (e.g., lightweight ontologies) and produces an alignment between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover an alignment by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the entities and the structures of ontologies. In this chapter we first overview the state of the art in the ontology matching field. Then, we present basic and optimized algorithms for semantic matching as well as their implementation within the S-Match system. Finally, we evaluate S-Match against state of the art systems, thereby justifying empirically the strength of the approach.

… of the 7th Conference on 7th …, 2007

Lecture Notes in Computer Science, 2004

We think of Match as an operator which takes two graph-like structures and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.

Lecture Notes in Computer Science, 2008

Matching of concepts describing the meaning of data in heterogeneous distributed information sources, such as database schemas and other metadata models, grouped here under the heading of an ontology, is one of the basic operations of semantic heterogeneity reconciliation. The aim of this chapter is to motivate the need for ontology matching, introduce the basics of ontology matching, and then discuss several promising themes in the area as reflected in recent research works. In particular, we focus on such themes as uncertainty in ontology matching, matching ensembles, and matcher self-tuning. Finally, we outline some important directions for future research.

Ontologies are nowadays used in many domains such as Semantic Web, information systems… to represent meaning of data and data sources. In the framework of knowledge management in an heterogeneous organization, the materialization of the organizational memory in a "corporate semantic web" may require to integrate the various ontologies of the different groups of this organization. To be able to build a corporate semantic web in an heterogeneous, multi-communities organization, it is essential to have methods for comparing, aligning, integrating or mapping different ontologies. This paper proposes a new algorithm for matching two ontologies based on all the information available about the given ontologies (e.g. their concepts, relations, information about the structure of each hierarchy of concepts, or of relations), applying TF/IDF scheme (a method widely used in the information retrieval community) and integrating WordNet (an electronic lexical database) in the process of ontology matching.

… Workshop on Ontology Matching ( …

Abstract. Methodologies to find and evaluate solutions for ontology matching should be centered on the practical problems to be solved. In this paper we look at matching from the perspective of a practitioner in search of matching techniques or tools. We survey actual matching use ...

2007

Schema matching is the process of developing semantic matches between two or more schemas. The purpose of schema matching is generally either to merge two or more databases, or to enable queries on multiple, heterogeneous databases to be formulated on a single schema (Doan and Halevy 2005). This paper develops a taxonomy of schema matching approaches, classifying them as being based on a combination schema matching technique and the type of data used by those techniques. Schema matching techniques are categorized as being based on rules, learning, or ontology, and the type of data used is categorized as being based on schema elements or instance data. This taxonomy is an extension to previous work, and significant current research efforts are categorized using this taxonomy. Several of these research efforts are profiled and their categorization in the taxonomy is explored. The current research is used to identify the directions in which future research is headed.

Adoption of semantic models, based on ontologies, is nowadays widely recognized to be particularly useful for integrating multiple data sources. It means that more and more net applications are challenged to real time matching problems. As Current algorithms for matching are time consuming and do not provide a model for storing and reuse found matchings, their adoption in such use cases is difficult. In this paper, we propose a new Semantic Data Model which is able to provide an extensive machine interpretable underground structure for storing and quickly recognize alignments between a set of input sources. As a result we are able to improve application knowledge matching performances and to provide a reusable global concept view for a given domain.

VLDB Journal

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, in previous research many techniques have been proposed to achieve a partial automation of the Match operation for specific application domains. We present a taxonomy that covers many of the existing approaches, and we describe these approaches in some detail. In particular, we distinguish between schema-and instance-level, element-and structure-level, and language-and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.