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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hrp46vebjrgh7mk6m7icyvwgma" style="color: black;">Artificial Intelligence and Law</a>
7 Abstract. Legal codes, such as the Uniform Commercial Code (UCC) examined in this article, are 8 good points of entry for AI and ontology work because of their more straightforward adaptability 9 to relationship linking and rules-based encoding. However, approaches relying on encoding solely 10 on formal code structure are incomplete, missing the rich experience of practitioner expertise that 11 identifies key relationships and decision criteria often supplied by experienced practitioners and<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10506-007-9027-3">doi:10.1007/s10506-007-9027-3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/owh3bhrjlfevvbgdiydpybmepy">fatcat:owh3bhrjlfevvbgdiydpybmepy</a> </span>
more »... 12 process experts from various disciplines (e.g., sociology, political economics, logistics, operations 13 research). This research focuses on the UCC because it transcends the limitations of a formal code, 14 functioning essentially as a composite. AI work can benefit from real-world codes like the UCC, 15 which are essentially formal codes enlightened from a more realistic experience-base from centuries 16 of development in international commercial transactions settings. This paper then describes our 17 initial work in converting an expert system on the U.S. law governing the sale of goods from 18 Article II of the Uniform Commercial Code (UCC), into a knowledge-based system using the Web 19 Ontology Language OWL. 20 Key words: legal ontology, uniform commercial code 21 22 23 1. Introduction 24 Artificial intelligence (AI) techniques have spread only slowly into the 25 domains of law, regulation and public policy. From time to time, prototype 26 expert systems are devised but many provide, at best, mixed results. The 27 perspective of this research is that artificial intelligence in law is inherently 28 interdisciplinary. Successful projects in artificial intelligence and ontologies 29 require domain expertise in both law and artificial intelligence. Domain 30 expertise in law is derived from two sources: legal experts in the formal law 31 and process theorists representing various disciplines. Codes, such as the 32 Uniform Commercial Code (UCC) examined in this article, are good points 33 of entry for AI and ontology work because of their more straightforward 34 adaptability to relationship linking and rules-based encoding. 35 approaches relying on encoding solely on formal code structure are incom-36 plete, missing the rich experience of practitioner expertise that identifies key 37 relationships and decision criteria often supplied by experienced practitioners 38 and process experts from various disciplines (e.g., sociology, political eco-39 nomics, logistics, operations research). This research focuses on the UCC 40 because it transcends the limitations of many formal codes, functioning 41 essentially as a composite largely due to the UCCÕs rather unique heritage. 42 The UCC was derived from the Law Merchant and Lex Mercatoria, codi-43 fications of actual practice rather than normative codes drafted by inexpe-44 rienced legislators. Therefore, AI work on real-world codes like the UCC is 45 benefited by the straightforward coding advantages of codes but enlightened 46 with a more realistic experience-base from centuries of development in 47 international commercial transactions settings. This paper then describes our 48 initial work in converting an expert system on parts of the law governing the 49 sale of goods, Article II of the Uniform Commercial Code (UCC), into a 50 knowledge-based system using the Web Ontology Language OWL with Jess 51 as our inference engine. 52 2. Related Work 53 Legal ontologies are a key technology enabling semantic representation and 54 reasoning about legal domains (Schweighofer and Liebwald 2005). Research 55 on extending standard ontologies into the legal domain span the range from 56 core ontologies (e.g., LRI-Core, Breuker 2004), normative ontologies (e.g., 57 NM-L, Shaheed et al. 2005), professional legal knowledge ontologies (e.g., 58 OPJK, Casanovas et al. 2005), or focused on sub-domains ontologies such as 59 intellectual property rights (Gil et al. 2005). Additional challenges arise when 60 considering the multi-language aspects of legal terms (Peters et al. 2005). 61 These ontologies provide the ability to incorporate social and organizational 62 roles and responsibilities (Royakkers et al. 2005; Boella and van der Torre 63 2005), causal relationships (Hoekstra and Breuker 2005), and norms (Boer 64 et al. 2005) are required to support sound representation and reasoning. In 65 our UCC domain, the ability to represent and reason about roles is crucial. 66 Buyers and sellers, merchants and non-merchants have different roles, rights, 67 and responsibilities in commercial transactions. For example, merchants are 68 assumed to have more knowledge and resources to anticipate and to address 69 any issues that arise during commercial activities. In addition, commercial 70 activities generally involve collective organizational obligations. Hafner 71 (Hafner 1987) has described aspects of conceptual organization necessary in 72 the UCC domain, including the domain knowledge model. Finally, legal 73 tools and methodologies are needed to support the general adoption of this 74 research. The eGanges system (Gray 2005) provides a legal expert shell h LE h TYPESET MS Code : ARTI 13 h CP h DISK 44 U N C O R R E C T E D P R O O F 75 environment, and LODE (Aoki et al. 1998) is a legal ontology development 76 tool. TERMINAE provides a construction methodology (Despres and 77 Szulman 2005) for composing micro-ontologies into a single composite 78 ontology. LawBot uses agents and ontology-augmented search to help those 79 outside the legal profession acquire legal information (Debnath et al. 2000). 80 3. Some Commercial Successes in Developing Legal Ontologies 81 Despite the enormous hurdles to comprehensive and robust AI in the 82 domains of law, regulation and public policy, some interesting experiments 83 have been conducted and a few notable functional systems are operative. 84 For example, there are some complex but deterministic systems successfully 85 deployed in specific sub-domains of law, regulation and public policy. Con-86 sider the rules-based systems in commercially available tax preparation 87 applications, some running as native software and others successfully oper-88 ating from online applications service providers (ASP) -the latter including 89 government as the ASP: the United States (U.S.) Internal Revenue Service 90 (IRS). Rather considerable progress in user assistance has characterized the 91 primary legal research databases (Lexis, Westlaw) in the U.S. Online legal 92 databases leverage the traditional categories in law and regulation, develop 93 and deploy cross-reference links, expand computer-aided search through 94 natural language, filters and sense-making. Moving from legal categories to 95 legal ontolgies is a non-trival task that may be supported through the use of 96 XML (Lachmayer and Hoffman 2005; Biagioli and Turchi 2005) . Finally, 97 there are numerous electronic government transaction processing systems 98 throughout the world. For example, many taxing authorities assist taxpayers 99 with AI technologies, licensing authorities process transactions, intellectual 100 property (IP) authorities provide research assistance and manage complex 101 processing of application transactions, grants of rights, ownership search, etc. 102 New services developed by legal research databases may be good predic-103 tors of successful AI and ontology work in law for three reasons. First, they 104 already have deployed AI research assistance as discussed above. Second, as 105 private-sector, for profit information service providers, they can be expected 106 to invest in AI innovation where there is reliable cash flow potential. Third, 107 they are already fulfilling the promise of AI in large, complex environments 108 by providing context-sensitive advice on information seeking, including 109 significant access to actual reliable sources. For example, the online legal 110 database services have mechanized and are enhancing traditional finding 111 strategies, although largely using variations and context sensitive enhance-112 ments of key word in context search and retrieval. Nevertheless, these ser-113 vices are adding functionality, such as natural language queries rather than 114 exclusively traditional Boolean approaches, with relevance prioritization and
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