This document discusses ontology engineering and practices. It begins with a review of ontology and compares ontology-like things such as controlled vocabularies, taxonomies, thesauruses, and data models. It defines ontology as a formal, explicit specification of a shared conceptualization of a domain. The document outlines ontology development methods and the typical life cycle of an ontology building project, which includes investigation, design, implementation, evaluation, and documentation. It provides an example ontology and discusses ontology building in summary.
This document discusses categorizing norms in legal documents. It identifies several types of legal provisions that can be distinguished based on their structure and language, including core rules, definitions, exceptions, sanctions, and procedural norms. It also notes that laws can be modeled as having different layers of provisions that overlap and operate on different domains. An integrated model that retains isomorphism with the original legal text could facilitate maintenance when laws change. The document concludes that Dutch laws often have provisions that match one sentence, with a limited number of language constructs per provision type, indicating automatic recognition and classification may be possible.
Data Model vs Ontology Development – a FIBO perspective | Mike BennettConnected Data World
1) The FIBO (Financial Industry Business Ontology) project aims to develop a shared business ontology for the financial industry with commonly agreed upon meanings expressed logically.
2) Early explorations involved modeling financial concepts like equities in OWL (Web Ontology Language) but it was found that a more abstract conceptual model was needed separate from implementation details.
3) FIBO takes a foundational semantics approach, grounding concepts in legal, accounting, and other real-world constructs to provide common shared meanings across the industry rather than focusing on implementation or data aspects.
This document provides an abstract for a study analyzing key aspects and problem areas in translating legal terminology between Polish and English, specifically in contracts. The study examines 5 official translations of English contracts into Polish and considers theoretical frameworks on legal translation and issues of equivalence between terms. It analyzes differences between Polish and English law and legal terminology, and identifies terminology problems in the sample contracts. A survey of professionals is also analyzed to identify the most accurate translation approaches and whether target texts can achieve the same legal effect.
Building a Legal Taxonomy & Thesaurus: The Palestinian ExperienceJamil Salem
The document summarizes the Palestinian experience in building a legal taxonomy and thesaurus. It describes several projects including: [1] The Al-Muqtafi database which consolidates Palestinian legislation and court judgments; [2] Defining the syntax needed to semantically link legislation and court judgments; [3] Developing relationships between concepts extracted from legislation and judgments. The goal is to semantically integrate Palestinian legal information systems using an ontology and allow users to access relevant laws, cases, and concepts.
INDONESIAN-ENGLISH CROSS-LINGUAL LEGAL ONTOLOGY FOR INFORMATION RETRIEVALdannyijwest
This research encompasses the construction of a multilingual lexical database for cross-lingual
information retrieval in the Indonesian legal domain. Multilingual lexical database featuring lexically
and legally grounded conceptual representation can fit the cross-lingual information retrieval. Lexical
database use Ontology Web Language (OWL) representation language. This representation is useful to provide application developers a high-quality resource and to promote interoperability.
This document provides the marking scheme for the January 2011 GCE LAW examination in England and Wales. It includes marking criteria and guidelines for 6 different law options that were assessed. For each question, it identifies the assessment objectives being tested, such as knowledge of the law (AO1) and skills in legal argument and reasoning (AO2). Sample answers are also provided to illustrate how different levels of response would be marked. The marking scheme aims to ensure examiners apply criteria consistently across all student scripts.
This lecture discusses ontologies and their applications. Ontologies aim to provide a shared understanding of domain concepts through formal semantics. They can be used for data integration across information systems, enabling semantic search on the semantic web. Ontologies are also useful for designing conceptual schemas for information systems to allow open data exchange and global queries. Examples discussed include a customer complaint ontology and a bookstore ontology specified in OWL. Standard vocabularies alone are insufficient for heterogeneous domains as they do not adapt well and definitions can be ambiguous - ontologies aim to formally and precisely specify meanings.
This document discusses ontology engineering and practices. It begins with a review of ontology and compares ontology-like things such as controlled vocabularies, taxonomies, thesauruses, and data models. It defines ontology as a formal, explicit specification of a shared conceptualization of a domain. The document outlines ontology development methods and the typical life cycle of an ontology building project, which includes investigation, design, implementation, evaluation, and documentation. It provides an example ontology and discusses ontology building in summary.
This document discusses categorizing norms in legal documents. It identifies several types of legal provisions that can be distinguished based on their structure and language, including core rules, definitions, exceptions, sanctions, and procedural norms. It also notes that laws can be modeled as having different layers of provisions that overlap and operate on different domains. An integrated model that retains isomorphism with the original legal text could facilitate maintenance when laws change. The document concludes that Dutch laws often have provisions that match one sentence, with a limited number of language constructs per provision type, indicating automatic recognition and classification may be possible.
Data Model vs Ontology Development – a FIBO perspective | Mike BennettConnected Data World
1) The FIBO (Financial Industry Business Ontology) project aims to develop a shared business ontology for the financial industry with commonly agreed upon meanings expressed logically.
2) Early explorations involved modeling financial concepts like equities in OWL (Web Ontology Language) but it was found that a more abstract conceptual model was needed separate from implementation details.
3) FIBO takes a foundational semantics approach, grounding concepts in legal, accounting, and other real-world constructs to provide common shared meanings across the industry rather than focusing on implementation or data aspects.
This document provides an abstract for a study analyzing key aspects and problem areas in translating legal terminology between Polish and English, specifically in contracts. The study examines 5 official translations of English contracts into Polish and considers theoretical frameworks on legal translation and issues of equivalence between terms. It analyzes differences between Polish and English law and legal terminology, and identifies terminology problems in the sample contracts. A survey of professionals is also analyzed to identify the most accurate translation approaches and whether target texts can achieve the same legal effect.
Building a Legal Taxonomy & Thesaurus: The Palestinian ExperienceJamil Salem
The document summarizes the Palestinian experience in building a legal taxonomy and thesaurus. It describes several projects including: [1] The Al-Muqtafi database which consolidates Palestinian legislation and court judgments; [2] Defining the syntax needed to semantically link legislation and court judgments; [3] Developing relationships between concepts extracted from legislation and judgments. The goal is to semantically integrate Palestinian legal information systems using an ontology and allow users to access relevant laws, cases, and concepts.
INDONESIAN-ENGLISH CROSS-LINGUAL LEGAL ONTOLOGY FOR INFORMATION RETRIEVALdannyijwest
This research encompasses the construction of a multilingual lexical database for cross-lingual
information retrieval in the Indonesian legal domain. Multilingual lexical database featuring lexically
and legally grounded conceptual representation can fit the cross-lingual information retrieval. Lexical
database use Ontology Web Language (OWL) representation language. This representation is useful to provide application developers a high-quality resource and to promote interoperability.
This document provides the marking scheme for the January 2011 GCE LAW examination in England and Wales. It includes marking criteria and guidelines for 6 different law options that were assessed. For each question, it identifies the assessment objectives being tested, such as knowledge of the law (AO1) and skills in legal argument and reasoning (AO2). Sample answers are also provided to illustrate how different levels of response would be marked. The marking scheme aims to ensure examiners apply criteria consistently across all student scripts.
This lecture discusses ontologies and their applications. Ontologies aim to provide a shared understanding of domain concepts through formal semantics. They can be used for data integration across information systems, enabling semantic search on the semantic web. Ontologies are also useful for designing conceptual schemas for information systems to allow open data exchange and global queries. Examples discussed include a customer complaint ontology and a bookstore ontology specified in OWL. Standard vocabularies alone are insufficient for heterogeneous domains as they do not adapt well and definitions can be ambiguous - ontologies aim to formally and precisely specify meanings.
Principles of Health Informatics: Terminologies and classification systemsMartin Chapman
Principles of Health Informatics: Terminologies and classification systems. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
A journal which discusses the relationship of logic to law; gives reference to previous researches related and provides logical questions which can be a guide for further explorations.
This document discusses theories of cyberspace regulation proposed by Lawrence Lessig. Lessig argues that four factors regulate behavior in cyberspace: norms, markets, law, and code (or architecture). Code plays a particularly important role as it determines what actions are possible or impossible. Lessig proposes reforms to copyright law and policies promoting open code to balance control and innovation on the internet. Critics argue surveillance should also be considered a form of regulation and that "architecture" is a more accurate term than "code." The document examines issues around balancing control, privacy, and innovation in cyberspace.
EDF2012 Andrew Farrow - (Copy)right information in the digital ageEuropean Data Forum
The Linked Content Coalition (LCC) aims to improve access to and licensing of digital content by specifying a framework for rights data interoperability. The LCC is developing a Rights Reference Model (RRM) expressed as an abstract model and formal XML schema. The RRM will be a schema for transforming rights data between existing schemas to facilitate interoperability, while remaining comprehensive, extensible, commercially and sector neutral, and technology neutral. In 2012, the LCC will focus on developing the RRM and investigating related technical and business issues.
Finish the term paper using the following outline. In addition to th.docxernestc3
Finish the term paper using the following outline. In addition to the 4-6 pages of the paper itself, you must include a title page and a reference page. You are to follow APA Guidelines for citing and referencing sources. Your paper must be in your own words, representing original work. Paraphrases of others' work must include attributions to the authors. Limit quotations to an average of no more than 3-5 lines, and use quotations sparingly. It is always better to write the information in your own words than to directly quote.
Title: Penalties and adjudication for various information and technology offenses.
Thesis: Penalties and adjudication for various information and technology offenses are not playing the crucial role of securing the abolition of breaching the law, which has been set, to safely guide the key players in the platform. The law is not even ensuring that those people who temple with the originality of a given person work.
i. Introduction
a. Thesis
b. The role of the thesis.
ii. Expounding on the law and some of the changes needs to make it better.
a. Background of the law
b. Some of the recommendation that has been pointed out concerning the law by different stakeholders.
c. The effects of the law on today's information and technology industry.
iii. The main objectives of the law.
a. The success and the future of IT in the country
b. The goals of addressing the topic
c. The significance of the law and the setbacks
d. The significance of implement the law to the grass root.
iv. The benefit of law to the industry
a. The key traits of for success
b. The goal of the law
c. Efforts to introduce new laws and ensuring they have been followed.
i. Significance of changing some articles in the law
ii. Incorporating of organs to ensure that the effectiveness of the law
d. Making sure that the law meets the national and international standards
v. Recommendation from the writer's view.
a. Some of the things that can make a difference in the information and technology sector.
vi. Conclusion
a. Summarizing the arguments and efforts made
Reference
Bottoms, A. (2001). Compliance and community penalties. Community penalties: Change and challenges, 87-116.
Roth, M. P. (2010). Crime and punishment: A history of the criminal justice system. Nelson Education.
.
Automated Discovery of Logical Fallacies in Legal Argumentationgerogepatton
This document summarizes an article that presents a model and system for discovering logical fallacies in legal argumentation. The model functions by formalizing legal text in Prolog. It assesses different parts of legal decision making like claims, applied laws, and decisions for detecting fallacies. The system checks arguments for validity, soundness, sufficiency, and necessity to identify fallacies. It is asserted that dealing with these challenges resolves fallacies in argumentation. The model provides a mechanism to discover non sequitur fallacies, where the conclusion does not follow the premises, in legal texts.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONijaia
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONgerogepatton
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONgerogepatton
This document presents a model for discovering logical fallacies in legal argumentation. The model analyzes formalized legal text implemented in Prolog to assess arguments for validity, soundness, sufficiency, and necessity. This helps identify errors in reasoning known as non sequitur fallacies. The system checks if a claim's premises adequately support its conclusion and the application of the relevant law. By examining the logic and consistency of arguments at each stage, the model aims to detect fallacies and ensure legally sound decision making.
The document discusses Akoma Ntoso, an open legal XML standard for parliamentary and legal documents. It describes Akoma Ntoso's structures for organizing legal documents and their metadata in XML, allowing documents to be searched, displayed, and linked across repositories and countries. Key features include identifying a document's parts, semantic descriptions of content, and mechanisms like FRBR and Top Level Classes for cross-referencing concepts and versions unambiguously.
AN INTERSEMIOTIC TRANSLATION OF NORMATIVE UTTERANCES TO MACHINE LANGUAGEIJwest
Programming Languages (PL) effectively performs an intersemiotic translation from a natural language to
machine language. PL comprises a set of instructions to implement algorithms, i.e., to perform
(computational) tasks. Similarly to Normative Languages (NoL), PLs are formal languages that can
perform both regulative and constitutive functions. The paper presents the first results of interdisciplinary
research aimed at highlighting the similarities between NoL (social sciences) and PL (computer science)
through everyday life examples, exploiting Object-Oriented Programming Language tools and an Internet
of Things (IoT) system as a case study. Given the pandemic emergency, the urge to move part of our social
life to the digital world arose, together with the need to effectively transpose regulative rules and
constitutive rules through different strategies for translating a normative utterance expressed in natural
language.
An Intersemiotic Translation of Normative Utterances to Machine Languagedannyijwest
This document discusses translating normative utterances expressed in natural language to machine language through programming. It begins by providing context on intersemiotic translation and programming languages. It then distinguishes between regulative and constitutive rules, and explores how each could be translated to Java code. For regulative rules, which regulate pre-existing behaviors, an example is given of coding a rule that turns on a light when motion is detected. For constitutive rules, which define new types of behaviors, an example examines how an object-oriented programming approach could represent real-world devices and their functions through defined classes and properties.
Legal Information: an introduction for Information Science studentsEmily Allbon
Lecture to City University's MSc Information Science students (March 2013). Covering the legal information profession, role of law librarians and intro to legal information.
The document discusses developing ontologies, including:
1. What an ontology is and different types of ontologies such as taxonomies, thesauri, and reference models.
2. Representing ontologies using knowledge representation formalisms that have evolved from semantic networks to description logics.
3. The Semantic Web ontology language OWL, which extends RDFS and is divided into three species with different levels of expressivity.
Semantic Modeling for Information FederationCory Casanave
Semantic Modeling for Information Federation describes the UML profile and methodology for conceptual modeling and using conceptual reference models for federation and integration of information, systems and organizations.
This presentation contains both an introduction and detail appropriate for experienced architects.
Detailed information on the operation of the Data Harmony Machine Aided Indexer module from Access Innovation’s, Inc. Presented by Alice Redmond-Neal and Jack Bruce at the 2012 Data Harmony User Group meeting on February 7, 2012 at the Access Innovations, Inc. offices.
The document summarizes the structure and content of the new cataloging standard Resource Description and Access (RDA). It discusses how RDA was influenced by other standards like FRBR and FRAD and aims to support both current and future database structures. The document outlines that RDA has two main parts - recording attributes and recording relationships. It describes the 10 sections of RDA which cover attributes of different entities and relationships between entities. The document provides details on the sections regarding recording attributes of manifestations, works, persons, and placeholders for other entities. It also explains how RDA will record relationships using access points, identifiers, and relationship designators.
More Related Content
Similar to From legal Language to computer language (2009)
Principles of Health Informatics: Terminologies and classification systemsMartin Chapman
Principles of Health Informatics: Terminologies and classification systems. Last delivered in 2023. All educational material listed or linked to on these pages in relation to King's College London may be provided for reference only, and therefore does not necessarily reflect the current course content.
A journal which discusses the relationship of logic to law; gives reference to previous researches related and provides logical questions which can be a guide for further explorations.
This document discusses theories of cyberspace regulation proposed by Lawrence Lessig. Lessig argues that four factors regulate behavior in cyberspace: norms, markets, law, and code (or architecture). Code plays a particularly important role as it determines what actions are possible or impossible. Lessig proposes reforms to copyright law and policies promoting open code to balance control and innovation on the internet. Critics argue surveillance should also be considered a form of regulation and that "architecture" is a more accurate term than "code." The document examines issues around balancing control, privacy, and innovation in cyberspace.
EDF2012 Andrew Farrow - (Copy)right information in the digital ageEuropean Data Forum
The Linked Content Coalition (LCC) aims to improve access to and licensing of digital content by specifying a framework for rights data interoperability. The LCC is developing a Rights Reference Model (RRM) expressed as an abstract model and formal XML schema. The RRM will be a schema for transforming rights data between existing schemas to facilitate interoperability, while remaining comprehensive, extensible, commercially and sector neutral, and technology neutral. In 2012, the LCC will focus on developing the RRM and investigating related technical and business issues.
Finish the term paper using the following outline. In addition to th.docxernestc3
Finish the term paper using the following outline. In addition to the 4-6 pages of the paper itself, you must include a title page and a reference page. You are to follow APA Guidelines for citing and referencing sources. Your paper must be in your own words, representing original work. Paraphrases of others' work must include attributions to the authors. Limit quotations to an average of no more than 3-5 lines, and use quotations sparingly. It is always better to write the information in your own words than to directly quote.
Title: Penalties and adjudication for various information and technology offenses.
Thesis: Penalties and adjudication for various information and technology offenses are not playing the crucial role of securing the abolition of breaching the law, which has been set, to safely guide the key players in the platform. The law is not even ensuring that those people who temple with the originality of a given person work.
i. Introduction
a. Thesis
b. The role of the thesis.
ii. Expounding on the law and some of the changes needs to make it better.
a. Background of the law
b. Some of the recommendation that has been pointed out concerning the law by different stakeholders.
c. The effects of the law on today's information and technology industry.
iii. The main objectives of the law.
a. The success and the future of IT in the country
b. The goals of addressing the topic
c. The significance of the law and the setbacks
d. The significance of implement the law to the grass root.
iv. The benefit of law to the industry
a. The key traits of for success
b. The goal of the law
c. Efforts to introduce new laws and ensuring they have been followed.
i. Significance of changing some articles in the law
ii. Incorporating of organs to ensure that the effectiveness of the law
d. Making sure that the law meets the national and international standards
v. Recommendation from the writer's view.
a. Some of the things that can make a difference in the information and technology sector.
vi. Conclusion
a. Summarizing the arguments and efforts made
Reference
Bottoms, A. (2001). Compliance and community penalties. Community penalties: Change and challenges, 87-116.
Roth, M. P. (2010). Crime and punishment: A history of the criminal justice system. Nelson Education.
.
Automated Discovery of Logical Fallacies in Legal Argumentationgerogepatton
This document summarizes an article that presents a model and system for discovering logical fallacies in legal argumentation. The model functions by formalizing legal text in Prolog. It assesses different parts of legal decision making like claims, applied laws, and decisions for detecting fallacies. The system checks arguments for validity, soundness, sufficiency, and necessity to identify fallacies. It is asserted that dealing with these challenges resolves fallacies in argumentation. The model provides a mechanism to discover non sequitur fallacies, where the conclusion does not follow the premises, in legal texts.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONijaia
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONgerogepatton
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
AUTOMATED DISCOVERY OF LOGICAL FALLACIES IN LEGAL ARGUMENTATIONgerogepatton
This document presents a model for discovering logical fallacies in legal argumentation. The model analyzes formalized legal text implemented in Prolog to assess arguments for validity, soundness, sufficiency, and necessity. This helps identify errors in reasoning known as non sequitur fallacies. The system checks if a claim's premises adequately support its conclusion and the application of the relevant law. By examining the logic and consistency of arguments at each stage, the model aims to detect fallacies and ensure legally sound decision making.
The document discusses Akoma Ntoso, an open legal XML standard for parliamentary and legal documents. It describes Akoma Ntoso's structures for organizing legal documents and their metadata in XML, allowing documents to be searched, displayed, and linked across repositories and countries. Key features include identifying a document's parts, semantic descriptions of content, and mechanisms like FRBR and Top Level Classes for cross-referencing concepts and versions unambiguously.
AN INTERSEMIOTIC TRANSLATION OF NORMATIVE UTTERANCES TO MACHINE LANGUAGEIJwest
Programming Languages (PL) effectively performs an intersemiotic translation from a natural language to
machine language. PL comprises a set of instructions to implement algorithms, i.e., to perform
(computational) tasks. Similarly to Normative Languages (NoL), PLs are formal languages that can
perform both regulative and constitutive functions. The paper presents the first results of interdisciplinary
research aimed at highlighting the similarities between NoL (social sciences) and PL (computer science)
through everyday life examples, exploiting Object-Oriented Programming Language tools and an Internet
of Things (IoT) system as a case study. Given the pandemic emergency, the urge to move part of our social
life to the digital world arose, together with the need to effectively transpose regulative rules and
constitutive rules through different strategies for translating a normative utterance expressed in natural
language.
An Intersemiotic Translation of Normative Utterances to Machine Languagedannyijwest
This document discusses translating normative utterances expressed in natural language to machine language through programming. It begins by providing context on intersemiotic translation and programming languages. It then distinguishes between regulative and constitutive rules, and explores how each could be translated to Java code. For regulative rules, which regulate pre-existing behaviors, an example is given of coding a rule that turns on a light when motion is detected. For constitutive rules, which define new types of behaviors, an example examines how an object-oriented programming approach could represent real-world devices and their functions through defined classes and properties.
Legal Information: an introduction for Information Science studentsEmily Allbon
Lecture to City University's MSc Information Science students (March 2013). Covering the legal information profession, role of law librarians and intro to legal information.
The document discusses developing ontologies, including:
1. What an ontology is and different types of ontologies such as taxonomies, thesauri, and reference models.
2. Representing ontologies using knowledge representation formalisms that have evolved from semantic networks to description logics.
3. The Semantic Web ontology language OWL, which extends RDFS and is divided into three species with different levels of expressivity.
Semantic Modeling for Information FederationCory Casanave
Semantic Modeling for Information Federation describes the UML profile and methodology for conceptual modeling and using conceptual reference models for federation and integration of information, systems and organizations.
This presentation contains both an introduction and detail appropriate for experienced architects.
Detailed information on the operation of the Data Harmony Machine Aided Indexer module from Access Innovation’s, Inc. Presented by Alice Redmond-Neal and Jack Bruce at the 2012 Data Harmony User Group meeting on February 7, 2012 at the Access Innovations, Inc. offices.
The document summarizes the structure and content of the new cataloging standard Resource Description and Access (RDA). It discusses how RDA was influenced by other standards like FRBR and FRAD and aims to support both current and future database structures. The document outlines that RDA has two main parts - recording attributes and recording relationships. It describes the 10 sections of RDA which cover attributes of different entities and relationships between entities. The document provides details on the sections regarding recording attributes of manifestations, works, persons, and placeholders for other entities. It also explains how RDA will record relationships using access points, identifiers, and relationship designators.
Similar to From legal Language to computer language (2009) (18)
2. Outline
Leibniz Center for Law
From sources of law to ICT
applications
Structure
References
Content
Empirical results
Conclusions and current
research
2 09/08/2010
3. Leibniz Center for Law
Computational Legal Theory and
Legal Knowledge Management
(Formal) Models of:
Legal Knowledge
Sources? Elementary legal concepts?
Constituents of norms, coherence, …
Valid Legal Reasoning
Case assessment, causality, legal comparison,
…
4. Leibniz Center for Law -2
Applied Topics:
Improve quality of legal products
Legislation; decisions; advises, etc.
Inprove access to legal information
and knowledge
Support teaching and learning of legal
knowledge and skills
Legal organisations and change
management
6. “Legal Engineering”
Legislation can be seen as
specification of a
normative system.
Legislation is
underspecified.
It suffers from anomalies:
• inconsistencies
• Circle reasoning
• open evaluative terms
• ambiguities
09/08/2010
7. From Sources of Law to ICT Applications
Formal
Sources Applications
Models
G
Term: This
means
doctrine that and
has
Case law on relations
legislation
Case law with those
legislation
p1,p2,…
q1,q2,…
O(α І β)
concepts norms Tasks and
reasoning
Meta-knowledge
FOLaw
LLD
Sartor CLIME
LRI-core e-Court
…
8. Sources of Law
Most important source of „knowledge‟
Explicite links between sources and
knowledge models essential for:
Validation
Maintenance (traceability)
Justification
Link at right level of detail (granularity)
9. From Sources of Law to Formal Models
Automatic support :
Increase quality models and efficiency process
Increase inter-coder reliability
Structured
text with Model of Integrated
NL text individual model of
explicit and
typed refs provisions meaning
Recognizing Model
and fragment
classifying suggestions
8/9/2010
13. Characteristics of Sources of Law
Legislation
Precise grammar for reference, clear
identity and version criteria
(Adm.) Case Law
Precise grammar for reference, precise
identity, no versions
Doctrine
Sloppy reference, no identity markings,
sloppy versioning
14. The Structure of References: Simple References
Simple references
Name
Customs Law
Label and number
Article 1
Label, number and publication date
The law of April 13th, 2006
Indirect references
That article
15. The Structure of References: Complex References
Multi-valued references
Articles 1, 5 and 12
Multi-layered references
Customs Law, article 5, first member
Multi-valued, multi-layered references
Customs Law, articles 1, 5, first
member, and 12
16. The Structure of References: Ordering
Zooming in
Customs Law, article 5, first member
Zooming out
first member, article 5, Customs Law
Zooming in, then zooming out
article 5, first member, Customs Law
17. The Structure of References: Miscellaneous
Opening words
Article 12, opening words and parts 1
and 2
Exceptions
Articles 5-21, with the exception of
article 9
Each time
Articles 5-10, each time the first
member
18. Complete and incomplete references
Complete references
Does mention the document that is being
referred to
Customs Law, article 5, first member
Incomplete references
Does not mention the document that is
being referred to
Article 5, first member
20. Problems
Names cannot be recognised
Add names as a list to the grammar
Headings will (falsely) be recognised
as a reference
Mark headings beforehand; use Metalex as
input
21. Resolving references
Incomplete references
Reference needs to be completed from
context
Within a regulation, an incomplete
reference refers to the regulation itself
Within commentaries, incomplete
reference refer back to an earlier made
complete reference
22. Automatic Parsing
1. Determine identity source
In doc: Title, citation title
In metadata
2. Parse document
“Natural language” – model sentences
3. Find references
4. Determine type reference
E.g. attribution and delegation of power;
definitions; enactment; change
5. Determine identity goal
I.e. the thing it refers to
23. Results simple parser
99% of all simple references correctly
identified
95% of all complex references
correctly identified
Few false positives
Works adapted for Flemish law
Opsomer (2009)
24. Causes of errors
Failing to detect a reference
Missing labels or names
Textual errors
False positives
Homonyms: a label has a second
meaning in addition to being part of a
reference
the first member
25. Conclusions
Automatic detection of references is
entirely feasible
No complicated methods are needed;
regular grammars may suffice
26. From Sources of Law to Formal Models
From structured text to models of individual
sentences…
Structured
text with Model of Integrated
NL text individual model of
explicit and
typed refs provisions meaning
Recognizing Model
and fragment
classifying suggestions
8/9/2010
28. Automatic modelling – Sentences (1)
Start with sentences
Independent unit.
Often marked, otherwise easy to
recognize
Different types of sentences require
different translation, different model
29. Conclusions From Earlier Research
Dutch Law:
Provisions usually match one sentence
Several types of sentences can be easily
distinguished
Limited amount of language constructs
per type
Automatic recognition and
classification seems doable
Types not specific for Dutch law
(cf. Tiscornia e.a. for Italian law)
30. Categories
1. Definitions 6. Value Assignment
2. Deeming Provision 7. Change*
3. Norm – 8. Delegation
Right/Permission 9. Enactment Date
4. Norm – 10. Citation Title
Obligation/Duty 11. Penalization
5. Application
Provision
Each category uses specific language
constructs that can be used to identify
them.
31. Example: Penalisation Provision
Penalisation provisions set punishments
for breaking the law, and mark such an
act as either a misdemeanour or a crime.
Mining Act, article 133
1.Breaking article 43, sub 2, is punished
with a monetary fine of the second
category.
2.The fact marked as punishable by this
article is a misdemeanour.
32. Example: Norms (1)
Normative sentences form the core of
each regulation, stating obligations
and rights
Rights can be denoted by a wide
range of verbs: can, may, is allowed
to, has a right to, …
Similarly, obligations can be denoted
by the use of certain verbs: is
prohibited, is charged with
Many variations
33. Example: Norms (2)
However, obligations are often represented
as a “statement of fact”
Funeral Act, article 46, section 1
No bodies are interred on a closed cemetery.
May be about any subject
No common signal words or patterns
Preferred by the Guidelines for Legal
Drafting
34. Experiment (1)
Classifier
Based on 88 patterns
JAVA
Based on input in which
sentences and quoted text have
already been marked (MetaLex)
Assumes a statement of fact
norm if no explicit pattern is used
35. Experiment (2) - Lists
Lists are classified based on its
header, if this contains a pattern;
otherwise, each item is classified
(without the header)
Tobacco Act, article 1
In this law, and in the stipulations based on it, is
understood by:
a. tobacco products: … ;
b. Our Minister: …;
c. appendix: …;
…
36. Experiment – Test Set
18 texts
One royal decree
Three new bills
Fourteen amending bills
All „recent‟
No overlap with the training set
654 sentences
592 „regular‟ sentences
62 lists
37. Results per Document (1)
Sentence List
Source Total Correct % Total Correct Partial % Type
Royal Decree Stb. 26 23 97% 4 4 0 75%
New
1945, F 214
Bill 20 585 nr. 2 31 30 97% 4 3 1 75% New
Bill 22 139 nr. 2 22 20 91% 2 2 100% New
Bill 27 570 nr. 4 21 16 76% Change
Bill 27 611 nr. 2 11 11 100% 1 1 100% Change
Relative low score due to
Bill 30 411 nr. 2 141 128 91% 25 20 3 80% New
a misapplied pattern (3x)
Bill 30 435 nr. 2 40 39 98% 4 3 1 75% Change
Bill 30 583 nr. A 27 27 100% Change
Bill 31 531 nr. 2 3 3 100% Change
38. Results per Document (2)
Sentence List
Source Total Correct % Total Correct Partial % Type
Bill 31 537 nr. 2 29 29 100% 2 2 0 100% Change
Bill 31 540 nr. 2 7 7 100% Change
Bill 31 541 nr. 2 8 8 100% Change
Bill 31 713 nr. 2 7 6 86% 2 2 0 100% Change
Bill 31 722 nr. 2 31 22 71% 6 5 0 83% Change
Bill 31 726 nr. 2 78 67 86% 2 1 1 50% Change
Bill 31 832 nr. 2 7 7 100% 3 3Relative low100% due to
score Change
a pattern appearing in an
Bill 31 833 nr. 2 4 4 100% auxiliary sentence Change
(5x)
Bill 31 835 nr. 2 99 90 91% 7 4 3 57% Change
Total 592 537 91% 62 50 9 81%
39. Overall Results
91% of all regular sentences have
been correctly classified
71%-100% over laws
81% of all lists have been correctly
classified
50%-100% over laws
40. Results per Type (1)
Type In corpus Missed False
Definition 2% 12 1 0
Norm - Right/Permission 11% 64 4 13
Norm - Duty 5% 29 0 1
Delegation 3% 19 6 0
Publication Provision 1% 4 0 0
Application Provision 7% 40 1 8
Enactment Date 3% 17 1 0
Citation Title 1% 3 0 0
Value Assignment/Change 0% 1 0 0
Penalisation 0% 0 0 2
Change 41% 241 16 8
Mixed Type 1% 3 3 0
Norm - Statement of Fact
(default) 27% 159 23 23
Total 592 55 55
41. Results per Type (2)
Mostly norms and
modifications
right/permission 11%
obligation/duty 27% + 5%
change 41%
Several definitions and
application provisions
Barely any of the others
42. Results – Patterns Used
Patterns Patterns
Type Known Used
Definition 14 5
Norm - Right/Permission 17 3 About 50% of the
Norm - Obligation/Duty
Delegation
15
7
8
5
known patterns has
Publication Provision 1 1 been used
Application Provision
Enactment Date
5
1
5
1
Difference in age
Citation Title 2 2 between test and
Value Assignment 8 1
training set?
Penalisation 3 1
Change - Scope 2 2 Underrepresented
Change - Insertion 4 4
sentence types
Change - Replacement 3 3
Change - Repeal 2 1
Change - Renumbering 3 2
87 44
43. Problems (1)
Patterns appearing in auxiliary
sentences instead of the main
sentence
Mostly happens with rights and
application provisions:
If x has the right to …
If x is able to …
If x applies …
44. Problems (2)
Lists need a more serious approach
Some can be classified by the header
only;
Some can be classified by the list item
only;
Some can only be classified by the header
combined with the item.
Lists need to be converted to
individual sentences (header plus list
item)
45. Minor problems
Missing patterns
Mixed sentences
Difficult to solve, but does not occur often
Patterns used for other purposes
Repeal of fines instead of repeal of
regulations
Specific patterns for specific laws
E.g. Tax Law (value assignment)
46. Conclusions
This (symbolic) approach is feasible
Using obligation as a default category
seems acceptable
No major categories are missing
We expect it to generalise to other
Dutch regulations
The approach could be used for other
(civil) jurisdictions and languages
Biagioli et al. (2005) similar results for
Italian law but statistical approach
47. Next Step
Structured
text with Model of Integrated
NL text individual model of
explicit and
typed refs provisions meaning
Recognizing Model
and fragment
classifying suggestions
8/9/2010
48. Next Step
Divide sentence in different terms that
are linked through relations
Classification (and base pattern) gives
a rough division, and a rough relation
More detailed division of the
sentences is needed
Using of Dutch grammar parsers
50. Automatic modelling – Reference parser
References are important in legal texts
Useful when the computer understands
these better
Better understanding is possible
References do not fit well in “normal
Dutch sentence structure”
Separate reference parser
51. Things to think about – Granularity
Granulary – How far do we want to go
with the splitting of text?
Liquor: those drinks, that, at a
temperature of twenty degrees
Celsius, consist of alcohol for at least
fifteen volume percents, with the
exception of wine.
52. Thinks to think about – Norms
Classification distinguishes only a
limited set of norms
Do we need more distinction?
For computer calculations?
For interaction with the user
53. Things to think about - Procedures
Procedures use the same language
constructs as other norms (at least in
Dutch), but:
Procedures have a more specific
context
Procedures have a stronger ordering
54. Overall Conclusions
Distance from Legal Language to Computer
Language is too big to cross in one step
Automatic modelling support is already
partially possible:
Structure and References
Classification of sentences in legislation
Generalisation to all Dutch legislation
possible
Same method for other languages and
jurisdictions
Generalisation to other sources of law more
difficult