A Carneades reconstruction of Popov v Hayashi

Carneades is an open source argument mapping application and a programming library for building argumentation support tools. In this paper, Carneades?? support for argument reconstruction, evaluation and visualization is illustrated by modeling most of the factual and legal arguments in Popov v Hayashi.     

Representing Popov v Hayashi with dimensions and factors

Modelling reasoning with legal cases has been a central concern of AI and Law since the 1980s. The approach which represents cases as factors and dimensions has been a central part of that work. In this paper I consider how several varieties of the approach can be applied to the interesting case of Popov v Hayashi. After briefly reviewing some of the key landmarks of the approach, the case is represented in terms of factors and dimensions, and further explored using theory construction and argumentation schemes approaches.     

An ontology in OWL for legal case-based reasoning

The paper gives ontologies in the Web Ontology Language (OWL) for Legal Case-based Reasoning (LCBR) systems, giving explicit, formal, and general specifications of a conceptualisation LCBR. Ontologies for different systems allows comparison and contrast between them. OWL ontologies are standardised, machine-readable formats that support automated processing with Semantic Web applications. Intermediate concepts, concepts between base-level concepts and higher level concepts, are central in LCBR. The main issues and their relevance to ontological reasoning and to LCBR are discussed. Two LCBR systems (AS-CATO, which is based on CATO, and IBP) are analysed in terms of basic and intermediate concepts. Central components of the OWL ontologies for these systems are presented, pointing out differences and similarities. The main novelty of the paper is the ontological analysis and representation in OWL of LCBR systems. The paper also emphasises the important issues concerning the representation and reasoning of intermediate concepts.

Reconstructing Popov v. Hayashi in a framework for argumentation with structured arguments and Dungean semantics

In this article the argumentation structure of the court??s decision in the Popov v. Hayashi case is formalised in Prakken??s (Argument Comput 1:93?C124; 2010) abstract framework for argument-based inference with structured arguments. In this framework, arguments are inference trees formed by applying two kinds of inference rules, strict and defeasible rules. Arguments can be attacked in three ways: attacking a premise, attacking a conclusion and attacking an inference. To resolve such conflicts, preferences may be used, which leads to three corresponding kinds of defeat, after which Dung??s (Artif Intell 77:321?C357; 1995) abstract acceptability semantics can be used to evaluate the arguments. In the present paper the abstract framework is instantiated with strict inference rules corresponding to first-order logic and with defeasible inference rules for defeasible modus ponens and various argument schemes. The main techniques used in the formal reconstruction of the case are rule-exception structures and arguments about rule validity. Arguments about socio-legal values and the use of precedent cases are reduced to arguments about rule validity. The tree structure of arguments, with explicit subargument relations between arguments, is used to capture the dependency relations between the elements of the court??s decision.     

OWL本体存储技术研究

对本体的存储介质研究的基础上,深入研究本体在关系数据库中的存储模式,指出当前各种本体存储模式的不足之处,提出了新的基于关系数据库的混合存储模式.用教育领域本体测试框架对所提出的存储模式进行实验验证.实验结果表明,设计的基于关系数据库的存储模式具有结构清楚、查询效率高和扩展性能强的优点.用新的混合存储模式要比目前广泛使用...     

从文本自动构建OWL本体的研究

随着语义检索的发展,近年来涌现了许多基于本体的研究和应用,但本体本身仍离不开领域专家手工或半自动化的构建,成为了本体研究领域的一个瓶颈。因此,本文着眼于本体的自动化构建,提出了一种用FCA(形式概念分析)从文本中提取并自动生成符合W3C标准的OWL通用本体库的方法。解决了目前本体构建自动化程度低,领域依赖性强的问题,使得本体的发展和应用不再是空中楼阁。     

基于OWL的制药企业本体的构建及应用

本体是共享概念模型的明确的形式化规范说明,通过概念之间的关系来描述概念的语义。药业本体是制药企业中或企业间公共的知识化的描述,不仅可以促进制药企业知识的重用和共享,还大大方便了制药企业内部以及企业间知识管理和信息交换。本文讨论了本体的概念,介绍了本体的构建原则和方法,以及各种本体表示语言,着重介绍了OWL本体语言,分析了药业本体应用领域,给出了一个简单的基于OWL的本体示例片段,最后简单介绍了本体编辑工具protege2000及其使用方法。     

A crowdsourcing approach to building a legal ontology from text

This article focuses on the problems of application of artificial intelligence to represent legal knowledge. The volume of legal knowledge used in practice is unusually large, and therefore the ontological knowledge representation is proposed to be used for semantic analysis, presentation and use of common vocabulary, and knowledge integration of problem domain. At the same time some features of legal knowledge representation in Ukraine have been taken into account. The software package has been developed to work with the ontology. The main features of the program complex, which has a Web-based interface and supports multi-user filling of the knowledge base, have been described. The crowdsourcing method is due to be used for filling the knowledge base of legal information. The success of this method is explained by the self-organization principle of information. However, as a result of such collective work a number of errors are identified, which are distributed throughout the structure of the ontology. The results of application of this program complex are discussed in the end of the article and the ways of improvement of the considered technique are planned.     

P2P case storage and retrieval with an unspecified ontology

Traditional approaches for similarity-based retrieval of structured data, such as Case-Based Reasoning (CBR), have been largely implemented using centralized storage systems. In such systems, when the cases contain both numeric and free-text attributes, similarity-based retrieval cannot exploit standard speedup techniques based on multi-dimensional indexing, and the retrieval is implemented by an exhaustive comparison of the case to be solved with the whole set of stored cases. In this work, we review current research on Peer-to-Peer (P2P) and distributed CBR techniques and propose a novel approach for storage of the case-base in a decentralized Peer-to-Peer environment using the notion of Unspecified Ontology to improve the performance of the case retrieval stage and build CBR systems that can scale up to large case-bases. We develop an algorithm for efficient retrieval of approximated most-similar cases, which exploits inherent characteristics of the unspecified ontology in order to improve the performance of the case retrieval stage in the CBR problem solving cycle. The experiments show that the algorithm successfully retrieves cases close to the most-similar cases, while reducing the number of cases to be compared. Hence, it improves the performance of the retrieval stage. Moreover, the distributed nature of our approach eliminates the computational bottleneck and single point of failure of the centralized storage systems.     

A K-way spectral partitioning of an ontology for ontology matching

Ontology matching, the process of resolving heterogeneity between two ontologies consumes a lot of computing memory and time. This problem is exacerbated in large ontology matching tasks. To address the problem of time and space complexity in the matching process, ontology partitioning has been adopted as one of the methods, however, most ontology partitioning algorithms either produce incomplete partitions or are slow in the partitioning process hence eroding the benefits of the partitioning. In this paper, we demonstrate that spectral partitioning of an ontology can generate high quality partitions geared towards ontology matching.     

Towards an ontology modeling tool. A validation in software engineering scenarios

Ontology creation and management related processes are very important to define and develop semantic services. Ontology Engineering is the research field that provides the mechanisms to manage the life cycle of the ontologies. However, the process of building ontologies can be tedious and sometimes exhaustive. OWL-VisMod is a tool designed for developing ontological engineering based on visual analytics conceptual modeling for OWL ontologies life cycle management, supporting both creation and understanding tasks. This paper is devoted to evaluate OWL-VisMod through a set of defined tasks. The same tasks also will be done with the most known tool in Ontology Engineering, Protégé, in order to compare the obtained results and be able to know how is OWL-VisMod perceived for the expert users. The comparison shows that both tools have similar acceptation scores, but OWL-VisMod presents better feelings regarding user’s perception tasks due to the visual analytics influence.     

Searching for arguments: applying a knowledge-based ontology of the legal domain

Legal ontologies are conceptual models of specific parts of the legal domain. They provide stable foundations for domain representation, essential to building legal information systems. The ontology presented in this article provides an analysis of the concept of knowledge in the legal domain. It is shown how new functions for information technology applications in the legal domain can be developed on the basis of this analysis. In particular, a search function distinguishing between the content and structure of legal arguments is described in order to show how the analysis is put into practice.     

用C语言实现类实例化的研究

多年以来,C语言在嵌入式软件开发中被广泛使用,但由于开发人员和应用场景等原因,面向对象、设计模式等优秀的软件开发方法始终没有很好地运用起来。时至今日,物联网等应用的兴起,给嵌入式软件开发带来新的挑战,而传统的面向过程开发已经难以支撑这些复杂的应用。因此,有必要在嵌入式软件开发中引入面向对象、设计模式等优秀的软件开发方法。面向对象是现代软件方法的根基,面向对象体现在类上,使用类来创建对象的过程就是实例化。文章结合C语言的特性,对使用C语言实现类实例化进行了讨论。     

A CBR system for injection mould design based on ontology: A case study

Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of design support in mould design, which is a domain strong in practice but poor in theory. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in design support system. This hinders the application of CBR in injection mould design. In order to develop an intelligent CBR system and meet the need of design support for injection mould design, this paper integrates ontology technology into a CBR system by constructing domain ontology as case-base with a new method, in which two means of acquisition are combined, one is acquiring ontology from existing ontologies, the other from established engineering knowledge resources, and proposing a new semantic retrieval method as the first grade case retrieval. Numerical measurement is also employed as the second grade case retrieval, which adopts various methods to calculate different types of attribute values. A case is executed to illustrate the use of proposed CBR system, then a lot of experiments are organized to evaluate its performance and the result shows that the proposed approach outperforms existing CBR systems.     

Ownership: A case study in the representation of legal concepts

This article is an exercise in computational jurisprudence. It seems clear thatthe field of AI and Law should draw upon the insights of legal philosophers,whenever possible. But can the computational perspective offer anything inreturn? I will explore this question by focusing on the concept of OWNERSHIP,which has been debated in the jurisprudential literature for centuries. Althoughthe intellectual currents here flow mostly in one direction – from legal philosophy to AI – I will show that there are also some insights to be gained from a computational analysis of the OWNERSHIP relation. In particular, the article suggests a computational explanation for the emergence of abstract property rights, divorced from concrete material objects.     

A study of the application of ontology to an FAQ automatic classification system

Many enterprises collect Frequently Asked Questions (FAQs) from their customer service departments or technical support centers and compile the questions and responses in a knowledge database to reuse the information when similar questions occur. However, the categorization of these FAQs is based on human judgment, which can result in inconsistencies. In this paper, an auto-categorizing system is designed and proposed to facilitate a more precise categorization of software-based FAQs for the case subject, S Company.This study applies the system development methodology. First, the domain ontology of an FAQ auto-categorizing system is devised, and categorizing methods and rules are developed with the intention that the system will generate categories automatically.Next, the FAQ auto-categorizing system is tested using a database based on the real FAQs of the study subject Company S, and the system reliability and FAQ searching performance are verified via interviews with the customer service staff and user satisfaction surveys. This study demonstrates that the FAQ auto-categorizing system can automatically generate categories that match the original FAQ attributes, but manual adjustment is still occasionally necessary to optimize the system and boost the accuracy of the categorization.The interviews and user satisfaction surveys show that categorizing according to question attributes is a more efficient method than the method used by the software products available for FAQ search performance. Although users are satisfied with both the FAQ auto-categorizing system and the original FAQ system used by subject Company S, this study discovers that the FAQ auto-categorizing system requires significantly less search time than the original system.     

A transdisciplinary ontology of innovation governance

Intellectual property law tends to be viewed as the only (or most significant) mechanism for achieving policy goals relating to innovation assets. Yet more creative and effective solutions are often available. When analysed from a transdisciplinary perspective, relying on the cooperative efforts of researchers from fields other than law, innovation governance is characterized not simply as the product of legal rules, but as a function of the interaction of legal rules, practices and institutions. When policy-makers seek to identify conditions under which the creation, use and exchange of innovation assets flourishes, care should be taken to focus on this combination of factors. This article describes the development of an ontology—a computerized method of representing knowledge as concepts and relations between concepts—to convey such understanding. Policy makers (and researchers) are provided with an organized, accessible representation of innovation governance that enriches their understanding and improves their decision-making.