Lexical reference: a semantic matching subtask (original) (raw)
Related papers
Proceedings of the workshop on Human Language Technology - HLT '93, 1993
A semantic concordance is a textual corpus and a lexicon So combined that every substantive word in the text is linked to its appropriate ~nse in the lexicon. Thus it can be viewed either as a corpus in which words have been tagged syntactically and semantically, or as a lexicon in which example sentences can be found for many definitions. A semantic concordance is being constructed to use in studies of sense resolution in context (semantic disambiguation). The Brown Corpus is the text and WordNet is the lexicon. Semantic tags (pointers to WordNet synsets) are inserted in the text manually using an interface, ConText, that was designed to facilitate the task. Another interface supports searches of the tagged text. Some practical uses for semantic concordances are proposed.
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.
The Role and Resolution of Textual Entailment in Natural Language Processing Applications
Lecture Notes in Computer Science, 2006
A fundamental phenomenon in Natural Language Processing concerns the semantic variability of expressions. Identifying that two texts express the same meaning with different words is a challenging problem. We discuss the role of entailment for various Natural Language Processing applications and develop a machine learning system for their resolution. In our system, text similarity is based on the number of consecutive and non-consecutive word overlaps between two texts. The system is language and resource independent, as it does not use external knowledge resources such as WordNet, thesaurus, semantic, syntactic or part-of-speech tagging tools. In this paper all tests were done for English, but our system can be used with no restrains by other languages.
Automatic Lexical Annotation Applied to the SCARLET Ontology Matcher
2010
This paper proposes lexical annotation as an effective method to solve the ambiguity problems that affect ontology matchers. Lexical annotation associates to each ontology element a set of meanings belonging to a semantic resource. Performing lexical annotation on the ontologies involved in the matching process allows to detect false positive mappings and to enrich matching results by adding new mappings (i.e. lexical relationships between elements on the basis of the semantic relationships holding among meanings). The paper will go through the explanation of how to apply lexical annotation on the results obtained by a matcher. In particular, the paper shows an application on the SCARLET matcher. We adopt an experimental approach on two test cases, where SCARLET was previously tested, to investigate the potential of lexical annotation. Experiments yielded promising results, showing that lexical annotation improves the precision of the matcher.
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.
Towards explaining semantic matching
2004
Interoperability among systems using different term vocabularies requires some mapping between terms in the vocabularies. Matching applications generate such mappings. When the matching process utilizes term meaning (instead of simply relying on syntax), we refer to the process as semantic matching. If users are to use the results of matching applications, they need information about the mappings.
Semantic integration in text: From ambiguous names to identifiable entities
2005
Abstract Semantic integration focuses on discovering, representing, and manipulating correspondences between entities in disparate data sources. The topic has been widely studied in the context of structured data, with problems being considered including ontology and schema matching, matching relational tuples, and reconciling inconsistent data values. In recent years, however, semantic integration over text has also received increasing attention.
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.