On content-driven search-keyword suggesters for literature digital libraries

ABSTRACT

We propose and evaluate a "content-driven search keyword suggester" for keyword-based search in literature digital libraries. Suggesting search keywords at an early stage, i.e., while the user is entering search terms, is helpful for constructing more accurate, less ambiguous, and focused search keywords for queries. Our search keyword suggestion approach is based on an a priori analysis of the publication collection in the digital library at hand, and consists of the following steps. We (i) parse the document collection using the Link Grammar parser, a syntactic parser of English, (ii) group publications based on their "most-specific" research topics, (iii) use the parser output to build a hierarchical structure of simple and compound tokens to be used to suggest search terms, (iv) use TextRank, a text summarization tool, to assign topic-sensitive scores to keywords, and (v) use the identified research-topics to help user aggregate search keywords prior to the actual search query execution.

We experimentally show that the proposed framework, which is optimized to work on literature digital libraries, promises a more scalable, high quality, and user-friendly search-keyword suggester when compared to its competitors. We validate our proposal experimentally using a subset of the ACM SIGMOD Anthology digital library as a testbed, and by employing the research-pyramid model to identify the "most-specific" research topics.

REFERENCES

• 1: Wiki, http://en.wikipedia.org
• 2: Google Suggest Labs, http://www.google.com/webhp?complete=1&hl=en
• 3: CiteSeer, www.citeseer.com
• 4: ACM Digital Library, http://portal.acm.org/dl.cfm
• 5: IEEE XPlore, http://ieeexplore.ieee.org
• 6: ScienceDirect, www.sciencedirect.info
• 7: S. Aya, C. Lagoze, and T. Joachims, "Citation Classification and its Applications", ICKM 2005.
• 8: Glen Jeh , Jennifer Widom, SimRank: a measure of structural-context similarity, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775126]
• 9: Google Scholar, http://scholar.google.com/scholar
• 10: Bani-Ahmad, S., Ozsoyoglu, T. "Improved Publication Scores for Online Digital Libraries via Research Pyramids", ECDL 2007.
• 11: Davy Temperley, Daniel Sleator, John Lafferty, Link Gramar Parser 4.1 - http://www.link.cs.cmu.edu/link, 2005.
• 12: D. Sleator, D. Temperly and J. Lafferty, Link Grammar Parser version 4.1 (http://www.link.cs.cmu.edu/link/), 2005
• 13: Derek Sisson, A Thoughtful Approach to Web Site Quality, http://www.philosophe.com/
• 14: iProspect Inc., iProspect Search Engine User Behavior study, iProspect 2006.
• 15: Uichin Lee , Zhenyu Liu , Junghoo Cho, Automatic identification of user goals in Web search, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060804]
• 16: Y. Zhang and A. Moffat, Some Observations on User Search Behavior, the 11th Australian Document Computing Symposium, Australia, 2006.
• 17: J. Wusteman and P. O'hIceadha, Using Ajax to Empower Dynamic Searching, Information Technolgy and Libraries, Vol. 25 No 2, June 2006, PP 57--64.
• 18: R. Mihalcea and P. Tarau, TextRank: Bringing Order into Texts, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain, July 2004.
• 19: The Onix full text indexing engine, The most widely used stopword list, http://www.lextek.com/manuals/onix/stopwords1.html
• 20: Using AJAX to Empower Dynamic Searching, Information Technology and Libraries, Vol 25, No 2, June 2006
• 21: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, v.30 n.1-7, p.107-117, April 1, 1998
• 22: elGiza Content-Based Search-keyword Suggester, http://sulieman.case.edu/giza/cbsks_xkb_search/
• 23: The CompleteSearch engine, http://search.mpi-inf.mpg.de/
• 24: Holger Bast , Debapriyo Majumdar , Ingmar Weber, Efficient interactive query expansion with complete search, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321560]
• 25: Holger Bast , Ingmar Weber, Type less, find more: fast autocompletion search with a succinct index, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148234]
• 26: Sulieman Bani-Ahmad, Gultekin Ozsoyoglu, elGiza, A Research-Pyramid Based Search Tool for Vertical Literature Digital Libraries, DBRank 2008.
• 27: Donald O. Cases , Georgeann M. Higgins, How can we investigate citation behavior?: a study of reasons for citing literature in communication, Journal of the American Society for Information Science, v.51 n.7, p.635-645, May 2000 [doi>10.1002/(SICI)1097-4571(2000)51:7<635::AID-ASI6>3.0.CO;2-H]
• 28: Gupta, Chirag, Efficient K-Word Proximity Search, MS Thesis, CWRU, EECS Department, 2008.
• 29: Robert Krovetz, Homonymy and polysemy in information retrieval, Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, p.72-79, July 07-12, 1997, Madrid, Spain
• 30: AJAX tutorial, http://www.w3schools.com/ajax/
• 31: Kunihiko Sadakane , Hiroshi Imai, Text Retrieval by Using k-word Proximity Search, Proceedings of the 1999 International Symposium on Database Applications in Non-Traditional Environments, p.183, November 28-30, 1999

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries

General Terms:
Experimentation, Performance

Keywords:
literature digital libraries, search-keyword suggester