Mining, indexing, and searching for textual chemical molecule information on the web

Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08

Volumn , Issue , 2008, Pages 735-744

  Sun, Bingjun ^a   Mitra, Prasenjit ^a   Giles, C Lee ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Conditional random fields; Entity extraction; Hierarchical text segmentation; Independent frequent subsequence; Index pruning; Ranking; Similarity search; Substring search

Indexed keywords

BOOLEAN FUNCTIONS; COMPUTER SOFTWARE; CONTENT BASED RETRIEVAL; DATA STRUCTURES; ELECTRIC BATTERIES; HIGH TEMPERATURE SUPERCONDUCTORS; IMAGE SEGMENTATION; INDEXING (OF INFORMATION); INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; INTERNET; RANDOM PROCESSES; SEARCH ENGINES; TELECOMMUNICATION NETWORKS; TREES (MATHEMATICS);

CONDITIONAL RANDOM FIELDS; ENTITY EXTRACTION; HIERARCHICAL TEXT SEGMENTATION; INDEPENDENT FREQUENT SUBSEQUENCE; INDEX PRUNING; RANKING; SIMILARITY SEARCH; SUBSTRING SEARCH;

WORLD WIDE WEB;

EID: 57349100781     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1367497.1367597     Document Type: Conference Paper

References (31)

1. Apache lucene. http://lucene.apache.org/.
2. Nist chemistry webbook. http://webbook.nist.gov/cheniistry.
3. Royal society of chemistry, http://www.rsc.org.
4. Wordnet. http://wordnet.princeton.edu/.
5. World wide molecular matrix. http://wwmm.ch.cam.ac.uk/wikis/wwmm.
6. S. Chakrabarti. Dynamic personalized pagerank in entity-relation graphs. In Proc. WWW, 2007.
7. E. S. de Moura, C. F. dos Santos, D. R. Fernandes, A. S. Silva, P. Calado, and M. A. Nascimento. Improving web search efficiency via a locality based static pruning method. In Proc. WWW, 2005.
8. L. Dehaspe, H. Toivonen, and R. D. King. Finding frequent substructures in chemical compounds. In Proc. SIGKDD, 1998.
9. D. Freitag and A. McCallum. Information extraction using hmms and shrinkage. In AAAI Workshop on Machine Learning for Information Extraction, 1999.
10. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In Proc. ICDM, 2001.
11. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML, 2001.
12. D. L.Banville. Mining chemical structural information from the drug literature. Drug Discovery Today, ll(l-2):35-42, 2006.
13. V. I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 1966.
14. W. Li and A. McCallum. Semi-supervised sequence modeling with syntactic topic models. In Proc. AAAI, 2005.
15. G. Luo, C. Tang, and Y. li Tian. Answering relationship queries on the web. In Proc. WWW, 2007.
16. A. McCallum, D. Freitag, and F. Pereira. Maximum entropy markov models for information extraction and segmentation. In Proc. ICML, 2000.
17. A. McCallum and W. Li. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In Proc. CoNLL, 2003.
18. R. McDonald and F. Pereira. Identifying gene and protein mentions in text using conditional random fields. Bioinforrnatics, 6(Suppl 1):S6, 2005.
19. P. Murray-Rust, H. S. Rzepa, S. Tyrrell, and Y. Zhang. Representation and use of chemistry in the global electronic age. Org. Biornol. Chern., 2004.
20. P. Murray-Rust, H. S. Rzepa, and M. Wright. Development of chemical markup language (cml) as a system for handling complex chemical content. New J. Chern., 2001.
21. J. W. Raymond, E. J. Gardiner, and P. Willet. Rascal: Calculation of graph similarity using maximum common edge subgraphs. The Computer Journal, 45(6):631-644, 2002.
22. B. Settles. Abner: an open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinforrnatics, 21(14):3191-3192, 2005.
23. D. Shasha, J. T. L. Wang, and R. Giugno. Algorithmics and applications of tree and graph searching. In Proc. PODS, 2002.
24. B. Sun, P. Mitra, H. Zha, C. L. Giles, and J. Yen. Topic segmentation with shared topic detection and alignment of multiple documents. In Proc. SIGIR, 2007.
25. B. Sun, Q. Tan, P. Mitra, and C. L. Giles. Extraction and search of chemical formulae in text documents on the web. In Proc. WWW, 2007.
26. W. J. Wilbur, G. F. Hazard, G. Divita, J. G. Mork, A. R. Aronson, and A. C. Browne. Analysis of biomedical text for chemical names: A comparison of three methods. In Proc. AMIA Syrnp., 1999.
27. P. Willet, J. M. Barnard, and G. M. Downs. Chemical similarity searching. J. Chern. Inf. Cornput. Sci., 38(6):983-996, 1998.
28. X. Yan and J. Han. Closegraph: Mining closed frequent graph patterns. In Proc. SIGKDD, 2003.
29. X. Yan, F. Zhu, P. S. Yu, and J. Han. Feature-based substructure similarity search. ACM Transactions on Database Systems, 2006.
30. G. Yang. The complexity of mining maximal frequent itemsets and maximal frequent patterns. In Proc. SIGKDD, 2004.
31. Y. Zhao and G. Karypis. Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery, 2005.