Cause of Action and the Right to Know. A Formal Conceptual Analysis of the Texas Senate Bill 25 Case

Bill 25 proposed by the Texas Senate in 2017 was created to eliminate the so-called ‘wrongful birth’ cause of action. This plan raised some questions about the ‘right to know’ and indirectly about rights in general. We provide a preliminary logical analysis investigating these questions by using deontic and epistemic logics within the theory of normative positions. This work contributes to the logic-based legal knowledge representation tradition, and to the formal conceptual analysis of legal rights studying the cause of action’s role in the debated relation between the Hohfeldian categories ‘claim-right’ and ‘power’.

Multiple Agent Based Entailment System(MABES) for RTE

Despite growing needs of the legal artificial intelligence (AI), its development is slower than other AI domains because legal expertise is essentially required to develop legal AI systems. Legal knowledge representation on legal expertise needs to be considered to implement legal reasoning AI systems. In this paper, we present a legal reasoning methodology, which utilizes multiple expert knowledge based agents. These agents are designed to solve recognizing textual entailment (RTE) problems with syntactic and interpretative knowledge. The validity of the proposed method is provided through experiments with the COLIEE 2017 data.

Law Modeling and BPR for Public Administration Improvement

The presence of Information and Communication Technologies (ICTs) is becoming more pronounced in Public Administrations and in the context of legal knowledge management. In most countries, it is now possible for citizens to freely access the text of Parliamentary Acts, bills, judgments, et cetera. Analysts that work on re-engineering public administration processes must take into account all relevant sources of law as they will ultimately be modified in order to legitimize the new processes. This chapter considers the requirements to design a framework for business process re-engineering for public administrations by analyzing the existing systems for legal knowledge representation and interchange and the current technologies to assist modeling and change management of business processes. The ultimate goal is that of supporting the law-making process, facilitating the participation of people without a jurisprudence background to the editing of regulations, by providing effective means to comprehend and observe the law, make changes to the law, and to keep track of the dependencies between the text and the models. The framework presented in this chapter integrates several different and rather mature technologies developed in Europe and in Africa, providing a set of tools applicable to virtually any legal system.

Semantic annotation of Italian legal texts

The FrameNet approach to text semantic annotation can be a reliable model to make the linguistic information and semantic content of legal texts explicit. This hypothesis is discussed and empirically demonstrated through a trial of annotating a corpus of Italian legal texts. This study aims to show that FrameNet is particularly appropriate to provide new perspectives for legal language studies and for legal knowledge representation tasks. Moreover, by relying on the output of a statistical dependency parser, the FrameNet-based annotation methodology presented here can be used successfully in the automatic semantic processing of legal texts.

Distinguishing between Knowledge Visualization and Knowledge Rrpresentation in Legal Informatics

Knowledge visualization (KV) and knowledge representation (KR) are distinguished, though both are knowledge management processes. Knowledge visualization is subject to humans, whereas knowledge representation – to computers. In computing, knowledge representation leverages reasoning of software agents. Thus, KR is a branch of artificial intelligence. The subject matter of KR is representation methods. They are classified into (1) knowledge level and symbol level representations; (2) procedural and declarative representations; (3) logic-based, rule-based, frame- or object-based representations (supporting inference by inheritance); and (4) semantic networks. In legal informatics, methods of legal knowledge representation (LKR) are dealt with. An essential feature of LKR is the representation of deep knowledge, which is mainly tacit. It is easily understood by professional jurists and hardly by amateurs from outside law. This knowledge comprises the teleology of law and a whole implicit framework of legal system. The paper focuses on (1) identifying key features of KV and KR in the legal domain; and (2) distinguishing between visualization, symbolization, formalisation and mind mapping.