French) (original) (raw)

Association for Computational Linguistics

Encyclopedia of Language & Linguistics, 2006

This article describes a spatial model for matching semantic values between two languages, French and English. Based on semantic similarity links, the model constructs a map that represents a word in the source language. Then the algorithm projects the map values onto a space in the target language. The new space abides by the semantic similarity links specific to the second language. Then the two maps are projected onto the same plane in order to detect overlapping values. For instructional purposes, the different steps are presented here using a few examples. The entire set of results is available at the following address: http://dico.isc.cnrs.fr.

Semantic Matching: Algorithms and Implementation

Lecture Notes in Computer Science, 2007

We view match as an operator that takes two graph-like structures (e.g., classifications, XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover mappings 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 elements and the structures of schemas. In this paper we present basic and optimized algorithms for semantic matching, and we discuss their implementation within the S-Match system. We evaluate S-Match against three state of the art matching systems, thereby justifying empirically the strength of our approach. * This article is an expanded and updated version of an earlier conference paper . 1 See www.OntologyMatching.org for a complete information on the topic.

Transactions in …, 2008

With the increasing amount of geographic information available on the Internet, searching, browsing, and organizing such information has become a major challenge within the field of Geographic Information Science (GIScience). As all information is ultimately for and from human beings, the methodologies applied to retrieve and organize this information should correlate with human similarity judgments. Semantic similarity measurement, which originated in psychology, is a methodology fulfilling this requirement and supporting geographic information retrieval.

S-Match: an Algorithm and an Implementation of Semantic Matching

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.

2007

Semantic relatedness refers to the degree to which two concepts or words are related. Humans are able to easily judge if a pair of words are related in some way. For example, most people would agree that apple and orange are more related than are apple and toothbrush. Semantic similarity is a subset of semantic relatedness. In this article we describe several methods for computing the similarity of two words, following two directions: dictionary-based methods that use WordNet, Roget's thesaurus, or other resources; and corpus-based methods that use frequencies of co-occurrence in corpora (cosine method, latent semantic indexing, mutual information, etc). Then, we present results for several applications of word similarity knowledge: solving TOEFL-style synonym questions, detecting words that do not fit into their context in order to detect speech recognition errors, and synonym choice in context, for writing aid tools. We also present a method for computing the similarity of two short texts, based on the similarities of their words. Applications of text similarity knowledge include: designing exercises for second language-learning, acquisition of domain-specific corpora, information retrieval, and text categorization. Before concluding, we briefly describe cross-language extensions of the methods for similarity of words and texts.

Semantic Matching with S-Match

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.

2007

Communication problems between humans and machines are often the reason for failures or wrong computations. While machines use well-defined languages and rules in formal models to compute information, humans prefer natural language expressions with only vaguely specified semantics. Similarity comparisons are a central construct of the human way of thinking. For instance, humans are able to act sensible in completely new situations by comparing them to similar experiences in the past. Similarity is used for reasoning on unknown information. It is necessary to overcome the differences in representing and processing information to avoid error-prone communication. A machine being able to understand natural language and detect the semantic similarity between expressions would be the key to eliminate human-machine communication problems. This paper addresses human-machine communication about spatial configurations in natural language. We propose a computational model to capture the semant...

Matching multi-lingual subject vocabularies

… Technology for Digital …, 2009

Most libraries and other cultural heritage institutions use controlled knowledge organisation systems, such as thesauri, to describe their collections. Unfortunately, as most of these institutions use different such systems, unified access to heterogeneous collections is difficult. Things are even worse in an international context when concepts have labels in different languages. In order to overcome the multilingual interoperability problem between European Libraries, extensive work has been done to manually map concepts from different knowledge organisation systems, which is a tedious and expensive process.

Cross Lingual Ontology Matching Based on Fuzzy Syntactic Matching

International Journal of Advanced Research in Computer Science

Ontologies bolsters data disclosure, sharing and reuse among people and enable semantic interoperability between PC based structures. To develop correspondences between data thoughts addressed in Ontologies. Once in a while the correspondence between the client and PC is in various language, which is extremely hard to comprehend for both. Ontology matching is at the center of overseeing Cross Lingual on the semantic web. In this paper, we present a way to deal with take care of the issue of multilingualism on the semantic web, in view of Syntactic matching. To determine linguistic issue, two Ontologies (one in English and one in Hindi) of same space, interior portrayal, number of matching algorithm dependent on Syntactic method (Edit distance (Levenshteindistance LD)), and Machine Translator.

Towards Building Lexical Ontology via Cross-Language Matching

Proceedings of the 7th Conference on Global WordNet. Global WordNet Association., 2014

In this paper, we introduce a methodology for mapping linguistic ontologies lexicalized across different languages. We present a classification-based semantics for mappings of lexicalized concepts across different languages. We propose an experiment for validating the proposed cross-language mapping semantics, and discuss its role in creating a gold standard that can be used in assessing cross-language matching systems.

A Model for Matching Semantic Maps between Languages (French/English, English/French) (original) (raw)

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