Legal Ontology Research Articles

Enhancing legal research through knowledge-infused information retrieval for Vietnamese labor law

<p>The role of intelligent information retrieval systems in legal research optimization has become increasingly recognized. There are many methods for exhibiting advance mentsin the proficient retrieval of legal documents. However, those methods fail to tackle the specific challenges encountered in real-world labor law searches. This research breaks new ground in Vietnamese labor law retrieval by leveraging a comprehensive dataset of 300,000 documents a cross diverse cat egories (20 document types and 27 legal fields) to train and evaluate retrieval models specifically designed for Vietnamese labor law. Unlike previous approaches, this work goes beyond simple information retrieval. It also constructs question & answer (Q&A) dataset specifically tailored to this legal domain. Be sides, this study introduces a novel approach of in corporating a legal ontology built from the data set itself. This knowledge in fusion significantly improves retrieval performance across legal search tasks, as demon strated through rigorous experimentation. These advancements empower intelligent systems to grasp the intricate semantic nuances of Vietnamese labor law.</p>

Розширення можливостей онтологічного моделювання правових знань з допомогою елементів нечіткої логіки

Introduction. Ontological analysis is a significant area in the field of intelligent information technologies, particularly in the development of legal systems where there is a continuous need for efficient management and exchange of legal knowledge. Due to the complexity of legal systems, the application of semantic technologies allows for formalizing legal concepts, simplifying the development of ontological models for representing legal knowledge, and integrating heterogeneous legal information systems. Additionally, incorporating fuzzy logic is essential for handling uncertainty and incompleteness in legal information. The purpose of the paper is to develop a legal ontology model capable of efficiently processing ambiguous legal terms and concepts while automating the classification and analysis of legal documents. The primary objective is to create a flexible system for formalizing legal knowledge that accounts for the specifics of legal acts, enhances the law enforcement process, and supports informed decision-making. Methods. The study employs semantic ontological modeling methods to create legal ontologies and fuzzy logic methods for processing vague and incomplete data. Modern tools and ontology development languages, such as Protege and OWL (Web Ontology Language), are used alongside machine learning techniques for classifying and analyzing legal texts. The approach also explores integrating fuzzy logic elements for evaluating document similarity and representing complex legal concepts. Results. A legal ontology model was developed to automate the classification and analysis of legal terms, concepts, and their relationships. The proposed methodology enables the system to extract information from various legal sources and analyze legal documents while addressing ambiguous data. Testing demonstrated improved classification accuracy and increased efficiency in retrieving legal norms from large volumes of unstructured data. Conclusions. The proposed legal ontology model, incorporating elements of fuzzy logic, significantly enhances the representation and processing of legal knowledge. The methodology includes grammar analysis and the construction of document ontological models, allowing for more precise comparisons of document similarities and differences. The semantic approach proved more effective than the k-means clustering method for key phrase classification. Integrating fuzzy sets into the ontology model facilitates the description of imprecise information and supports reasoning with varying levels of completeness. Ongoing work aims to expand the Ukrainian-language version of the legal ontology to address practical challenges in knowledge-based legal systems. The obtained results serve as a foundation for further advancements in intelligent information systems within the legal domain. Keywords: fuzzy reasoning, fuzzy logic, text analysis, model, decision-making, clustering method, legal knowledge textual content, data processing, knowledge representation, OWL, intelligent information systems.

A systematic mapping study of applying artificial intelligence and data analytics in law and jurisdiction: legal ontologies based on technology

The use of technology in legal reasoning has become the mainstream direction of legalsystems and governments. Especially, higher quality services provided by artificial intelligence and bigdata technologies enable lawyers and government officials to reduce the risk of inappropriatelegislation and act efficiently. Although a significant number of researches have been conducted sofar in that area, there is still an increasing need for structuring knowledge ontologies about the use ofartificial intelligence and data analysis in law and jurisdiction. Therefore, this paper aims to provide acomprehensive overview of applications of those higher-level technologies in legal reasoning andprocessing by identifying, classifying, and evaluating existing literature related to that area of research.The main procedures of this study include planning of the study (definitions of research questions andthe scope and establishment of the search strategy and selection criteria) and conducting of the study(analysis & mapping, data extraction & classification, selection of primary studies, and searchexecution). As a result of this systematic mapping study, the findings include a classified portfolio ofpublications, higher level mapping of the structure of existing literature, and visualized status of thetarget research area about pre-established research questions. Based on the findings of thissystematic study, the author draws implications and guidelines for lawyers and government officialswho have an interest in using such first-class technologies in their jobs. Finally, this article also providesresearch agenda to fill in the gaps in this knowledge domain and spot research trends.

Theoretical and Legal Characteristics of Machine-Readable Law

The idea of automating law and legal activity has become increasingly popular in the scientific legal environment in recent years. One of the ways of such automation is to transform legislation into a machine-readable form. This could provide the possibility of automatic application of the law, increase legal certainty, and make the law accessible to the user. The adoption in 2021 of the Concept for the Development of Machine-Readable Law Technologies became one of important steps towards the introduction of machine-readable law in Russia. It seems that for the successful development of this area, we need a comprehensive theoretical and legal understanding of a new phenomenon for the domestic legal theory, namely: machine-readable law. Using formal logical methods, the author gives a definition of machine-readable law based on the essential and necessary features of the phenomenon under consideration. The paper provides a list of mandatory and optional features of machine-readable law. Special attention is paid to the issues of algorithmization of law and the use of legal ontologies. The paper highlights the main consequences of machine-readable law relating both to the law-making stage and to the stage of using machine-readable law. Such consequences include various types of testability of machine-readable law, the possibility of automatic interpretation, enforcement and direct implementation of machine-readable legal regulations. The author provides the concept of machine-readable law in the broadest sense of the word, covering not only legal regulations, but also the content of smart contracts, machine-readable powers of attorney. The paper also deals with the issues of machine-executable law and the related transition from legal normativity to technological normativity. This work contributes to the theoretical and legal understanding of machine-readable law and can contribute to its successful development in Russia.

A deflationary approach to legal ontology

Contra recent, inflationary views, the paper submits a deflationary approach to legal ontology. It argues, in particular, that to answer ontological questions about legal entities, we only need conceptual analysis and empirical investigation. In developing this proposal, it follows Amie Thomasson’s ‘easy ontology’ and her strategy for answering whether ordinary objects exist. The purpose of this is to advance a theory that, on the one hand, does not fall prey to sceptical views about legal reality (viz., that ontological truths about legal entities are established by metaphysical principles); and, on the other, is compatible with common-sense jurisprudence (viz., that there in fact exist legal entities, and they all have important practical implications). While this methodology is common to legal philosophers interested in elucidating the ‘artifactual nature’ of law, the paper departs from their projects by taking a step forward in connecting this deflationary approach to legal ontology with an expressivist account of legal discourse, thus providing enough resources to satisfy the general ambition of jurisprudence, viz., to establish the conditions for the analysis of legal reality and its normative character.

David J. Gunkel, "Person, Thing, Robot: A Moral and Legal Ontology for the 21st Century and Beyond"

David J. Gunkel, "Person, Thing, Robot: A Moral and Legal Ontology for the 21st Century and Beyond"

Path Updating of the Legal Ontology Model for Personal Information Protection in the Age of Big Data

Abstract This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.

Ontological Relations of the 1945 Basic Act as the Constitution with Pancasila as the State Ideology

This article discusses the relationship between Pancasila and the UUD 1945 as a constitution and laws, implementing regulations and policies to see its ontological gaps. The discussion focused on the constitutional phenomenon, which must elaborate on constructing the 1945 Basic Act and Pancasila. This article aims to see the dimensions of legal ontology related to the understanding and practice of justice in the country and state. The method used is to apply a content analysis approach, which identifies patterns, themes, or meanings that arise from the concept material contained in Pancasila and the 1945 Basic Act. Hail from this research is found ontological problems that need deeper attention. Given the overly broad ontological nature, further research is needed to identify deeper problems. This research has significant significance because it can be a marker of problems at the philosophical level that need attention. Empowerment.

David J. Gunkel, Person, Thing, Robot: A Moral and Legal Ontology for the 21st Century and Beyond

David J. Gunkel, Person, Thing, Robot: A Moral and Legal Ontology for the 21st Century and Beyond

From semantic weight to legal ontology via classification of concepts in legal texts

ABSTRACT A difficulty with legal vocabulary is that concepts in different legal systems map imperfectly to each other, particularly from common law systems where classification is often unclear or convoluted to codified civil systems. Even within the English language domain there are numerous legal systems where concepts differ, e.g. between Scotland and England. This causes significant problems for students’, especially foreign language students’, reading comprehension and developing understanding of legal lexis where translation dictionaries, while they may be efficient, are imperfect. This article sets out a classification approach to reading and English language legal concept deep understanding rooted in theories from education and cognitive linguistics, in particular Categories and Prototypes, Schema theory, and Legitimation Code Theory (LCT). I have used the approach successfully for some years with international students studying law in Scotland, assisting their reading of textbooks and especially journal articles while building domain knowledge. The application of the strategy is presented in the context of research on the adoption of reading strategies which finds that adoption is influenced by awareness of the complexity of the concepts in the text, complexity of the strategy, and by how much particular strategies are seen as a valid method in legal study.

Semantic Representation of the Intersection of Criminal Law & Civil Tort

The more complex and globalized social structures become, the greater the need for new ways of exchanging information and knowledge. Legal science is a field that needs to be codified to allow the interoperability between people and states, as well as between humans and machines. The objective of this work is to develop an ontology in order to describe two different pillars of codified law (civil and criminal) and be able to depict the interaction between them. To answer the above question, we examine the Greek Criminal Law as depicted in the Greek Penal Code (ΠΚ) and the way its articles can be analyzed. Then we examine Tort as described in the Greek Civil Code (AΚ) and link the two codifications through the concepts of illegality and damage, both being prerequisites of tortious liability. Following that, through the Protégé application, a legal ontology is created in the OWL semantic language, while finally, four articles of the Penal Code are codified in the ontology and a presentation of their relation to the civil tort is required from a reasoning algorithm.

Legal Information Retrieval System with Entity-Based Query Expansion: Case study in Traffic Accident Litigation

This article describes an information retrieval system with entity query expansion by relevance feedback.The performance of the system is tested assuming its usage as a support tool for lawyers constructing a legalframework for a case. The objective is to improve the precision of results when searching for relevantjurisprudence. For this, the entities belonging to a knowledge base are used as a means to expand thequery. The expansion can be done using either an automatic or an interactive mechanism. This secondapproach suggests to the user concepts related to the query, which might improve the search experience. Anontology and a knowledge base, called LegalOnto and LegalBase, respectively, were developed. The ontology includes concepts not addressed by existing legal ontologies, and the knowledge base integrates LegalOnto with the thesaurus of the Argentine System of Legal Information (Sistema Argentino de Informacion ´Jur´ıdica: SAIJ), enriched in the subject of traffic accidents. Quantitative experimentation is carried out upon aset of court documents that are used to populate the knowledge base. Preliminary results are encouraging.

Analogical lightweight ontology of EU criminal procedural rights in judicial cooperation

This article describes the creation of a lightweight ontology of European Union (EU) criminal procedural rights in judicial cooperation. The ontology is intended to help legal practitioners understand the precise contextual meaning of terms as well as helping to inform the creation of a rule ontology of criminal procedural rights in judicial cooperation. In particular, we started from the problem that directives sometimes do not contain articles dedicated to definitions. This issue provided us with an opportunity to explore a phenomenon typically neglected in the construction of domain-specific legal ontologies. Whether classical definitions are present or absent, laws and legal sources in general are typically peppered with a number of hidden definitions (in the sense that they are not clearly marked out as such) as well as incomplete definitions, which may nevertheless help legal practitioners (and legal reasoning systems) to reason on the basis of analogy or teleology. In this article we describe the theoretical basis for building an analogical lightweight ontology in the framework of an EU project called CrossJustice. We present our methodology for collecting the data, extracting the data fields and creating the ontology with WebProtégé, followed by our conclusions and ideas for future work.

Named Entity Recognition for Public Interest Litigation Based on a Deep Contextualized Pretraining Approach

The named entity recognition (NER) in the field of public interest litigation can assist prosecutors in handling cases and provide them with specific entities in making legal documents. Previously, the context-free deep learning model is used to catch the semantic comprehension, in which the static word vector is obtained without considering the context. Moreover, this kind of method relies on word segmentation technology and cannot solve the error transmission caused by word segmentation inaccuracy, which brings great challenges to the Chinese NER task. To tackle the above issues, an entity recognition method based on pretraining is proposed. First, based on the basic entities, three legal ontologies, NERP, NERCGP, and NERFPP are developed to expand the named entity recognition corpus in the judicial field. Second, a variant of the pretrained model BERT (Bidirectional Encoder Representations from Transformer) called BERT-WWM (whole-word mask)-EXT(extra) is introduced to catch the text character-level word vector hierarchical and the context bidirectional features, which effectively solve the problem of task boundary division of named entities. Then, to further improve the model recognition effect, the general knowledge learned from the pretrained model is used to fit the downstream neural network BiLSTM (bi-long short-term memory), and at the end of the architecture, CRF (conditional random fields) is introduced to restrict the label relationship. Finally, the experimental results show that the proposed method is more effective than the existing methods, which reach 96% and 90% in the F1 index of NER and NERP entities, respectively.

Legal Text Mining

Abstract: The research and advancements in the field of “legal informatics” has resulted into development of various legal databases. Thus, huge volume of legal information gets generated. This information growth has accelerated the need to develop legal ontologies. Ontologies are widely used by legal practitioners, researchers and ordinary citizens for simulating legal actions, performing linguistics search and classification, and to stay up-to-date with the continual modification of laws. This research contributes to this purpose of developing an open legal ontology

Toward a practical legal rule revision in legal debugging

Computational law researchers have long been interested in developing machine legal reasoning systems and machine-readable rule-bases to simulate legal reasoning given by judges. Basically, legal rule-bases rely on literal interpretations of statutes. In practice, however, literal interpretation of statute can have counterintuitive consequences. To deal with such consequences, English courts (the courts of England and Wales) have developed rules of judicial interpretation for reinterpreting statutes i.e. choosing or modifying the meaning of the statute’s wording (golden rule) or determining the statute’s purpose (mischief rule), and these rules have not been investigated in computational law. Previous research proposed the concept of legal debugging to formalize detection and resolution of counterintuitive consequences in legal rule-bases. Legal debugging consists of two steps. The first step is culprit detection, which involves interacting with a judge in order to identify a culprit, which is intuitively a part of statute that can be determined as a root cause of counterintuitive consequences. The second step is culprit resolution, which involves revising the rule-base to resolve a detected culprit. In this paper, we match two inductive logic programming approaches to two rules of judicial interpretation. The first approach is a bottom-up approach, which involves identifying an exceptional situation of the case and generalizing the legal rule revision. We match the bottom-up approach to the golden rule by using knowledge bases in the form of legal ontologies. The second approach is a top-down approach, which involves determining the purpose of the statute and refining the legal rule revision so that the revision fits within the context of the statute. We match the top-down approach to the mischief rule by using knowledge bases in the form of metarules. By using these matches, legal debugging can perform more practical legal revisions during the culprit resolution step.

An Ontology-Based and Deep Learning-Driven Method for Extracting Legal Facts from Chinese Legal Texts

The construction of smart courts promotes the in-deep integration of internet, big data, cloud computing and artificial intelligence with judicial trial work, which can both improve trials and ensure judicial justice with more efficiency. High-quality structured legal facts, obtained by extracting information from unstructured legal texts, are the foundation for the construction of smart courts. Based on the strong normative characteristics of Chinese legal text content and structure composition and the strong text feature learning ability of deep learning, this paper proposes an ontology-based and deep learning-driven method for extracting legal facts from Chinese legal texts. The proposed method utilizes rules and patterns generated in the process of knowledge modeling to extract simple entities, and then extracts complex entities hidden in legal text details with deep learning methods. Finally, the extracted entities are mapped into structured legal facts with clear logical relationships by the Chinese Legal Text Ontology. In the information extraction test of judicial datasets composed of Chinese legal texts on theft, the proposed method effectively extracts up to 38 categories of legal facts from legal texts and the number of categories extracted increases significantly. Among them, the rule-based extractor obtains an F1-score of 99.70%, and the deep learning-driven extractor obtains an F1-score of 91.43%. Compared with existing methods, the proposed method has great advantages in extracting the completeness and accuracy of legal facts.

ViLO — A Core Ontology of Vietnamese Legal Documents

Legal ontologies play a key role in various legal applications and have been broadly used by many stakeholders. Innovative systems and ontologies in the law hold potential to conduct legal research. With the needs for legal information management in smart applications, especially for Vietnamese law, it is vitally important to construct core legal ontologies for knowledge representation. This study proposes a core ontology for Vietnamese legal documents which covers general legal domain called as ViLO. The ViLO ontology mainly consists of related institutions of Vietnamese political system, types and structures of legal documents. The method of the NeOn-based collaborations among domain experts and ontology engineers was conducted to build up the ViLO ontology. Through FOCA-based validation results, the proposed method was shown to be effective and efficient. The resulting ontology was demonstrated to be reliable and enriched. The ViLO ontology is supposed to be a basis for further constructions of domain ontologies and artificial intelligence applications in Vietnamese law.

Semiotics of law in modern philosophical and legal research

The objective of the article was to assess the semiotics of law in modern philosophical and legal research. Recently, semiotic scientific research on the analysis of legal reality has become increasingly relevant, its demand is explained by the active search for meta-legal foundations for the integration of modern legal theory. The research carried out in the proposed article is applied using dialectical, systemic structural, genetic, and other methods, which allows us to affirm that interdisciplinary studies of law from the point of view of semiotics compete with many other approaches and contribute to solving important problems of philosophical and legal ciencias. It is conceptually concluded that the semiotics of law has direct and indirect organic relations with all the main subdisciplines of the philosophy of law: legal ontology, epistemology, anthropology, axiology, and praxeology, and represents one of the interdisciplinary approaches to law. The importance of semiotic analysis of the problems of legal reality is emphasized, which demonstrates the logic of its construction, systemic and structural connections, reveals the internal mechanisms and symbolic patterns of its development.

In search of the last abstraction of civil law

This publication was intended to be a regular review of an extremely informative book written by S.V. Tretyakov. Nonetheless, since the book poses important ontological and legal issues, the author focuses in this essay on methods of scientific research that are inherent to legal philosophy, particularly legal ontology. S.V. Tretyakov, in his quest to find “the last abstraction of private law” (A. von Tuhr), discovered what can be designated as universal categories of private law, including a subjective private right, which is at the heart of a will theory perspective on subjective rights. The article argues in solidarity with the book’s author that civil law methodology needs to be supplemented by the methods of knowledge intrinsic to the ontology of private law. A legal philosophy in this context should be considered a collection of mainly artificial, imaginary, and sometimes fictional ideas denoted by the polysemantic and inclusive Latin word “ars” (jus est ars...). A combination of private law ontology and a will theory, exemplified by Hegel’s aphoristic statement that lawyers think in terms of will, led the author to conclude that the ultimate abstraction of civil law is free will, which implies both dignity and autonomy of the owner of a right.