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Regulation retrieval using industry specific taxonomies

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Abstract

Increasingly, taxonomies are being developed and used by industry practitioners to facilitate information interoperability and retrieval. Within a single industrial domain, there exist many taxonomies that are intended for different applications. Industry specific taxonomies often represent the vocabularies that are commonly used by the practitioners. Their jobs are multi-faceted, which include checking for code and regulatory compliance. As such, it will be very desirable if industry practitioners are able to easily locate and browse regulations of interest. In practice, multiple sources of government regulations exist and they are often organized and classified by the needs of the issuing agencies that enforce them rather than the needs of the communities that use them. One way to bridge these two distinct needs is to develop methods and tools that enable practitioners to browse and retrieve government regulations using their own terms and vocabularies, for example, via existing industry taxonomies. The mapping from a single taxonomy to a single regulation is a trivial keyword matching task. We examine a relatedness analysis approach for mapping a single taxonomy to multiple regulations. We then present an approach for mapping multiple taxonomies to a single regulation by measuring the relatedness of concepts. Cosine similarity, Jaccard coefficient and market basket analysis are used to measure the semantic relatedness between concepts from two different taxonomies. Preliminary evaluations of the three relatedness analysis measures are performed using examples from the civil engineering and building industry. These examples illustrate the potential benefits of regulatory usage from the mapping between various taxonomies and regulations.

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References

  • Al-Kofahi K, Tyrrell A, Vachher A et al (2001) A machine learning approach to prior case retrieval. In: Proceedings of the 8th international conference on artificial intelligence and law (ICAIL 2001), St. Louis, Missouri, pp 88–93

  • Aumueller D, Do HH, Massmann S et al (2005) Schema and ontology matching with COMA++. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data, Baltimore, Maryland, pp 906–908

  • Begley EF, Palmer ME, Reed KA (2005) Semantic mapping between IAI ifcXML and FIATECH AEX Models for centrifugal pumps. Technical Report NISTIR 7223, NIST

  • Bench-Capon TJM (1991) Knowledge based systems and legal applications. Academic Press Professional, Inc., San Diego, CA

    Google Scholar 

  • Bicer V, Laleci G, Dogac A et al (2005) Artemis message exchange framework: semantic interoperability of exchanged messages in the healthcare domain. ACM Sigmod Rec 34(3):71–76

    Article  Google Scholar 

  • Bonnel N, Lemaire V, Cotarmanac’h A et al (2006) Effective organization and visualization of Web search results. In: Proceedings of the 24th IASTED international conference on internet and multimedia systems and applications, Innsbruck, Austria, pp 209–216

  • Brüninghaus S, Ashley KD (2001) Improving the representation of legal case texts with information extraction methods. In: Proceedings of the 8th international conference on artificial intelligence and law (ICAIL 2001), St. Louis, Missouri, pp 42–51

  • Brunnermeier SB, Martin SA (2002) Interoperability costs in the US automotive supply chain. Supply Chain Manag 7(2):71–82

    Article  Google Scholar 

  • Cheng CP, Lau GT, Law KH (2007) Mapping regulations to industry-specific taxonomies. In: Proceedings of the 11th international conference on artificial intelligence and law (ICAIL), Stanford, CA, USA

  • Cheng CP, Lau GT, Pan J et al (2008a) Domain-specific ontology mapping by corpus-based semantic similarity. In: Proceedings of 2008 NSF CMMI engineering research and innovation conference, Knoxville, TN, USA

  • Cheng CP, Pan J, Lau G T et al (2008b) Relating taxonomies with regulations. In: Proceedings of the 9th annual international conference on digital government research (dg.o2008), Montreal, Canada

  • Construction Specifications Institute (2006) OmniClass Construction Classification System, Edition 1.0, Alexandria, Virginia

  • Crowley A, Watson A (2000) CIMsteel Integration Standards Release 2. SCI-P-268, the Steel Construction Institute, Berkshire, England

  • de Bruijn J, Martin-Recuerda F, Manov D et al (2004) State-of-the-art Survey on Ontology Merging and Aligning V1. Technical Report, D4.2.1 (WP4), EU-IST Integrated Project (IP) IST-2003-506826 SEKT, EU

  • Fountain JE (2002) Information institutions and governance: advancing a Basic Social Science Research Program for Digital Government. Technical Report, National Center for Digital Government, John F. Kennedy School of Government, Harvard University

  • Gallaher MP, O’Connor AC, Dettbarn JL et al (2004) Cost analysis of inadequate inoperability in the capital facilities industry. Technical Report, GCR 04-867, National Institute of Standards and Technology (NIST)

  • Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Hum-Comput Stud 43(5/6):907–928

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York, NY

  • International Alliance for Interoperability (1997) Guidelines for the development of Industry Foundation Classes (IFC). IAI, Muenchen, Germany

  • International Conference of Building Officials (2006) International Building Code 2006. Whittier, CA

  • Kerrigan S (2003) A software infrastructure for regulatory information management and compliance assistance. Ph.D. Thesis, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA

  • Kerrigan S, Law K (2003) Logic-based regulation compliance-assistance. In: Proceedings of the 9th international conference on artificial intelligence and law (ICAIL 2003), Edinburgh, Scotland, pp 126–135

  • Kim M-C, Choi K-S (1999) A comparison of collocation-based similarity measures in query expansion. Inf Process Manag 35(1):19–30

    Article  Google Scholar 

  • Larsen B, Aone C (1999) Fast and effective text mining using linear-time document clustering. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, pp 16–22

  • Lau G (2004) A comparative analysis framework for semi-structured documents, with applications to government regulations. Ph.D. Thesis, Civil and Environmental Engineering, Stanford University, Stanford, CA

  • Lau G, Law K, Wiederhold G (2005) Legal information retrieval and application to E-rulemaking. In: Proceedings of the 10th international conference on artificial intelligence and law (ICAIL 2005), Bologna, Italy, pp 146–154

  • Li J (2004) LOM: a Lexicon-based Ontology Mapping Tool. In: Proceedings of the information interpretation and integration conference (I3CON) and the performance metrics for intelligent systems (PerMIS) workshop, Gaithersburg, MD

  • Li W, Clifton C, Liu S (2000) Database integration using neural network: implementation and experiences. Knowl Inf Syst 2(1):73–96

    Article  MATH  Google Scholar 

  • Lipman R (2006) Mapping between the CIMsteel Integration Standards (CIS/2) and Industry Foundation Classes (IFC) Product Model for Structural Steel. In: Proceedings of the 11th international conference on computing in civil and building engineering, (ICCCBE XI), Montreal, Canada, pp 3087–3096

  • Madhavan J, Bernstein PA, Rahm E (2001) Generic schema matching with cupid. In: Proceedings of the 27th international conference on very large data bases (VLDB), Rome, Italy, pp 49–58

  • Melnik S, Garcia-Molina H, Rahm E (2002) Similarity flooding: a versatile graph matching algorithm. In: Proceedings of the 18th international conference on data engineering (ICDE), San Jose, CA, pp 117–128

  • Miller GA, Beckwith R, Fellbaun C et al (1993) Five papers on WordNet. Technical Report, Cognitive Science Laboratory, Princeton, NJ

  • Milo T, Zohar S (1998) Using schema matching to simplify heterogeneous data translation. In: Proceedings of the 24th international conference on very large data bases, New York, NY, pp 122–133

  • Mitra P (2003) An algebraic framework for the interoperation of ontologies. Ph.D. Thesis, Computer Science Department, Stanford University, Stanford, CA

  • Moens M-F, Uyttendaele C, Dumortier J (1997) Abstracting of legal cases: the SALOMON experience. In: Proceedings of the 6th international conference on artificial intelligence and law (ICAIL 1997), Melbourne, Australia, pp 114–122

  • Noy NF (2003) Tools for mapping and merging ontologies. In: Staab S, Stude R (eds) Handbook on ontologies. Springer-Verlag, Berlin, Heidelberg, pp 365–384

  • Noy NF, Musen MA (2003) The PROMPT suite: interactive tools for ontology merging and mapping. Int J Hum-Comput Stud 59(6):983–1024

    Article  Google Scholar 

  • Palopoli L, Sacca D, Terracina G et al (1999) A unified graph-based framework for deriving nominal interscheme properties, type conflicts and object cluster similarities. In: Proceedings of the 4th IFCIS international conference on cooperative information systems (CoopIS), Edinburgh, Scotland, pp 34–45

  • Porter MF (1980) An algorithm for suffix stripping. Program 14(3):130–137

    Google Scholar 

  • Ray SR (2002) Interoperability standards in the semantic web. J Comput Inf Sci Eng 2(1):65–69

    Article  Google Scholar 

  • Roussinov D, Zhao JL (2003) Automatic discovery of similarity relationships through web mining. Decis Support Syst 25:149–166

    Article  Google Scholar 

  • Salton G (1989) Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc, Boston, MA, USA

    Google Scholar 

  • Schweighofer E, Rauber A, Dittenbach M (2001) Automatic text Representation, classification and labeling in European Law. In: Proceedings of the 8th international conference on artificial intelligence and law (ICAIL 2001), St. Louis, Missouri, pp 78–87

  • Stumme G, Maedche A (2001) Ontology merging for federated ontologies on the semantic web. In: Proceedings of the international workshop on foundations of models for information integration (FMII 2001), Seattle, WA, pp 16–18

  • Thompson P (2001) Automatic categorization of case law. In: Proceedings of the 8th international conference on artificial intelligence and law (ICAIL 2001), St. Louis, Missouri, pp 70–77

  • van Hage W, Katrenko S, Schreiber G (2005) A method to combine linguistic ontology-mapping techniques. In Proceedings of the fourth international semantic web conference (ISWC), Galway, Ireland

  • Wang H, Akinci B, Garrett JH (2007) Formalism for detecting version differences in data models. J Comput Civil Eng 21(5):321–330

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the supports by the US National Science Foundation, Grant No. CMS-0601167 and IIS-0811460, the Center for Integrated Facility Engineering (CIFE) at Stanford University, and the Enterprise Systems Group at the National Institute of Standards and Technology (NIST). The authors would like to thank the International Code Council (ICC) for providing the XML version of the International Building Code 2006. Any opinions and findings are those of the authors, and do not necessarily reflect the views of NSF, CIFE, NIST, or ICC. No approval or endorsement of any commercial product by NIST, NSF, ICC, or Stanford University is intended or implied.

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Authors and Affiliations

  1. Engineering Informatics Group, Department of Civil and Environmental Engineering, Stanford University, Palo Alto, CA, 94305-4020, USA

    Chin Pang Cheng, Gloria T. Lau & Kincho H. Law

  2. School of Civil Engineering, Tsinghua University, Beijing, 100084, China

    Jiayi Pan

  3. Enterprise Systems Group, National Institute of Standards and Technology, Gaithersburg, MD, 20899-0001, USA

    Albert Jones

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  1. Chin Pang Cheng
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  2. Gloria T. Lau
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  3. Kincho H. Law
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  5. Albert Jones
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Correspondence to Chin Pang Cheng.

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Cheng, C.P., Lau, G.T., Law, K.H. et al. Regulation retrieval using industry specific taxonomies. Artif Intell Law 16, 277–303 (2008). https://doi.org/10.1007/s10506-008-9065-5

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