Answering contextual questions based on ontologies and question templates

doi:10.1007/s11704-011-1031-9

Front Comput Sci Chin

2011, Vol. 5

Issue (4) : 405-418 https://doi.org/10.1007/s11704-011-1031-9

RESEARCH ARTICLE

Answering contextual questions based on ontologies and question templates

Dongsheng WANG^1,2,3(

)

1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. Graduate University of the Chinese Academy of Sciences, Beijing 100049, China; 3. Department of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Download: PDF(363 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Contextual question answering (CQA), in which user information needs are satisfied through an interactive question answering (QA) dialog, has recently attracted more research attention. One challenge is to fuse contextual information into the understanding process of relevant questions. In this paper, a discourse structure is proposed to maintain semantic information, and approaches for recognition of relevancy type and fusion of contextual information according to relevancy type are proposed. The system is evaluated on real contextual QA data. The results show that better performance is achieved than a baseline system and almost the same performance as when these contextual phenomena are resolved manually. A detailed evaluation analysis is presented.

Keywords Contextual question answering (CQA) ontology question templates

Corresponding Author(s): WANG Dongsheng,Email:wds_ict@163.com

Issue Date: 05 December 2011

Cite this article:

Dongsheng WANG. Answering contextual questions based on ontologies and question templates[J]. Front Comput Sci Chin, 2011, 5(4): 405-418.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-011-1031-9
https://academic.hep.com.cn/fcs/EN/Y2011/V5/I4/405

Fig.1 Part of the domain ontology of BSC domain

Fig.2 Part of problem ontology

Fig.3 Definition of question template

Fig.4 Relationship between question template and conceptual model

Fig.5 Pseudo-code of QTM algorithm.

Tab.1 Example discourse structure of user1’s question

Fig.6 Pseudo-code of RTR algorithm.

Fig.7 Pseudo-code of CIF algorithm.

Fig.8 Relations between number of question template and accuracy as well as recognition rate

Fig.9 Relations between number of services and accuracy as well as recognition rate

Tab.2 Four characteristics adding orderly to the formula

Tab.3 Evaluation of contextual question answering (accuracy)

1	Tsuneaki K, Junichi F, Fumito M, Kando N. Handling information access dialogue through QA technologies - a novel challenge for open domain question answering. In: Proceedings of HLT-NAACL 2004 Workshop on Pragmatics in Question Answering . 2004, 70–77
2	Chai J, Jin R. Discourse structure for context question answering. In: Proceedings of HLT-NAACL 2004 Workshop on Pragmatics of Question Answering . 2004, 23–30
3	Carbonell J G. Discourse pragmatics and ellipsis resolution in task-oriented natural language interfaces. In: Proceedings of 21st Annual Meeting on Association for Computational Linguistics . 1983, 164–168
4	Nils D, Jonsson A. Empirical studies of discourse representations for natural language interfaces. In: Proceedings of 4th Conference of the European Chapter of the ACL . 1989, 291–298
5	Wang D S. A domain-specific question answering system based on ontology and question templates. In: Proceedings of 11th ACIS International Conference on Software Engineering . 2010, 151–156
6	Fernlcndez R, Katagiri Y, Komatani K, Lemon O, Nakano M, eds. Proceedings of 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Stroudsburg: Association for Computational Linguistics, 2010
7	Chai J Y, Moore J D, Passonneau R J, Traum, D R, et al, eds. Proceedings of 12th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Stroudsburg: Association for Computational Linguistics, 2011
8	Mitkov R, Boguraev B K, Tait J I, Palmer M, eds. Journal of Natural Language Engineering, Special Issue on Interactive Question Answering, Cambridge: Cambridge University Press, 2009
9	Voorhees E M. Overview of the TREC 2001 question answering track. In: Proceedings of 10th Text Retrieval Conference . 2001, 42–51
10	Mori T, Kawaguchi S, Ishioroshi M. Answering contextual questions based on the cohesion with knowledge. International Journal of Computer Processing Of Languages , 2007, 20(2/3): 115–135
11	Bernardi R, Kirschner M. From artificial questions to real user interaction logs: real challenges for interactive question answering systems. In: Proceedings of Workshop on Web Logs and Question Answering . 2010, 8–15
12	Yang F, Feng J, DiFabbrizio G. A data driven approach to relevancy recognition for contextual question answering. In: Proceedings of HLT-NAACL 2006 Workshop on Interactive Question Answering . 2006, 33–40
13	Kirschner M, Bernardi R, Baroni M, Dinh L T. Analyzing interactive QA dialogues using logistic regression models. In: Proceedings of XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence . 2009, 334–344
14	Bernardi R, Kirschner M, Ratkovic Z. Context fusion: the role of discourse structure and centering theory. In: Proceedings of 19th International Conference on Language Resources and Evaluation . 2010, 2014–2021
15	Kirschner M, Bernardi R. Towards an empirically motivated typology of follow-up questions: the role of dialogue context. In: Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue . 2010, 322–331
16	Kato T, Fukumoto J, Masui F, Kando N. Are open-domain question answering technologies useful for information access dialogues? ---an empirical study and a proposal of a novel challenge. ACM Transactions on Asian Language Information Processing , 2005, 4(3): 243–262
17	Van schooten B W, Op den akker R, Rosset S, Galibert O, Max A, Illouz G. Follow-up question handling in the IMIX and Ritel systems: A comparative study. Journal of Natural Language Engineering , 2009, 15(1):97–118
18	Murata Y, Akiba T, Fujii A, Itou K. Question answering experiments at NTCIR-5: Acquisition of answer evaluation patterns and context processing using passage retrieval. In: Proceedings of the 5th NTCIR Workshop Meeting . 2005, 394–401
19	Matsuda M, Fukumoto J. Answering questions of IAD task using reference resolution of follow-up questions. In: Proceedings of the 5th NTCIR Workshop Meeting . 2005, 414–421
20	Hobbs J R. On the coherence and structure of discourse. Center for the Study of Language and Information from Leland Stanford Junior University. Report No. CSLI-85-37 , 1985
21	Mann W C, Thompson S A. Rhetorical structure theory: a theory of text organization. USC/ISI Technical Report ISI/RS-87-190 , 1987
22	Grosz B J, Sidner C. Attention, intention, and the structure of discourse. Computational Linguistics , 1986, 12(3): 175–204
23	Kamp H, Reyle U. From Discourse To Logic. Dordrecht: Kluwer Academic Publishers, 1993
24	Lars A, Nils D, Jagonsson A. Discourse representation and discourse management for natural language interfaces. In: Proceedings of the 2nd Nordic Conference on Text Comprehension in Man and Machine . 1990, 1–14
25	Quarteroni S, Manandhar S. Adaptivity in question answering with user modeling and a dialogue interface. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics . 2006, 199–202
26	Quarteroni S, Manandhar S. Designing an interactive open-domain question answering system. Journal of Natural Language Engineering , 2009, 15(1): 73–95
27	Quarteroni S, Manandhar S. User modeling for personalized question answering. In: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence (AIIA* ). 2007, 386–397
28	Sun M, Chai J J. Towards intelligent QA interfaces: discourse processing for context questions. In: Proceedings of 11th International Conference on Intelligent User Interfaces . 2006, 163–170
29	Noy N F, McGuinnes D L. Ontology development 101: a guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report . 2001
30	De Boni M, Manandhar S. Implementing clarification dialogues in open domain question answering. Journal of Natural Language Engineering , 2005, 11(4): 343–361

[1]	Houda AKREMI, Sami ZGHAL. DOF: a generic approach of domain ontology fuzzification[J]. Front. Comput. Sci., 2021, 15(3): 153322-.
[2]	Yuxin YE, Xianji CUI, Dantong OUYANG. Extracting a justification for OWL ontologies by critical axioms[J]. Front. Comput. Sci., 2020, 14(4): 144305-.
[3]	Changlong WANG, Zhiyong FENG, Xiaowang ZHANG, Xin WANG, Guozheng RAO, Daoxun FU. ComR: a combined OWL reasoner for ontology classification[J]. Front. Comput. Sci., 2019, 13(1): 139-156.
[4]	Abdelkrim CHEBIEB, Yamine AIT AMEUR. A formal model for plastic human computer interfaces[J]. Front. Comput. Sci., 2018, 12(2): 351-375.
[5]	Chuanping HU,Zheng XU,Yunhuai LIU,Lin MEI. Video structural description technology for the new generation video surveillance systems[J]. Front. Comput. Sci., 2015, 9(6): 980-989.
[6]	Chuantao YIN,Bingxue ZHANG,Betrand DAVID,Zhang XIONG. A hierarchical ontology context model for work-based learning[J]. Front. Comput. Sci., 2015, 9(3): 466-473.
[7]	Dantong OUYANG, Xianji CUI, Yuxin YE. Integrity constraints in OWL ontologies based on grounded circumscription[J]. Front Comput Sci, 2013, 7(6): 812-821.
[8]	Weimin WANG, Jingchun ZHANG, Cong CAO, Tao HOU, Yue LIU, Keji CHEN. An efficient approach to representing and mining knowledge from Qing court medical records[J]. Front Comput Sci Chin, 2011, 5(4): 395-404.
[9]	Ruixuan LI, Kunmei WEN, Xiwu GU, Yuhua LI, Xiaolin SUN, Bing LI. Type-2 fuzzy description logic[J]. Front Comput Sci Chin, 2011, 5(2): 205-215.

Viewed

Full text

Abstract

Cited

Shared

Discussed