skip to main content
10.1145/1966883.1966893acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbewebConference Proceedingsconference-collections
research-article

Model-driven restricted-domain adaptation of question answering systems for business intelligence

Published: 25 March 2011 Publication History

Abstract

Business Intelligence (BI) applications no longer limit their analysis to structured databases, but they also need to obtain actionable information from unstructured sources (e.g. data from the Web, etc.). Interestingly, Question Answering (QA) systems are good candidates for these purposes, since they allow users to obtain concise answers to questions stated in natural language from a collection of text documents. Traditionally, QA systems include patterns for dealing with a large spectrum of general questions, namely open-domain question answering (ODQA). However, BI users should be aware of asking questions related to a specific activity of the business (e.g. healthcare, agricultural, transportation, etc.). Therefore, adapting ODQA systems to new restricted domains is an increasingly necessity for these systems to be precisely used in BI. Unfortunately, research addressing this topic has two main drawbacks: (i) patterns are manually tuned, which requires a huge effort in time and cost, and (ii) tuning of patterns is based on analyzing potential questions to be answered, which is not a realistic situation since, in restricted domains, questions are highly complex and difficult to be acquired. To overcome these drawbacks, this paper presents a novel approach based on model-driven development in order to use knowledge resources to automatically and effortlessly adapt patterns of ODQA systems to be useful for restricted-domain BI scenarios.

References

[1]
R. Baeza-Yates and B. Ribeiro-Neto. Modern information retrieval. ACM Press, New York, 1999.
[2]
J. Bézivin. On the unification power of models. Software and System Modeling, 4(2):171--188, 2005.
[3]
W. F. Cody, J. T. Kreulen, V. Krishna, and W. S. Spangler. The integration of business intelligence and knowledge management. IBM Systems Journal, 41(4):697--713, 2002.
[4]
A. Ferrández, M. Palomar, and L. Moreno. An Empirical Approach to Spanish Anaphora Resolution. Machine Translation, 14(3--4):191--216, 1999.
[5]
A. Ferrández and J. Peral. The benefits of the interaction between data warehouses and question answering. In F. Daniel, L. M. L. Delcambre, F. Fotouhi, I. Garrigós, G. Guerrini, J.-N. Mazón, M. Mesiti, S. Müller-Feuerstein, J. Trujillo, T. M. Truta, B. Volz, E. Waller, L. Xiong, and E. Zimányi, editors, EDBT/ICDT Workshops, ACM International Conference Proceeding Series. ACM, 2010.
[6]
S. Ferrández and J. Peral. Investigating the Best Configuration of HMM Spanish PoS Tagger when Minimum Amount of Training Data Is Available. In Natural Language Processing and Information Systems (NLDB), pages 341--344, 2005.
[7]
G. Hodge. Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files. The Digital Library Federation Council on Library and Information Resources, 2000.
[8]
A. Kleppe, J. Warmer, and W. Bast. MDA Explained. The Practice and Promise of The Model Driven Architecture. Addison Wesley, 2003.
[9]
L. Kosseim and J. Yousefi. Improving the performance of question answering with semantically equivalent answer patterns. Data Knowl. Eng., 66(1):53--67, 2008.
[10]
A. Löser, F. Hueske, and V. Markl. Situational business intelligence. In W. Aalst, J. Mylopoulos, N. M. Sadeh, M. J. Shaw, C. Szyperski, M. Castellanos, U. Dayal, and T. Sellis, editors, Business Intelligence for the Real-Time Enterprise, volume 27 of Lecture Notes in Business Information Processing, pages 1--11. Springer Berlin Heidelberg, 2009. 10.1007/978-3-642-03422-0-1.
[11]
D. Mollá and J. L. V. González. Question answering in restricted domains: An overview. Computational Linguistics, 33(1):41--61, 2007.
[12]
A. Peñas, P. Forner, R. Sutcliffe, Á. Rodrigo, C. Forascu, I. Alegria, D. Giampiccolo, N. Moreau, and P. Osenova. Overview of ResPubliQA 2009: Question Answering Evaluation over European Legislation. In Working Notes of Cross Language Evaluation Forum (CLEF), 2009.
[13]
J. M. Pérez-Martínez, R. B. Llavori, M. J. A. Cabo, and T. B. Pedersen. Contextualizing data warehouses with documents. Decision Support Systems, 45(1):77--94, 2008.
[14]
T. Priebe and G. Pernul. Ontology-based integration of olap and information retrieval. In DEXA Workshops, pages 610--614. IEEE Computer Society, 2003.
[15]
S. Roger, K. Vila, A. Ferrández, M. Pardiño, J. M. Gómez, M. Puchol-Blasco, and J. Peral. Using AliQAn in Monolingual QA@CLEF 2008. In CLEF, pages 333--336, 2008.
[16]
B. Selic. The pragmatics of model-driven development. IEEE Software, 20(5):19--25, 2003.
[17]
K. Vila, J.-N. Mazón, A. Ferrández, and J. M. Gómez. Model-driven knowledge-based development of expected answer type taxonomies for restricted domain question answering. In Fourth International Conference on Metadata and Semantic Research (MTSR) 2010, CCIS, vol. 108, pages 107--118, 2010.

Cited By

View all
  • (2024)Situational Data Integration in Question Answering systems: a survey over two decadesKnowledge and Information Systems10.1007/s10115-024-02136-066:10(5875-5918)Online publication date: 18-Jun-2024
  • (2015)Target Detection and Knowledge Learning for Domain Restricted Question AnsweringNatural Language Processing and Chinese Computing10.1007/978-3-319-25207-0_27(325-336)Online publication date: 20-Oct-2015
  • (2014)LSA Based Approach to Domain DetectionHuman-Inspired Computing and Its Applications10.1007/978-3-319-13647-9_7(62-69)Online publication date: 2014

Index Terms

  1. Model-driven restricted-domain adaptation of question answering systems for business intelligence

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    BEWEB '11: Proceedings of the 2nd International Workshop on Business intelligencE and the WEB
    March 2011
    54 pages
    ISBN:9781450306102
    DOI:10.1145/1966883
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 March 2011

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. knowledge organization systems
    2. metamodeling
    3. model-driven development
    4. question answering

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    EDBT/ICDT '11

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Situational Data Integration in Question Answering systems: a survey over two decadesKnowledge and Information Systems10.1007/s10115-024-02136-066:10(5875-5918)Online publication date: 18-Jun-2024
    • (2015)Target Detection and Knowledge Learning for Domain Restricted Question AnsweringNatural Language Processing and Chinese Computing10.1007/978-3-319-25207-0_27(325-336)Online publication date: 20-Oct-2015
    • (2014)LSA Based Approach to Domain DetectionHuman-Inspired Computing and Its Applications10.1007/978-3-319-13647-9_7(62-69)Online publication date: 2014

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media