Entity Synonyms for Structured Web Search
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 (h...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2012
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/1549 https://ink.library.smu.edu.sg/context/sis_research/article/2548/viewcontent/tkde12b.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-2548 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-25482017-12-26T08:26:29Z Entity Synonyms for Structured Web Search CHENG, Tao LAUW, Hady W. PAPARIZOS, Stelios 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. 2012-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1549 info:doi/10.1109/TKDE.2011.168 https://ink.library.smu.edu.sg/context/sis_research/article/2548/viewcontent/tkde12b.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Entity synonym fuzzy matching structured data web query query log Databases and Information Systems Numerical Analysis and Scientific Computing |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Entity synonym fuzzy matching structured data web query query log Databases and Information Systems Numerical Analysis and Scientific Computing |
spellingShingle |
Entity synonym fuzzy matching structured data web query query log Databases and Information Systems Numerical Analysis and Scientific Computing CHENG, Tao LAUW, Hady W. PAPARIZOS, Stelios Entity Synonyms for Structured Web Search |
description |
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. |
format |
text |
author |
CHENG, Tao LAUW, Hady W. PAPARIZOS, Stelios |
author_facet |
CHENG, Tao LAUW, Hady W. PAPARIZOS, Stelios |
author_sort |
CHENG, Tao |
title |
Entity Synonyms for Structured Web Search |
title_short |
Entity Synonyms for Structured Web Search |
title_full |
Entity Synonyms for Structured Web Search |
title_fullStr |
Entity Synonyms for Structured Web Search |
title_full_unstemmed |
Entity Synonyms for Structured Web Search |
title_sort |
entity synonyms for structured web search |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2012 |
url |
https://ink.library.smu.edu.sg/sis_research/1549 https://ink.library.smu.edu.sg/context/sis_research/article/2548/viewcontent/tkde12b.pdf |
_version_ |
1770571296711639040 |