Show simple item record

dc.contributor.authorMa, Denghao
dc.contributor.authorChen, Yueguo
dc.contributor.authorChen, Jun
dc.contributor.authorDu, Xiaoyong
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2019-02-24T08:06:16Z
dc.date.available2019-02-24T08:06:16Z
dc.date.issued2018-01-11
dc.identifier.citationMa D, Chen Y, Chen J, Du X, Zhang X (2017) ESearch. Proceedings of the 26th International Conference on World Wide Web Companion - WWW ’17 Companion. Available: http://dx.doi.org/10.1145/3041021.3054720.
dc.identifier.doi10.1145/3041021.3054720
dc.identifier.urihttp://hdl.handle.net/10754/631132
dc.description.abstractThe paper introduces an open domain entity search system called ESearch, which aims at finding a list of relevant entities to an open domain entity search query (a natural language question). The system is built on top of a Wikipedia text corpus, as well as the structured DBPedia knowledge base. Entities are initially ranked by a model which effectively associates context matching (based on the contexts of entities in the unstructured text corpus) and category matching (based on the types of entities in the structured knowledge base). They are ranked further by a re-ranking component supported by blind feedback or user feedback on entities. We show that category matching is critical for the search performance and the re-ranking component can boost the performance largely. Category matching therefore needs some query entity types (especially specific entity types) as input. However, it is often hard for systems to detect specific entity types because users may not be familiar with how the types of desired entities are defined in the structured knowledge base. In ESearch, we design an effective ranking model of entity types to facilitate blind feedback and user feedback on desired entity types for category matching, so that users can effectively perform entity search without the need of explicitly providing any query entity types as inputs.
dc.description.sponsorshipThis work is supported by the National Science Foundation of China under grant (No. 61472426 and 61432006), 863 key project under grant No. 2015AA015307, the open research program of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Science (No. CARCH201510), and the ECNU-RUC-InfoSys Joint Data Science Lab.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3041021.3054720
dc.rights2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEntity search
dc.subjectInformation retrieval
dc.subjectType ranking
dc.titleESearch: Incorporating Text Corpus and Structured Knowledge for Open Domain Entity Search
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalProceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion
dc.conference.date2017-04-03 to 2017-04-07
dc.conference.name26th International World Wide Web Conference, WWW 2017 Companion
dc.conference.locationPerth, WA, AUS
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSchool of Information, Renmin University of China, , China
dc.contributor.institutionDEKE Key lab (MOE), Renmin University of China, , China
kaust.personZhang, Xiangliang
refterms.dateFOA2019-02-24T08:09:42Z
dc.date.published-online2018-01-11
dc.date.published-print2017


Files in this item

Thumbnail
Name:
p253-ma.pdf
Size:
1.065Mb
Format:
PDF
Description:
Published version

This item appears in the following Collection(s)

Show simple item record

2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
Except where otherwise noted, this item's license is described as 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.