Iuriservice II Ontology Development Iuriservice II Ontology Development

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15 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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Iuriservice II Ontology Development
Iuriservice II Ontology Development
Núria Casellas,
Núria Casellas,
Denny Vrande
Denny Vrande
čić, Joan Josep Vallbé,
čić, Joan Josep Vallbé,
Aleks Jakulin, Mercedes Blázquez
Aleks Jakulin, Mercedes Blázquez
Workshop on Artificial Intelligence and Law
Workshop on Artificial Intelligence and Law
XXII World Congress of Philosophy of Law and Social Philosophy
XXII World Congress of Philosophy of Law and Social Philosophy
Granada, May 2005
Granada, May 2005
May 25, 2005
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Introduction to SEKT Project and
Legal Case Study

Methodology

OPJK

Improving knowledge discovery on
the competency questions

Architecture
Agenda
May 25, 2005
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The inSEKTs
BT
University of Sheffield
Vrije Universiteit Amsterdam
Sirma AI
Empolis
Universit
ä
t Karlsruhe
Ontoprise
Universitat Aut
ò
noma
de Barcelona
Universit
ä
t Innsbruck
Jozef Stefan Institute
iSOCO
Kea-pro
May 25, 2005
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SEKT

Main goals of SEKT

European Leadership in Semantic
Technologies

Core Research

Combine
Human Language Technologies
,
Knowledge Discovery
and
Ontology
Technologies

Provide intelligent knowledge access
May 25, 2005
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Description of the Problem:
Legal Domain

In General:

Complaint about diligence of legal administration.

The Judges are overworked.

In Particular:

New Judges

A lot of theoretical knowledge, but few practical knowledge

On Duty.

When they are confronted with situations in which they are not
sure what to do


Disturb” experienced judges with typical questions.

Usually his/her former tutor (Preparador)

Existing Technology

Legal Databases

Essential in their daily work

Based on keywords and boolean operators

A search retrieves a huge number of hits
May 25, 2005
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Description of the Problem:
Legal Domain

Solution:

Design an intelligent system to help new judges with their
typical problems.

Extended FAQ system using Semantic Web technologies

Connect the FAQ system with the exiting jurisprudence.

Search Jurisprudence using Semantic Web technologies.
May 25, 2005
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LLD [Language for Legal Discourse, L.T. McCarty, 1989]:
Atomic formula, Rules and Modalities.

NOR [Norma, R.K. Stamper, 1991, 1996]: Agents Behavioral
invariants, Realizations.

LFU [Functional Ontology for Law, R.W. van Kranlinger; P.R.S.
Visser, 1995]: Normative Knowledge, World knowledge,
Responsibility knowledge, Reactive knowledge and Creative
knowledge.

FBO [Frame-Based Ontology of Law, A. Valente, 1995]: Norms,
Acts and Concepts Descriptions].

LRI-Core Legal Ontology [J. Breuker et al., 2002]: Objects,
Processes, Physical entities, Mental entities, Agents,
Communicative Acts.

IKF-IF-LEX Ontology for Norm Comparaison [A. Gangemi et al.,
2001]: Agents, Institutive Norms, Instrumental provisions;
Regulative norms; Open-textured legal notions, Norm dynamics.
State of the Art in Legal Ontologies
May 25, 2005
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Professional Knowledge (PK)

Legal Knowledge (LK)

Legal
Core Ontologies (LCO) [based on
General Theories of Law]

Legal Professional Knowledge
(LPK)

OPLK

Judicial Professional Knowledge
(JPK)

OPJK
Conceptual distinctions
May 25, 2005
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14
7
5
29
8
16
8
8
10
12
10
6
1
16
Total Autonomous Communities: 14 (out of 17)
Ethnographic survey
May 25, 2005
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Statistical analysis of results

Judicial units: heterogeneity

Judge’s profile
Protocols of analysis

Literal transcripts

Completed questionnaires

List of extracted questions
Preliminary exploitation of data
May 25, 2005
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Identification of possible
concepts through ALCESTE’s
results and TextToOnto
conceptual distribution


Domain detection


Competency questions
discussion and concept
extraction
OPJK Modeling
May 25, 2005
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JUDGE
ON-DUTY
FAMILY
ISSUES
IMMIGRATION
REAL ESTATE
DECISION-
MAKING &
JUDGMENTS
PROCEEDINGS
JUDICIAL
CLERKS
COMMERCIAL
LAW
CONTRACT LAW
CRIMINAL
LAW
GENDER
VIOLENCE
ORDER OF
PROTECTION
/ INJUNCTION
Intuitive ontological subdomains
May 25, 2005
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Term extraction using TextToOnto
May 25, 2005
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Term extraction using TextToOnto
and Spanish Gate
May 25, 2005
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1.
Identify important concepts that should
be represented
2.
Hierarchy construction
3.
Identify relations between them
4.
Redefine the ontology repeting steps 1-4
May 25, 2005
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S
electing (underlying) all the nouns (
usually
concepts) and
adjectives (
usually
properties) contained in the competency
questions.

¿Cuál es el tratamiento de las
denuncias
manifiestamente
inverosímiles o relativas a
hechos
que evidentemente carecen de
tipicidad?

¿Y si se trata de una
querella
que reúne todos los demás
presupuestos procesales
pero los
hechos
objeto de la misma
carecen de relevancia penal o manifiestamente falsos?

¿Qué ocurre si
comparece
en el
juzgado
una
persona
que
quiere
denunciar

hechos
difícilmente creíbles, sin relación entre
sí, dudándose por el
juez
de la capacidad mental del
denunciante
?

¿Ante quién debe
interponerse
el
recurso de reforma
contra la
prisión
, delante del
juez de guardia
o del
juez
que dictó el
correspondiente
auto de prisión
?
Competency question discussion
May 25, 2005
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OPJK classes identified
May 25, 2005
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OPJK and Proton Integration
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Improving knowledge discovery on
Improving knowledge discovery on
the competency questions
the competency questions
May 25, 2005
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Data:

3 text corpora (judges’ questions):

Corpus 1:
Scholar “on duty” questions (Spanish
Judicial School = 99)

Corpus 2:
Practical “on duty” questions (= 163)
(field work)

Corpus 3:
All practical questions (=756)(field
work)
Method:


TEXT GARDEN
(J. Stefan Institute, Ljubljana)

ALCESTE -
Analysis of the co-occurring lexemes
within the simple statements of a text [Reinert
2002, 2003]
Data and Method
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The text needs to be represented in an
appropriate way for statistical analysis:
1.
Breaking text into “units” (lines, sentences, …)
2.
Morphological categorization (adjectives,
prepositions, …)
3.
Putting words into canonical form:
a)
Lemmatization (is,was,are be)→
b)
Stemming (loved, loving lov+) →
4.
Analysis:
a)
Clustering
b)
Latent semantic indexing
c)
Correspondence analysis
d)
Classification
e)
Visualization
Analysis of Text
ALCESTE (Reinert,1988)
Corpus
Segmented in
chunks
Classes of related
chunks
List of
typical
words
related to
each
class
{
}
{
}
{
}
Geometric
representation
Hierarchical
descending
clustering
Correspondence
analysis
Folch & Habert (2000)
Example of Correspondence Analysis and
Visualization

+-----|---------|---------|---------+---------|---------|---------|-----+

20| solo| |

19| | parte+ |

18| | monitorio demand+ |

17| | archiv+accion+ |

16| present+ | falta+ vehiculo+fase+ |

15| | seguir procurador+ |

14| |recurso+ pago+quiebra+ |

13| ofici+| gasto+ . .ejecut+ejecucion+ |

12| sido dia+ .finca+embarg+verbal+ |

11| interes+traficoacto+.notificacionentrega+ |

10| momentocelebr+hall+ cuantia+resolver |

9 | valor+ |auto+admit+qued+.juicio+deposit+ |

8 | lesion+ venirdinero.. notific+pericial+ |

7 | | si vista+aport+inform+ |

6 madreacord+viviend+ | cabo solicit+ |

5 | victima+maridoempresa+ | llev+ ya prueba+abogado+ |

4 | ..tratosproteccion | |

3 | .senor+alejamiento | responsabili |

2 tema+mujer+malo+violencia | |

1 | denunci+medida+visitas | |

0 +--.separacion+orden+---------------+-----venirfiscal+------------------+

1 | pidepresun+ | |

2 | | |

3 | | |

4 | | |

5 | | |

6 | | |

7 | dict+ | |

8 | | |

9 | | |

10| | |

11| | |

12| | |

13| | |

14| | un |

15| | |

16| | levantamient |

17| | tenerdeten+ libertadforense |

18| |person+ .. . ..hacercausa+asunto+ |

19| servicio+ ......judicial+actuacion+ |

20| guardia+. juezllam+ .. .policiadetenido+ |

21| | partido+ |

+-----|---------|---------|---------+---------|---------|---------|-----+
ALCESTE
TEXT GARDEN
Example of Clustering
Class 1: Judicial unit
funcionar+ (21), juzgar(26), oficina(11), trabaj+(13), decir(26),
llam+(16), mand+(12), acudir(11), adjunto(4), busc+(4), consult+(4),
dato(6), hablar(4), jurisprudencia(3), local+(3), material(6), necesit+(7),
policia(14), prensa(4), sala(4), funerari+(2), hurto(3), informacion(5),
miedo(3), robo(3), servicio+(7), sustitu+(4), tecnico(2), venir(15)
Class 2: Family law
alejamiento(22), malo(22), medida(16), orden+(23), proteccion(17),
senor+(13), trat+(22), victima(11), mujer(11), padre(7), denunci+(12),
domestico(8), violencia(8), agresor(4), dict+(10), madre(7), marido(6),
nino(5), pension(4), psicolog+(5), separacion(5), abus+(5), alimento(3),
ayud+(4), casa(3), cautelar+(3), divorcio(2), empresa(3), hijo(4),
lesion+(6)
Class 3: Proceedings
escrit+(9), fiscal+(13), instruccion(9), ordinario(5), seguir(11),
acumular(5), audiencia-provincia(2), conform+(2), contradictori+(3),
criterio+(10), cuantia(5), falt+(7), injusto(3), interpretacion(3), ley(6),
motiv+(3), pendiente(2), perito(5)
Class 4: Enforcement (judgment)
ejecucion(14), ejecut+(15), embarg+(11), finca+(9), depositar+(6),
interes+(6), pago(6), suspension(5), deposito(6), entreg+(6),
quiebra(5), sentencia(9), solicit+(9), vehiculo(4), acreedor(3),
administracion(4), cantidad(4), conden+(4), cost+(4), dinero(4),
edicto(2), imposibilidad(3), multa(3), notificacion(4), pagar+(4)
May 25, 2005
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Stemming:
the longest string of
characters that is common to different
words:
For all the variants of ‘love’, but also for
‘lover’ (noun), ‘lovely’ (adverb), it can
offer the stem: lov+
Lemmatization
respects the category:
3 different lemma: love (verb), lover
(noun) lovely (adv)
If we apply this process to Spanish or
Catalan (or every Romanesque
language), which have a high flection
capacity (60 forms for verbs, without
taking into account the composed
forms), stemming would hide a lot of
information.
Stem
Lema
acumulacion
acumulación
acumularse
acumular
acumul+
---
admision
admisión
admit+
admitir
celebracion
celebración
celebr+
celebrar
misma+
mismo
mismo+
---
suspenderse
suspender
suspend+
---
EXAMPLES
Stemming vs Lemmatization
Quantitative Comparison
Stemmed
Corpus
Lemmatized
Corpus
Num.
different
forms
3074
2064
Num.
Ocurrences
19861
19946
Max. Freq.
Of a form
1230
2208
Hapax
1666
934


Lemmatized corpus has fewer word-forms than the stemmed version.


The LSI on the lemmatized corpus is able to reconstruct documents better,
especially in few dimensions.


The lemmatized corpus clustering is more detailed.
May 25, 2005
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1.
Clustering with
stemmed
corpus offers us
4 classes:
1.

On-duty’ actions (mixed with Judicial Office) (54,06%)
2.
Proceedings and Trial (18,10%)
3.
Enforcement (judgements)
(14,39%)
4.
Family Law (gender violence, divorce, separation…)
(13,46%)
2.
Clustering with
lemmatized
corpus is more detailed
and offers 6 classes:
1.
Judicial Office (20,11%)
2.

On-duty’ actions (27,25%)
3.
Family Law (gender violence, divorce, separation…)
(14,55%)
4.
Proceedings (15,61%)
5.
Trial (8,47%)
6.
Enforcement (judgements) (14,02%)
Comparision of Clustering Results
May 25, 2005
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Take-Home Messages

Do text analysis of legal documents!

If you do that, Do lemmatization!
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Methodology
Methodology
May 25, 2005
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Initial Methodology
+
Based on 800 competency questions
+
Questions were clustered
+
Middle-out strategy


Usage of ontology not considered


Repetitive discussions


Long discussions
May 25, 2005
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Considering the “Why”

No normative knowledge

Stick to the questions as sources

Model the questions, not the answers
May 25, 2005
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Wiki visualization
May 25, 2005
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Diligent Argumentation Ontology

Argumentation
ontology
defined

Based on
Case Studies
to identify the
most effective
types of
arguments

Argument type
recognition
based on RST
May 25, 2005
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Methodology changes
Using DILIGENT made the ontology
engineering…


much faster


amenable to distributed development


better documented


trackable


better manageable
Also DILIGENT itself got changed!
May 25, 2005
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Outlook

Better tool support – off-the-shelf wiki
had weaknesses

Moderator support in discussions

Competency question clustering

Gathering further experience from
legal and other case studies
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Architecture
Architecture
May 25, 2005
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High Level Requirements

Judges should not be bothered with a complex
user interface
.

A simple natural language interface is probably appropriate.

The decision as to whether a new question is
similar
to a stored
question (with its corresponding answer) should be based on
semantics
rather than on simple word matching.

An ontology can be used to perform this semantic matching of
questions.

The
questions
included in the system should be of
high

quality
.

Be rather exhaustive and reflect the actual situation

As extensive survey with more than 250 Spanish judges forms the
basis for the questions.

Justify
the answer provided by the system with existing
Jurisprudence
.

Jurisprudence databases.

Metadata and Ontology process of documents.

Knowledge

Management
at all levels
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Example Question-Answer

Question
:

What problems can we foresee with the analysis of
small amounts of drugs, where the identification test
destroys the drugs?

Answer
:

This is an unrepeatable piece of evidence at the trial. In
these cases, the Spanish Criminal Procedure Act states
that the adversarial principle should be respected. While
the trial proceedings are prepared, the judge must
explain to all parties that they may choose an expert to
perform these tests.
May 25, 2005
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Court
and
docket
number
Names of the magistrates
Date and place
Prefatory statement
History of the
Case
Grounds of
Decision
Example of judgment: parts


May 25, 2005
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Question
Answer
FAQ
FAQ
Judgement
Judgement
Summary
Case History
Decision Grounds
Ruling
OPJK
OPJK
Practical
Practical
Knowledge
Knowledge
Instances
Instances
Relations between the
Question/Answer & Judgment
May 25, 2005
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Architecture
Questions-
Answers
Expert Knowledge
Semantic
Matching
DB 1
Decisions
DB N
Decisions
Ontology Learning
& feeding
Ontology Merging
Jurisprudence
Ontology
Alignment
Web
browser
Natural
Language
May 25, 2005
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Expert Knowledge Retrieval
Design - Technological considerations
Ontology
Domain
Detection
Keyword
Matching
Ontology
Grapth
Path Matching
iFAQ System
Multistage Searching Subsystem
Ontology Technology
Natural Language
Processing
Caching subsystem
Persistence subsystem
Eficiency
Accur
acy
May 25, 2005
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Expert Knowledge Retrieval

Chain of Resposability pattern
FAQ
Candidates
FAQ
FAQ
FAQ
User
Question
iFAQ Search Engine
Ontology Domain
Detection
FAQ
Search Factory
Other search engines ...
Keyword/synonym
matching stage
Ontology graph
path matching
Plugged Searching Stages
May 25, 2005
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Expert Knowledge Retrieval
Ontology
Linking
NLP
NL query
POS list
(lemmas)
Semantic
Distance
Calculation
Semantic distance
Between queries
Term Coverage
Calculation between
queries
Best match
of stored queries
Ontology
Semantic Similarity: Main steps
May 25, 2005
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Expert Knowledge Retrieval

The semantic distance is based on the weighted navigation
distance between terms in the ontology.

Navigation through the ontology means that one moves from
one concept to another concept, via one of its relations or
attributes.

Is a

Follows

Actor

Etc.

The task of associating distance costs:

Is a domain specific

Needs to be performed by legal expert.
Semantic Similarity
Ontology
Accuse
Actions
Follow
Denounce
Mother
Son
Son
Mother
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Conclusions

Decision support system
for unexperienced
judges

Using
Semantic Web technology
for handling
knowledge

Provide
knowledge for decision making process

Capture
knowledge from experts

Share
knowledge among all users

Extended understanding capacities

Background knowledge:
Professional Legal Ontology

Decision Explanation

Improved Knowledge Acquisition
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Expert Knowledge Retrieval

Terms of the input question are filtered by their part-of-speech
category:

Nouns, Verbs, Adjectives, and Adverbs

Each term is linked to the ontology if it is possible

The algorithm constructs a semantic path from each input term
to terms of the stored query.

Terms which are linked to the ontology

Terms (user questions) linked to the ontology but no corresponding
them can be found in the stored questions (ontology navigation)


Semantic distance infinitely large
.

Terms cannot be linked to the ontology.

But have a corresponding one at the stored question (same lemma)


Distance is Zero

Not corresponding lemma in the stored question


Distance is
infinite
.
Term Coverage