Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2012

Abstract

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.

Keywords

Entity synonym, fuzzy matching, structured data, web query, query log

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

24

Issue

10

First Page

1862

Last Page

1875

ISSN

1041-4347

Identifier

10.1109/TKDE.2011.168

Publisher

IEEE

Additional URL

https://doi.org/10.1109/TKDE.2011.168

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