scholarly journals Using the Unified Foundational Ontology (UFO) for Grounding Legal Domain Ontologies

2017 ◽  
Author(s):  
Mirna El Ghosh ◽  
Habib Abdulrab ◽  
Hala Naja ◽  
Mohamad Khalil
Keyword(s):  
Foundational Ontology ◽  
Domain Ontologies ◽  
Legal Domain

CargO-S: A pattern-based well-founded legal domain ontology for the traceability of goods in logistic sea corridors

2021 ◽  
pp. 1-40
Author(s):  
Mirna El Ghosh ◽  
Habib Abdulrab
Keyword(s):  
Development Process ◽  
Conceptual Modeling ◽  
Domain Ontology ◽  
Ontology Engineering ◽  
Legal Rules ◽  
Evaluation Approach ◽  
Foundational Ontology ◽  
Core Ontology ◽  
Legal Domain ◽  
Ontology Reuse

Building legal domain ontologies is a prominent challenge in the ontology engineering community. The ontology builders confront issues such as the complexity of the legal domain, the difficulty of applying existing ontology engineering approaches, and the intention of developing legal models faithful to realities. In this paper, we discuss constructing a well-founded legal domain ontology, named CargO-S, for the traceability of goods in logistic sea corridors. For building CargO-S, a pattern-oriented approach is applied, supported by ontology-driven conceptual modeling, ontology layering, and ontology reuse processes. CargO-S is grounded in the unified foundational ontology UFO by using the ontology-driven conceptual modeling language OntoUML. Besides, ontology layering is proposed to simplify the development process by dividing CargO-S into three layers located at different granularity levels: upper, core, and domain. For building the upper and core layers, conceptual ontology patterns are reused from the foundational ontology UFO and the legal core ontology UFO-L. These patterns are applied, either by extension or analogy with legal rules, for building the domain layer. CargO-S is then validated by implementing the ontology as OWL and SWRL rules. Finally, the performance and the semantic accuracy of CargO-S are evaluated using a dual evaluation approach.

The Powers Metaphysic

2019 ◽  
Author(s):  
Neil E. Williams
Keyword(s):  
Potential Interaction ◽  
Status Quo ◽  
Repeated Application ◽  
Fundamental Ontology ◽  
The World ◽  
The Status ◽  
Foundational Ontology ◽  
Interaction Types

Systematic metaphysics is defined by its task of solving metaphysical problems through the repeated application of a single, fundamental ontology. The dominant contemporary metaphysic is that of neo-Humeanism, built on a static ontology typified by its rejection of basic causal and modal features. This book offers and develops a radically distinct metaphysic, one that turns the status quo on its head. Starting with a foundational ontology of inherently causal properties known as ‘powers’, a metaphysic is developed that appeals to powers in explanations of causation, persistence, laws, and modality. Powers are properties that have their causal natures internal to them: they are responsible for the effects in the world. A unique account of powers is developed that understands this internal nature in terms of a blueprint of potential interaction types. After the presentation of the powers ontology, it is put to work in offering solutions to broad metaphysical puzzles, some of which take on different forms in light of the new tools that are available. The defence of the ontology comes from the virtues of metaphysic it can be used to develop. Particular attention is paid to the problems of causation and persistence, simultaneously solving them as it casts them in a new light. The resultant powers metaphysic is offered as a systematic alternative to neo-Humeanism.

AI Applications to the Law Domain in Japan

2020 ◽  
Vol 7 (3) ◽  
pp. 471-494
Author(s):  
Katsumi NITTA ◽  
Ken SATOH
Keyword(s):  
Expert Systems ◽  
Information And Communication Technologies ◽  
Language Processing ◽  
Text Processing ◽  
Research Area ◽  
Legal Information ◽  
Research Activities ◽  
Legal Expert ◽  
Legal Domain ◽  
Legal Expert Systems

AbstractArtificial intelligence (AI) and law is an AI research area that has a history spanning more than 50 years. In the early stages, several legal-expert systems were developed. Legal-expert systems are tools designed to realize fair judgments in court. In addition to this research, as information and communication technologies and AI technologies have progressed, AI and law has broadened its view from legal-expert systems to legal analytics and, recently, a lot of machine-learning and text-processing techniques have been employed to analyze legal information. The research trends are the same in Japan as well and not only people involved with legal-expert systems, but also those involved with natural language processing as well as lawyers have become interested in AI and law. This report introduces the history of and the research activities on applying AI to the legal domain in Japan.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

Equality of Treatment, Opportunity, and Outcomes: Mapping the Law

2021 ◽  
Author(s):  
Alain Klarsfeld ◽  
Gaëlle Cachat-Rosset
Keyword(s):  
Data Collection ◽  
Time Frame ◽  
Equal Treatment ◽  
Nation State ◽  
Same Sex ◽  
The Public ◽  
Legal Foundations ◽  
Legal Domain ◽  
The Continuum ◽  
Strict Equality

Equality is a concept open to many interpretations in the legal domain, with equality as equal treatment dominating the scene in the bureaucratic nation-state. But there are many possibilities offered by legal instruments to go beyond strict equality of treatment, in order to ensure equality of opportunity (a somehow nebulous concept) and equality of outcomes. Legislation can be sorted along a continuum, from the most discriminatory ones (“negative discrimination laws”) such as laws that prescribe prison sentences for people accused of being in same-sex relationships, to the most protective ones, labeled as “mandated outcome laws” (i.e., laws that prescribe quotas for designated groups) through “legal vacuum” (when laws neither discriminate nor protect), “restricted equal treatment” (when data collection by employers to monitor progress is forbidden or restricted), “equal treatment” (treating everyone the same with no consideration for outcomes), “encouraged progress” (when data collection to monitor progress on specific outcomes is mandatory for employers), and mandated progress (when goals have to be fixed and reached within a defined time frame on specified outcomes). Specific countries’ national legislation testify that some countries moved gradually along the continuum by introducing laws of increasing mandate, while (a few) others introduced outcome mandates directly and early on, as part of their core legal foundations. The public sector tends to be more protective than the private sector. A major hurdle in most countries is the enforcement of equality laws, mostly relying on individuals initiating litigation.

Acquisitive Information Extraction Framework for Legal Domain

2020 ◽  
pp. 70-73
Author(s):  
Mehran Kamkarhaghighi ◽  
Afsaneh Towhidi ◽  
Masoud Makrehchi
Keyword(s):  
Information Extraction ◽  
Legal Domain
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