Ramakanth Kavuluru
Amit P. Sheth
ABSTRACT
In domain-specific search systems, knowledge of a domain of interest is embedded as a backbone that guides the search process. But the knowledge used in most such systems 1. exists only for few well known broad domains; 2. is of a basic nature: either purely hierarchical or involves only few relationship types; and 3. is not always kept up-to-date missing insights from recently published results. In this paper we present a framework and implementation of a focused and up-to-date knowledge-based search system, called Scooner, that utilizes domain-specific knowledge extracted from recent bioscience abstracts. To our knowledge, this is the first attempt in the field to address all three shortcomings mentioned above. Since recent introduction for operational use at Applied Biotechnology Branch of AFRL, some biologists are using Scooner on a regular basis, while it is being made available for use by many more. Initial evaluations point to the promise of the approach in addressing the challenge we set out to address.
- E. Agichtein and L. Gravano. Snowball: Extracting relations from large plain-text collections. In 5th ACM conf. on Digital libraries, pages 85--94, 2000. Google Scholar
Digital Library
- O. Bodenreider. Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support. Yearbook of medical informatics, page 67, 2008.Google Scholar
- K. Clauson, H. Polen, M. Boulos, and J. Dzenowagis. Scope, completeness, and accuracy of drug information in Wikipedia. The Annals of pharmacotherapy, 42(12):1814, 2008.Google ScholarCross Ref
- M. de Marneffe, B. MacCartney, and C. Manning. Generating Typed Dependency Parses from Phrase Structure Parses. In Proceedings of LREC 2006.Google Scholar
- H. Dietze, D. Alexopoulou, M. Alvers, L. Barrio-Alvers, B. Andreopoulos, A. Doms, J. Hakenberg, J. Monnich, C. Plake, A. Reischuck, et al. Gopubmed: Exploring pubmed with ontological background knowledge. Bioinformatics for Systems Biology, pages 385--399, 2009.Google ScholarCross Ref
- O. Etzioni, M. J. Cafarella, D. Downey, S. Kok, A. M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates. Web-scale information extraction in knowitall: (preliminary results). In Proceedigns of WWW '04, pages 100--110. ACM, 2004. Google ScholarDigital Library
- M. Gillam, C. Feie, J. Handler, E. Moody, B. Shneiderman, C. Plaisant, M. Smith, and J. Dickason. The healthcare singularity and the age of semantic medicine, pages 57--63. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, 2009.Google Scholar
- M. Harris, J. Clark, A. Ireland, J. Lomax, M. Ashburner, R. Foulger, K. Eilbeck, S. Lewis, B. Marshall, C. Mungall, et al. The Gene Ontology (GO) database and informatics resource. Nucleic acids research, 32 (Database issue): D258, 2004.Google Scholar
- M. Hearst. Automatic acquisition of hyponyms from large text corpora. In 14th conf. on Computational linguistics-Volume 2, pages 539--545, 1992. Google ScholarDigital Library
- W. Hersh, A. Cohen, P. Roberts, and H. Rekapalli. TREC 2006 Genomics Track Overview.Google Scholar
- G. Jeh and J. Widom. SimRank: A measure of structural-context similarity. In ACM SIGKDD, pages 538--543, 2002. Google ScholarDigital Library
- M. Laurent and T. Vickers. Seeking health information online: does Wikipedia matter? Journal of the American Medical Informatics Association, 16(4):471--479, 2009.Google ScholarCross Ref
- D. Lizorkin, P. Velikhov, M. Grinev, and D. Turdakov. Accuracy estimate and optimization techniques for simrank computation. The VLDB Journal, 19(1):45--66, 2010. Google ScholarDigital Library
- Z. Lu. PubMed and beyond: a survey of web tools for searching biomedical literature. Database: the journal of biological databases and curation, 2011, 2011.Google Scholar
- Q. Nguyen, D. Tikk, and U. Leser. Simple tricks for improving pattern-based information extraction from the biomedical literature. Journal of Biomedical Semantics, 1(1):9, 2010.Google ScholarCross Ref
- C. Perez-Iratxeta, P. Bork, and M. Andrade. XplorMed: a tool for exploring MEDLINE abstracts. Trends in biochemical sciences, 26(9):573--575, 2001.Google Scholar
- C. Ramakrishnan, P. Mendes, R. Gama, G. Ferreira, and A. Sheth. Joint Extraction of Compound Entities and Relationships from Biomedical Literature. In IEEE Intl. Conf. on Web Intelligence and Intelligent Agent Technology, pages 398--401, 2008. Google ScholarDigital Library
- A. Ruttenberg, T. Clark, W. Bug, M. Samwald, O. Bodenreider, H. Chen, D. Doherty, K. Forsberg, Y. Gao, V. Kashyap, et al. Advancing translational research with the Semantic Web. BMC bioinformatics, 8(Suppl 3):S2, 2007.Google ScholarCross Ref
- D. Swanson. Migraine and magnesium: eleven neglected connections. Perspectives in biology and medicine, 31(4):526--557, 1988.Google Scholar
- C. Thomas, P. Mehra, R. Brooks, and A. Sheth. Growing Fields of Interest-Using an Expand and Reduce Strategy for Domain Model Extraction. In Intl. Conf. on Web Intelligence and Intelligent Agent Technology, pages 496--502, 2008. Google ScholarDigital Library
- C. J. Thomas, P. Mehra, A. P. Sheth, W. Wang, and G. Weikum. Automatic Domain Model Creation from Structured and Unstructured Sources. In submitted to ISWC 2011, 2011.Google Scholar
- P. Turney. Expressing implicit semantic relations without supervision. In Proceedings of ACL 2006, pages 313--320, 2010. Google ScholarDigital Library
- F. Wu and D. S. Weld. Open Information Extraction using Wikipedia. In ACL-2010, 2010. Google ScholarDigital Library
- Y. Yamamoto and T. Takagi. Biomedical knowledge navigation by literature clustering. Journal of Biomedical Informatics, 40(2):114--130, 2007. Google ScholarDigital Library
Index Terms
- An up-to-date knowledge-based literature search and exploration framework for focused bioscience domains
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