Document (#32590)

Author

Niemi, T.
Jämsen , J.

Title

¬A query language for discovering semantic associations, part I : approach and formal definition of query primitives

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.11, S.1559-1568

Year

2007

Abstract

In contemporary query languages, the user is responsible for navigation among semantically related data. Because of the huge amount of data and the complex structural relationships among data in modern applications, it is unrealistic to suppose that the user could know completely the content and structure of the available information. There are several query languages whose purpose is to facilitate navigation in unknown structures of databases. However, the background assumption of these languages is that the user knows how data are related to each other semantically in the structure at hand. So far only little attention has been paid to how unknown semantic associations among available data can be discovered. We address this problem in this article. A semantic association between two entities can be constructed if a sequence of relationships expressed explicitly in a database can be found that connects these entities to each other. This sequence may contain several other entities through which the original entities are connected to each other indirectly. We introduce an expressive and declarative query language for discovering semantic associations. Our query language is able, for example, to discover semantic associations between entities for which only some of the characteristics are known. Further, it integrates the manipulation of semantic associations with the manipulation of documents that may contain information on entities in semantic associations.

Content

Part II: Journal of the American Society for Information Science and Technology. 58(2007) no.11, S.1686-1700.

Theme

Computerlinguistik
Semantisches Umfeld in Indexierung u. Retrieval

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4. Han, B.; Chen, L.; Tian, X.: Knowledge based collection selection for distributed information retrieval (2018) 0.22

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5. Ning, X.; Jin, H.; Wu, H.: RSS: a framework enabling ranked search on the semantic web (2008) 0.22

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