Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset

Natural language processing techniques contribute more and more in analyzing legal documents recently, which supports the implementation of laws and rules using computers. Previous approaches in representing a legal sentence often based on logical patterns that illustrate the relations between concepts in the sentence, often consist of multiple words. Those representations cause the lack of semantic information at the word level. In our work, we aim to tackle such shortcomings by representing legal texts in the form of abstract meaning representation (AMR), a graph-based semantic representation that gains lots of polarity in NLP community recently. We present our study in AMR Parsing (producing AMR from natural language) and AMR-to-text Generation (producing natural language from AMR) specifically for legal domain. We also introduce JCivilCode, a human-annotated legal AMR dataset which was created and verified by a group of linguistic and legal experts. We conduct an empirical evaluation of various approaches in parsing and generating AMR on our own dataset and show the current challenges. Based on our observation, we propose our domain adaptation method applying in the training phase and decoding phase of a neural AMR-to-text generation model. Our method improves the quality of text generated from AMR graph compared to the baseline model. (This work is extended from our two previous papers: “An Empirical Evaluation of AMR Parsing for Legal Documents”, published in the Twelfth International Workshop on Juris-informatics (JURISIN) 2018; and “Legal Text Generation from Abstract Meaning Representation”, published in the 32nd International Conference on Legal Knowledge and Information Systems (JURIX) 2019.).

     

Institutionalizing expert systems: Guidelines and legal concerns

Often, knowledge engineers become so involved in the development process of the expert system that they fail to look further down the road toward the expert system's institutionalization within the organization. Institutionalization is an important component of the expert system planning process. More specifically, the legal issues associated with expert systems development and deployment are critical institutionalization factors. This paper looks at some expert system institutionalization guidelines, and then focuses on legal considerations. Adapted from Zeide, J. S. and J. Liebowitz, “Institutionalizing Expert Systems: Guidelines and Legal Concerns,” Proceedings of FLAIRS-92 Conference, Ft. Lauderdale, FL.     

基于本体的法律知识库的研究与实现

基于描述逻辑的本体理论已广泛用于知识表示和推理，而本体在法律领域的研究和应用在国外已开展近10年，目前国内、还没有这方面的研究与应用成果。本文研究分析了国内外法律知识库的成果，结合中国的法律法规给出了一个法律领域的本体模型DOLegal，介绍了法律知识库的设计思路及架构。在知识库原型中加入了国内部分法规涉及的行为及处罚，实现了法律知识的表示和推理。文中还对设计中遇到的一些难点进行了研究与分析。     

Expert identification and selection: Legal liability concerns and directions

Legal liabilities pertaining to the identification and selection of domain experts is an issue that could adversely impact expert systems developers. Problems pertaining to flawed knowledge, improperly defined expertise, and behavioural and psychological impediments are just some of the issues. This paper examines the torts of strict products liability and negligence that system developers could incur as a result of expert-related difficulties. Parallels from legal scholars and federal and state court decisions are discussed relevant to expert system projects and developers. The paper concludes with a presentation of steps that systems developers can take to minimize potential legal liability.     

HAUSS: Incrementally building a summarizer combining multiple techniques

The idea of automatic summarization dates back to 1958, when Luhn invented the “auto abstract” (Luhn, 1958). Since then, many diverse automatic summarization approaches have been proposed, but no single technique has solved the increasingly urgent need for automatic summarization. Rather than proposing one more such technique, we suggest that the best solution is likely a system able to combine multiple summarization techniques, as required by the type of documents being summarized. Thus, this paper presents HAUSS: a framework to quickly build specialized summarizers, integrating several base techniques into a single approach. To recognize relevant text fragments, rules are created that combine frequency, centrality, citation and linguistic information in a context-dependent way. An incremental knowledge acquisition framework strongly supports the creation of these rules, using a training corpus to guide rule acquisition, and produce a powerful knowledge base specific to the domain. Using HAUSS, we created a knowledge base for catchphrase extraction in legal text. The system outperforms existing state-of-the-art general-purpose summarizers and machine learning approaches. Legal experts rated the extracted summaries similar to the original catchphrases given by the court. Our investigation of knowledge acquisition methods for summarization therefore demonstrates that it is possible to quickly create effective special-purpose summarizers, which combine multiple techniques, into a single context-aware approach.     

Brussels revisited: The European conflicts guide

In this paper, we examine the soundness of Capper & Susskind's recommended legal expert system development methodology in the areas of knowledge acquisition and knowledge representation. Legal expert systems have not yet had the impact in the United Kingdom that might be expected. We argue that this is partly the result of developers paying insufficient attention to the ‘third estate’: user interfaces. We make suggestions about both the look and feel of legal expert systems, and the facilities that such systems should offer. Lastly, we claim that we have developed an exploratory expert system encapsulating the Brussels Convention 1968 which can contribute to the development of a useful computer‐based guide to an important legal domain.     

The elements of an open KBS infrastructure

This paper describes the elements of an open knowledge based system infrastructure. These are: standard knowledge representation systems; a knowledge interchange format; a knowledge manipulation and query language; common shared ontology and an agent based software engineering model. These elements provide the means to achieve Knowledge Sharing and Reuse between heterogeneous knowledge based systems. The elements have been adopted as the basis of the conceptual design for the Advanced Technology Operations System ATOS being designed by the European Space Agency ESA.     

An emerging – Social and emerging computing enabled philosophical paradigm for collaborative learning systems: Toward high effective next generation learning systems for the knowledge society

The vast evolution of Social Computing in the last years and the tremendous improvement of novel technologies including cloud computing, open source technologies, recommender systems, personalized knowledge management systems, Big Data Systems, and Open Educational Resources approaches set a challenging context for the establishment of novel high effective approaches to Collaborative learning in both Business and Academia.This editorial provides an overview of a magnificent top quality research collection of articles related to the New Generation Collaborative Learning Systems. It is an opportunity for a scientific debate for the enabling technologies and the required adjustments in Academic Programs and Executives Training programs worldwide. It is a bold contribution to a new philosophical paradigm for the need to promote flexible, open, collaborative learning beyond time, personality, and place constraints. It seems that the old fashioned classroom based learning has to be enriched or in some cases replaced by technological learning innovations fostering collaboration between learners.Another important contribution of this special issue is the in depth discussion of a variety of requirements for next generation learning systems. This can be extremely useful for researchers interested on future research on the domain. Two more special issues on prestigious journals have been confirmed on similar topics for the next year in order to provide a continuity on this fascinating research domain that is directly linked to the vision of the Knowledge Society.     

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.     

试探性知识的表示及其解题算法

知识表示是研究工程规范管理系统首要解决的问题，本文提出用状态知识元与决策表相结合来表示试探性工程规范知识，并介绍了基于这一结构的推理方法。     

Logic knowledge bases with two default rules

Logic knowledge based systems (LKBS) containing at most one form of default negation and explicit (or “classical”) negation have been studied in the literature. In this paper we describe a class of LKBS containing multiple forms of default negation in addition to explicit negation. We define a semantics for these systems in terms of the well‐founded semantics defined by Van Gelder et al. (1988) and the stable semantics introduced by Gelfond and Lifschitz (1988) and later extended to the 3‐valued case by Przymusinski (1991). We investigate properties of the new combined semantics and calculate the computational complexity of three main reasoning tasks for this semantics, namely existence of models, skeptical and credulous reasoning. An effective procedure to construct the collection of models characterizing the semantics of such a system is given. Applications to knowledge representation and knowledge base merging are presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

知识链模型研究

知识链定义为有限个知识资源组成的有序集合。从“过程”的观点来看,伴随业务过程而存在的一个流动的“知识过程”,也就是知识链。知识链映射业务过程,支持并主导业务过程,更提升、激励业务过程。知识链模型包含了知识资源生产过程和知识资源应用过程。前者定义为知识生产链,其操作包括知识资源的挖掘、表示和存储。后者定义为知识应用链,其操作包括知识资源的识别和选用、执行、评估。一个完备、健壮的知识链模型可以通过定义知识链单元的特性参数来评估,以保证知识链操作过程的高效率、低成本和负荷平衡。     

Causal design knowledge: Alternative representation method for product development knowledge management

This paper presents a mathematical comparison of procedural knowledge and causal knowledge, and discusses the potential roles and feasibility of causal knowledge across product development knowledge management. Since reuse of knowledge is so important in product development, various knowledge management approaches have been introduced. Most of the product design knowledge is represented by procedural knowledge, which unfortunately requires cumbersome processes to define, and is typically inadequate for representing the kind of knowledge generated during the product development process. A causal knowledge representation, however, can help us to overcome this limitation and is an alternative formalism for representing product design knowledge. In this paper we compare the procedural and causal knowledge representations. We present the mathematical definitions of two knowledge paradigms, then mathematically describe the relationship between the two. Both knowledge paradigms are then compared based on the perspective of knowledge expression, decision alternative representation, reasoning capability, and knowledge cultivation. This paper concludes that causal knowledge representation is superior to procedural knowledge representation based on the four perspectives. Finally, the knowledge systems are modeled using Systems Modeling Language (SysML), and we present a case study that demonstrates the causal knowledge features using a realistic example from industry.     

A knowledge engineering approach to knowledge management

Knowledge management facilitates the capture, storage, and dissemination of knowledge using information technology. Methods for managing knowledge have become an important issue in the past few decades, and the KM community has developed a wide range of technologies and applications for both academic research and practical applications. In this paper, we propose a knowledge engineering approach (KMKE) to knowledge management. First, a knowledge modeling approach is used to organize and express various types of knowledge in a unified knowledge representation. Second, a verification mechanism is used to verify knowledge models based on the formal semantics of the knowledge representation. Third, knowledge models are classified and stored in a hierarchical ontology system. Fourth, a knowledge query language is designed to enhance the dissemination of knowledge. Finally, a knowledge update process is applied to modify the knowledge storage with respect to users’ needs. A knowledge management system for computer repair is used as an illustrative example.     

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.     

Facilitating educators’ knowledge sharing with dedicated Information Systems

The process of knowledge sharing can be seen as the lifeblood for the establishment of a true Knowledge Society. Such a society must be grounded on an iterative process whereby existing knowledge is constantly shared, consolidated and – crucially – enriched with new knowledge. And knowledge sharing is the means by which that enrichment can take place. Without doubt, these processes play a vital role in the context of education, which ultimately has a strong bearing on the formation of the Knowledge Society. In this paper, knowledge sharing among educators is discussed within the context of long-term research work that the authors have carried out in the field. The paper reports on the design and development of a series of Information Systems conceived for the sharing of practices among educational practitioners. It discusses the thinking behind these different but related systems and examines how this is borne out in the implementation of key system features.     

专家系统中不确定性知识的表示和处理

知识表示和处理是专家系统的基本问题，不确定性知识的表示和处理一直是专家系统研究的热点。本文在阐述不确定性知识概念的基础上，简单介绍传统的不确定知识表示和处理方法，重点讨论近年来出现的新的不确定知识表示和处理方法。     

Conceptual framework for document semantic modelling: an application to document and knowledge management in the legal domain

The main contribution of this paper is to lay down a conceptual framework for document semantics modeling. This framework provides a generic graphical knowledge representation model based on Sowa’s conceptual structures. Modeling primitives are introduced to represent factual and ontological knowledge that can be expressed in electronic documents. Binding features are proposed so as to keep knowledge representation and knowledge formulation linked together.

This framework may be applied to various domains and may accept, for this purpose, many different ontological extensions. Thus an extension is provided so as to properly handle the particular kind of knowledge encountered in the legal domain.     

Prajna: Adding Automated Reasoning to the Visual- Analysis Process

Applications and systems can represent knowledge in various ways. Graphic displays might help a data analyst infer new information through interactive visualizations. Knowledge represented as a collection of facts can be used for automatic inference, although it might be represented or stored in various archives, such as databases or formatted files. Developers who create applications for knowledge representation frequently must contend with not only data challenges but also challenges caused by a wide variety of software toolkits, architectures, and standards for knowledge representation. To overcome these obstacles, Vision Systems & Technology, Inc. initiated the Prajna project. The result was a Java toolkit designed to provide various capabilities for visualization, knowledge representation, geographic displays, semantic reasoning, and data fusion. This article is part of a special issue on knowledge-assisted visualization.