Knowledge discovery from academic search engine

Ye Wang*, Miao Jiang, Xiaoling Wang, Aoying Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The purpose of a search engine is to retrieve information relevant to a user's query from a big textual collection. However, most vertical search engines, such as Google Scholar and Citeseer, only return the flat ranked list without an efficient result exhibition and knowledge arrangement for given users. This paper considers the problem of knowledge discovery in the literature of search computing. We design some search and ranking strategies to mining potential knowledge from returned search results. A vertical search engine prototype, called Dolphin, is implemented where users are not only getting the results from search engine, but also the knowledge relevant to given query. Experiments show the effectiveness of our approach.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - Third International Conference, KSEM 2009, Proceedings
Pages158-167
Number of pages10
DOIs
StatePublished - 2009
Event3rd International Conference on Knowledge Science, Engineering and Management, KSEM 2009 - Vienna, Austria
Duration: 25 Nov 200927 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5914 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Knowledge Science, Engineering and Management, KSEM 2009
Country/TerritoryAustria
CityVienna
Period25/11/0927/11/09

Keywords

  • Academic Search
  • Clustering
  • Knowledge discovery
  • Ranking
  • Vertical Search engine

Fingerprint

Dive into the research topics of 'Knowledge discovery from academic search engine'. Together they form a unique fingerprint.

Cite this