Xart: Discovery of correlated arguments of n-ary relations in text

Expert Systems with Applications

Volumn 73, Issue , 2017, Pages 115-124

  Berrahou, Soumia Lilia ^a,b   Buche, Patrice ^a,b   Dibie, Juliette ^c   Roche, Mathieu ^a,d  

^a LIRMM   (France)

^b UMR IATE   (France)

^c UNIVERSITÉ PARIS SACLAY   (France)

^d UMR TETIS   (France)

Author keywords

Data mining; Information extraction; Linguistic pattern; N ary relation; Ontology; Quantitative data; Sequential pattern

Indexed keywords

DATA MINING; INFORMATION RETRIEVAL; ONTOLOGY; SYNTACTICS;

DOMAIN ONTOLOGIES; HYBRID METHOD; LINGUISTIC PATTERNS; PLAIN TEXT; QUANTITATIVE DATA; SEQUENTIAL PATTERNS; SYNTACTIC ANALYSIS;

TEXT MINING;

EID: 85007402149     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2016.12.028     Document Type: Article

References (47)

1. Agrawal, R., Srikant, R., Fast algorithms for mining association rules in large databases. Proceedings of the 20th international conference on very large data bases VLDB ’94, 1994, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 487–499.
2. Agrawal, R., Srikant, R., Mining sequential patterns. Proceedings of the eleventh international conference on data engineering ICDE ’95, 1995, IEEE Computer Society, Washington, DC, USA, 3–14.
3. Béchet, N., Cellier, P., Charnois, T., Crémilleux, B., Discovering linguistic patterns using sequence mining. Proceedings of the 13th international conference on computational linguistics and intelligent text processing - volume part I CICLing’12, 2012, Springer-Verlag, Berlin, Heidelberg, 154–165.
4. Berrahou, S.L., Buche, P., Dibie, J., Roche, M., Xart system: discovering and extracting correlated arguments of n-ary relations from text. Proceedings of the 6th international conference on web intelligence, mining and semantics, WIMS 2016, Nîmes, France, June 13–15, 2016, 2016, 8:1–8:12.
5. Björne, J., Heimonen, J., Ginter, F., Airola, A., Pahikkala, T., Salakoski, T., Extracting complex biological events with rich graph-based feature sets. Proceedings of the workshop on current trends in biomedical natural language processing: Shared task BioNLP ’09, 2009, Association for Computational Linguistics, Stroudsburg, PA, USA, 10–18.
6. Buche, P., Dervaux, S., Dibie-Barthélemy, J., Soler, L., Ibanescu, L., Touhami, R., Intégration de données hétérogènes et imprécises guidée par une ressource termino-ontologique. Revue d'Intelligence Artificielle 27:4–5 (2013), 539–568.
7. Buche, P., Dibie-Barthélemy, J., Ibanescu, L., Soler, L., Fuzzy web data tables integration guided by an ontological and terminological resource. IEEE Transactions on Knowledge and Data Engineering 25:4 (2013), 805–819.
8. Bui, Q.-C., Sloot, P.M.A., Extracting biological events from text using simple syntactic patterns. Proceedings of the bionlp shared task 2011 workshop BioNLP Shared Task ’11, 2011, Association for Computational Linguistics, Stroudsburg, PA, USA, 143–146.
9. Buyko, E., Faessler, E., Wermter, J., Hahn, U., Event extraction from trimmed dependency graphs. Proceedings of the workshop on current trends in biomedical natural language processing: Shared task BioNLP ’09, 2009, Association for Computational Linguistics, Stroudsburg, PA, USA, 19–27.
10. Cellier, P., Charnois, T., Plantevit, M., Rigotti, C., Crémilleux, B., Gandrillon, O.,.. Manguin, J., Sequential pattern mining for discovering gene interactions and their contextual information from biomedical texts. Journal of Biomedical Semantics, 6, 2015, 27.
11. Damerau, F.J., A technique for computer detection and correction of spelling errors. Communications of the ACM 7:3 (1964), 171–176.
12. Di-Jorio, L., Bringay, S., Fiot, C., Laurent, A., Teisseire, M., Sequential patterns for maintaining ontologies over time. On the move to meaningful internet systems: OTM 2008, OTM 2008 confederated international conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008, Monterrey, Mexico, November 9–14, 2008, proceedings, part II, 2008, 1385–1403.
13. Gkoutos, G. (2011). Units ontology. http://www.obofoundry.org/ontology/uo.html.
14. Gruber, T.R., Olsen, G., An ontology for engineering mathematics. Doyle, J., Torasso, P., Sandewall, E., (eds.) Principles of knowledge representation and reasoning: Proceedings of the 4th international conference (kr ’94): Bonn, Germany: 1994, May, 24 - 27 The Morgan Kaufmann Series in Representation and Reasoning, 1994, Morgan Kaufmann Publishers, 258–269.
15. Guillard, V., Buche, P., Destercke, S., Tamani, N., Croitoru, M., Menut, L.,.. Gontard, N., A decision support system to design modified atmosphere packaging for fresh produce based on a bipolar flexible querying approach. CEA(111), 2015, 131–139.
16. Hao, Y., Zhu, X., Huang, M., Li, M., Discovering patterns to extract protein-protein interactions from the literature: Part ii. Bioinformatics 21 (2005), 3294–3300.
17. Hawizy, L., Jessop, D., Adams, N., Murray-Rust, P., Chemicaltagger: a tool for semantic text-mining in chemistry. Journal of Cheminformatics, 3(1), 2011, 17.
18. Hiemstra, D., A probabilistic justification for using tf x idf term weighting in information retrieval. International Journal on Digital Libraries 3:2 (2000), 131–139.
19. Hodgson, R., Paul, J., Jack, H., & Jack, S. (2013). Qudt-quantities, units, dimensions and data types ontologies. http://www.qudt.org.
20. Huang, M., Zhu, X., Payan, D.G., Qu, K., Li, M., Discovering patterns to extract protein-protein interactions from full biomedical texts. Proceedings of the international joint workshop on natural language processing in biomedicine and its applications JNLPBA ’04, 2004, Association for Computational Linguistics, Stroudsburg, PA, USA, 22–28.
21. Jaillet, S., Laurent, A., Teisseire, M., Sequential patterns for text categorization. Intelligent Data Analysis 10:3 (2006), 199–214.
22. Jessop, D.M., Adams, S.E., Murray-Rust, P., Mining chemical information from open patents. Journal of Cheminformatics, 3(1), 2011, 40.
23. Jessop, D.M., Adams, S.E., Willighagen, E.L., Hawizy, L., Murray-Rust, P., OSCAR4: A flexible architecture for chemical text-mining. Journal of Cheminformatics 3:1 (2011), 1–12.
24. John, G.H., Langley, P., Estimating continuous distributions in Bayesian classifiers. Proceedings of the conference on uncertainty in artificial intelligence, 1995, 338–345.
25. Jones, K.S., Walker, S., Robertson, S.E., A probabilistic model of information retrieval: development and comparative experiments - part 1. Information Processing and Management 36:6 (2000), 779–808.
26. Kohavi, G.H., Ron, J., The wrapper approach. Liu, H. Huanand Motoda, (eds.) Feature Extraction, Construction and Selection: A Data Mining Perspective, 1998, Springer US, Boston, MA, 33–50).
27. Kohavi, R., Quinlan, J.R., Data mining tasks and methods: Classification: decision-tree discovery. Handbook of data mining and knowledge discovery, 2002, Oxford University Press, Inc, 267–276.
28. Le Minh, Q., Truong, S.N., Bao, Q.H., A pattern approach for biomedical event annotation. Proceedings of the BioNLP shared task 2011 workshop BioNLP Shared Task ’11, 2011, Association for Computational Linguistics, Stroudsburg, PA, USA, 149–150.
29. Lossio-Ventura, J.A., Jonquet, C., Roche, M., Teisseire, M., Biomedical term extraction: Overview and a new methodology. Information Retrieval Journal 19:1–2 (2016), 59–99.
30. Maedche, A., Staab, S., Measuring similarity between ontologies. Knowledge engineering and knowledge management: Ontologies and the semantic web LNCS, 2473, 2002, Springer, 251–263.
31. Minard, A.-L., Ligozat, A.-L., Grau, B., Multi-class svm for relation extraction from clinical reports. Angelova, G., Bontcheva, K., Mitkov, R., Nicolov, N., (eds.) Ranlp, 2011, RANLP 2011 Organising Committee, 604–609.
32. Miwa, M., Sætre, R., Miyao, Y., Tsujii, J., A rich feature vector for protein-protein interaction extraction from multiple corpora. Proceedings of the 2009 conference on empirical methods in natural language processing: Volume 1 EMNLP ’09, 2009, Association for Computational Linguistics, Stroudsburg, PA, USA, 121–130.
33. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M., Prefixspan: Mining sequential patterns by prefix-projected growth. Proceedings of the 17th international conference on data engineering, 2001, IEEE Computer Society, Washington, DC, USA, 215–224.
34. Platt, J.C., Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods, 1999, MIT Press, 185–208.
35. Proux, D., Rechenmann, F., Julliard, L., A pragmatic information extraction strategy for gathering data on genetic interactions. Bourne, P.E., Gribskov, M., Altman, R.B., Jensen, N., Hope, D.A., Lengauer, T., Mitchell, J.C., Scheeff, E.D., Smith, C., Strande, S., Weissig, H., (eds.) Ismb, 2000, AAAI, 279–285.
36. Qiu, J., Tang, C., Zeng, T., Shaojie, Q., Zuo, J., Chen, P., A novel text classification approach based on enhanced association rule. Alhajj, H.L.J.L.X.Z.O.R., Redaand, Gao, (eds.) Advanced Data Mining and Applications: Third International Conference, ADMA 2007 Harbin, China, August 6–8, 2007. Proceedings, 2007, Springer, Berlin, Heidelberg, 252–263).
37. Raja, K., Subramani, S., Natarajan, J., Ppinterfinder - a mining tool for extracting causal relations on human proteins from literature. Database, 2013, 2013.
38. Rijgersberg, H., van Assem, M., Top, J.L., Ontology of units of measure and related concepts. Semantic Web 4:1 (2013), 3–13.
39. Rosario, B., Hearst, M.A., Multi-way relation classification: Application to protein-protein interactions. Proceedings of the conference on human language technology and empirical methods in natural language processing HLT ’05, 2005, Association for Computational Linguistics, Stroudsburg, PA, USA, 732–739.
40. Schadow, G., McDonald, C.J., Suico, J.G., Föhring, U., Tolxdorff, T., Model formulation: Units of measure in clinical information systems. JAMIA 6:2 (1999), 151–162.
41. Su, J., Zhang, H., Ling, C.X., Matwin, S., Discriminative parameter learning for Bayesian networks. Proceedings of the international conference on machine learning, 2008, 1016–1023.
42. Touhami, R., Buche, P., Dibie-Barthélemy, J., Ibanescu, L., An Ontological and Terminological Resource for n-ary Relation Annotation in Web Data Tables. ODBASE Conferences (2), 2011, 662–679.
43. Van Landeghem, S., Saeys, Y., De Baets, B., Van de Peer, Y., Analyzing text in search of bio-molecular events: A high-precision machine learning framework. Proceedings of the workshop on current trends in biomedical natural language processing: Shared task BioNLP ’09, 2009, Association for Computational Linguistics, Stroudsburg, PA, USA, 128–136.
44. Yan, X., Han, J., Afshar, R., Clospan: Mining closed sequential patterns in large databases. Barbará, D., Kamath, C., (eds.) Sdm, 2003, SIAM.
45. Zaki, M.J., Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42:1–2 (2001), 31–60.
46. Zhang, H., Huang, M., Zhu, X., Protein-protein interaction extraction from bio-literature with compact features and data sampling strategy. 4th international conference on biomedical engineering and informatics, BMEI 2011, Shanghai, China, October 15–17, 2011, 2011, 1767–1771.
47. Zhou, D., Zhong, D., He, Y., Event trigger identification for biomedical events extraction using domain knowledge. Bioinformatics 30:11 (2014), 1587–1594.