Ontology Learning Process as a Bottom-up Strategy for Building
Domain-specific Ontology from Legal Texts
Mirna El Ghosh
1
, Hala Naja
2
, Habib Abdulrab
1
and Mohamad Khalil
3
1
INSA, Rouen, France
2
Lebanese University, Faculty of Sciences, Tripoli, Lebanon
3
Lebanese University, Faculty of Engineering, Tripoli, Lebanon
Keywords: Ontology Learning, Semi-automatic Extraction, Natural Language Processing, Legal Ontologies, Domain-
specific Ontologies.
Abstract: The objective of this paper is to present the role of Ontology Learning Process in supporting an ontology
engineer for creating and maintaining ontologies from textual resources. The knowledge structures that
interest us are legal domain-specific ontologies. We will use these ontologies to build legal domain ontology
for a Lebanese legal knowledge based system. The domain application of this work is the Lebanese criminal
system. Ontologies can be learnt from various sources, such as databases, structured and unstructured
documents. Here, the focus is on the acquisition of ontologies from unstructured text, provided as input. In
this work, the Ontology Learning Process represents a knowledge extraction phase using Natural Language
Processing techniques. The resulted ontology is considered as inexpressive ontology. There is a need to
reengineer it in order to build a complete, correct and more expressive domain-specific ontology.
1 INTRODUCTION
It is commonly known that the knowledge of the
legal domain is expressed and conveyed in texts
using domain-specific terminology. However, this
terminology does not provide a well-defined
structure to be used by machines for reasoning tasks.
Meanwhile, the extracting and mining of this
terminology will lead to a certain domain
representation model such as ontology (Mädche,
2000). Ontology is defined as a conceptualization of
a domain into a human understandable, machine-
readable format consisting of entities, attributes,
relationships and axioms (Guarino, 1995). This
definition imposes that the concepts and relations
among them have to be explicitly represented and
expressed using formal language such as Web
Ontology Language (OWL). This formal structure
representation leads to specify axioms for reasoning,
in order to define constraints in ontologies (Wong,
2009). Building and maintaining ontologies
manually remains a resource-intensive, time
consuming and costly task. This is due to the
difficulty in capturing knowledge, also known as the
“knowledge acquisition bottleneck”. Even with
some reuse of Core or Upper ontologies. Therefore,
there is a need to automatic or semi-automatic
techniques that support the building process. These
techniques have become to be known as Ontology
Learning (OL) (Cimiano, 2004). OL has the
potential to reduce the cost of creating and
maintaining ontologies using semi-automatic
methods and tools. Actually, we motivate to develop
legal domain ontology for the Lebanese criminal
domain. In a previous work (El Ghosh, 2016), a
middle-out approach is proposed for building this
ontology for a legal knowledge based system that
performs reasoning and information retrieval tasks
(Figure 1). Accordingly, we proposed to modularize
the legal domain ontology into four modules or
ontologies: upper, core, domain and domain-
specific. The upper module represents the most
general concepts and relations that cover all the
domains (such as Agent, Act and Action). The core
module provides a definition of structural
knowledge in the legal domain. For instance,
concepts, such as Legal_Source, Legal_Act and
Legal_Document, are common for all the legal fields
(criminal, civil, etc.). The concepts of the domain
module, in turn, such as Offence, Infraction and
Offender, describe the conceptualization of the
criminal domain. Finally, in the domain-specific
El Ghosh M., Naja H., Abdulrab H. and Khalil M.
Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts.
DOI: 10.5220/0006188004730480
In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017), pages 473-480
ISBN: 978-989-758-220-2
Copyright
c
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
473
module, we learn the knowledge of the Lebanese
criminal system from textual resources such as the
criminal code. Furthermore, an alignment process
will be applied to complete the global ontology by
linking the concepts of the different modules. In
order to develop the different modules, two different
strategies are applied (top-down and bottom-up).
The top-down represents the conceptual modeling
process based on reusing foundational and core
ontologies (El Ghosh, 2016). Meanwhile, the
ontology learning process from textual resources is
depicted by the bottom-up strategy that aims to
develop the domain-specific ontology module.
Domain-specific ontologies specify formally
concepts and relations of a specific subject domain
(Hatala, 2012). They cannot be reused unlike other
kinds of ontologies (upper and core).
Figure 1: Middle-out approach for building modularized
ontology.
What is important that these ontologies are
useful in systems involved with artificial reasoning
and information retrieval. In this context, the OL
process from unstructured legal documents could be
useful for building the criminal domain-specific
ontology. Meanwhile, the main obstacle that exists
is to reduce the efforts required for creating the
ontology by defining a convenient semi-automatic
development process and ontology learning tool. In
order to achieve the goal, we started by discussing
the ontology learning from unstructured texts in
section II. In section III, we overviewed existent
ontology learning methods and tools. The
experimental work is presented in section 4. The
section 5 discusses similar works. We finished by
section 6 for the discussion and section 7 for the
conclusion.
2 ONTOLOGY LEARNING
FROM UNSTRUCTURED TEXT
The term Ontology Learning (OL) was introduced in
(Mädche, 2005) and is considered as an important
task in Artificial Intelligence, Semantic Web and
Knowledge Management. It is the dynamic process
of building ontologies. OL is a data model that
represents a set of concepts and relations within a
domain (Yang, 2008). More specifically, OL is
considered as a subtask of Information Extraction
(IE), which is a type of Information Retrieval (IR)
(Rogger, 2010). The main purpose of OL process is
to apply methods from various fields such as
linguistic analysis, machine learning, knowledge
acquisition, statistics and information retrieval in
order to extract knowledge and support the
construction of ontologies. This dynamic process,
depicted in the Figure 2, takes as input implicit and
unstructured knowledge and produces as output
explicit structured knowledge (Cimiano 2005).
Generally, OL is a semi-automatic process where the
ontology engineer and the domain expert can be
involved to achieve better results (Rogger, 2010).
Thus, the techniques used in the ontology
development process will be under their supervision.
Their expertise and background knowledge helps in
verifying the obtained information and decide the
valuable information.
Figure 2: The dynamic process of ontology learning,
(Buitelaar, 2005).
2.1 Input
As aforementioned, ontologies can be learnt, by
applying the OL process, from various sources of
data types: structured (such as databases), semi-
structured (e.g. XML) and unstructured textual
documents. The domain application of this work is
the Lebanese criminal code which is an unstructured
text resource. This type of resources is the most
available format as input for ontology learning
processes. They reflect mostly the domain
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
474
knowledge for which the user is building the
ontology. In addition, they describe the terminology,
concepts and conceptual structures of the given
domain. However, some authors, such as (Rogger,
2010), consider that processing unstructured data is
the most complicated problem because most of the
knowledge is implicit and allows conceptualizing it
by different people in different manner. Specifically,
in the legal domain, the implicit knowledge of the
natural language is one of the main obstacles to
progress in the field of artificial intelligence and law
(McCarty, 2007).
2.2 Output
Ontology learning from text is the process of
deriving concepts, relations and axioms from textual
resources to build ontologies. The main output of the
OL process is a structured content represented in an
explicit formal way. For (Cimiano, 2004), the tasks
in ontology learning from text are organized in a set
of layers (Figure 3). These tasks aim at returning six
main outputs: terms, synonyms, concepts, taxonomic
relations, non-taxonomic relations and axioms.
These outputs represent the main elements of
ontology.
Figure 3: Ontology learning from text, layer cake
(Buitelaar, 2005).
Terms are the most basic building blocks in
ontology learning (Wong, 2009). Concepts can be
abstract or concrete, real or fictitious. Concept
hierarchies or taxonomies are crucial for any
knowledge based system (Cimiano, 2005). Non-
taxonomic or non-hierarchical relations represent the
interactions between concepts (e.g. meronymy,
thematic roles, attributes, possession and causality)
(Wong, 2009). Finally, the axioms are defined as
propositions or sentences that are always taken as
true. Axioms act as a starting point for deducing
other truth, verifying correctness of existing
ontological elements and defining constraints (Wong,
2009).
3 ONTOLOGY LEARNING
METHODS AND TOOLS
There are many works in the literature that deal with
ontology learning from textual resources. The focus
of this paper is to discuss and evaluate existent
methods and tools to develop (semi-)automatically
text-based domain ontologies. Furthermore, we will
define a (semi-) automatic approach for building our
legal domain-specific ontology.
3.1 Methods
In order to obtain high-quality ontologies, the
development process has to be driven by a
methodology (Hatala, 2012). In this section, we
discuss briefly the most known ontology learning
methodologies from textual resources. In the work
of (Sabou, 2005), the ontology learning process is
based on three major tasks: term extraction,
conceptualization and enrichment. For (Mädche,
2005), the OL process is composed of four different
phases: extract concepts, prune, refine and Import or
reuse. In other studies, such as (Mazari, 2012) and
(Ge, 2012), the ontology learning tasks are resumed
in three: documents preprocessing, concepts
extraction and relations discovery. Actually, these
tasks discover only taxonomic relations (parent-
child, hyponymy (is-a) and meronymy (part-of)).
However, some authors such as (Novelli, 2012),
(Balakrishna, 2010) and (Serra, 2012) propose
methods to solve the problem of learning non-
taxonomic relations of ontologies from text. In the
legal domain, most of the methodologies focus on
concepts extraction as a main step of the ontology
development process (Lenci, 2009). The approach of
(Walter, 2006) is based on the exploitation of the
frequency of definitions in legal texts.
3.2 Tools
In the literature, a long list of ontology learning tools
has been proposed. The existent tools differ
according to input data types, output formats and
mainly the methods and algorithms used in order to
extract the ontological structures. The main goal of
using ontology learning tools is to reduce the time
and cost of ontology development process. In this
section, we discuss mainly the existent ontology
learning tools from unstructured textual resources.
Terminae is a method and tool that generates
standard OWL ontologies (Biebow, 1999). Terminae
integrates linguistic and knowledge engineering
Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts
475
tools to guide the knowledge acquisition from texts
and to build terminological and ontological models.
Text2Onto, successor of Text-to-Onto (Mädche,
2001), is a data-driven, ontology learning tool that
supports automatic development of ontologies from
textual documents (Cimiano, 2005). Text2Onto is
built upon the GATE
1
framework. Accordingly,
Text2Onto implements linguistic processing and
machine learning statistical techniques to extract
domain concepts and relations. This tool features
also algorithms for generating concepts, taxonomic
and non-taxonomic relations. OntoGen is a semi-
automatic and data-driven ontology editor that helps
the users to build ontologies by suggesting concepts
and relations. This system integrates machine
learning and text mining algorithms. OntoGen offers
two main features: concept suggestion and naming
and ontology and concept visualization. T2K
2
extracts domainspecific information from texts
using natural language processing techniques in
three main phases: preprocess text and extract terms,
form concepts using POS patterns and relations or
knowledge organization (Dell’Orletta, 2014).
CRCTOL is Concept-Relation-Concept tuple-based
ontology learning system from domain-specific text
documents. The tool adapts a full text parsing
technique and incorporates both statistical and
lexico-syntactic methods (Jiang, 2005). We
conclude that most of these tools rely on linguistic
and statistic methods to learn ontologies. The focus
is on extracting concepts and taxonomies. Thus, we
need to learn more semantic relations and axioms.
Table 1: Summary of ontology learning tools.
Tool
Elements extracted
Techniques
Terminae
(2005)
Terms, synonyms,
concepts, taxonomies,
non-taxonomic
relations
Linguistic and
knowledge
engineering
Text2Onto
(2005)
Terms, synonyms
concepts, taxonomies,
non-taxonomic
relations, instances
linguistic processing
statistical text
analysis
machine learning
association rules
OntoGen
(2006)
Terms, concepts,
taxonomies
Machine learning
text mining
T2K
(2008)
Terms, concepts
,taxonomies
statistical text
analysis and
machine learning
CRCTOL
(2010)
Concepts,
taxonomies,
non-taxonomic
relations
Statistical lexico-
syntactic association
rules
1
https://gate.ac.uk/
4 OUR WORK
Even after a comprehensive literature review, we
found a difficulty to define a complete approach or
tool that can totally extract domain-specific
ontologies from textual resources. This is due to
two reasons. First, we could not find a complete
(semi-)automatic tool or approach that carries the
ontology development process. Second, there is no
guarantee that the (semi-)automatically generated
ontology is correct and precise enough to
characterize the domain in question (Rudolph, 2007).
Since the focus of the current research is mainly on
extracting the elements of a criminal domain-
specific ontology from textual resources, using an
existent semi-automatic ontology learning can help
to extract an OWL ontology including the basic
elements (concepts, taxonomies, relations and
disjointness axioms). Meanwhile, and based on what
is found in the literature, incomplete and not
satisfactory results are expected. For this reason, the
intervention of ontology engineer and legal expert
during the ontology learning process is required in
order to supervise the work and to verify the
obtained information. Furthermore, a reengineering
methodology is needed in order to enhance the
results by transforming the resulted ontology into a
new more correct, complete and expressive ontology.
The general idea of the reengineering approach is
depicted in figure 4. In the current work, mainly the
ontology learning process, from texts, is discussed.
The reengineering phase will be the study of further
works.
Figure 4: Reengineering phase for updating domain-
specific ontology.
In this section, we introduce the main
components of the ontology learning process used in
the preparation and execution of the criminal
domain-specific ontology.
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
476
4.1 Material Selection
Actually, the domain-specific ontology that we aim
to build, using (semi-)automatic ontology learning
tool guided by an approach, represents the domain-
specific module in the modularized legal domain
ontology. The context of interest is the Lebanese
criminal system. The domain related material is the
Lebanese penal code that consists of legal natural
language texts. The Lebanese penal code contains
the general penal laws of Lebanon. First enacted in
1943 and it remains in effect today. It is translated to
French and English versions. Concerning the
structure of the code, it is divided into two main
books composed of 770 articles (Figure 5).
Figure 5: Excerpt of the Lebanese criminal code.
4.2 Tool Selection
After exploring the literature and collecting the
state-of-the-art for the most frequently used
ontology learning tools, we met some access
difficulties in our experimentations. In fact, three of
the tools were publicly available on the internet to
download and install: Terminae, OntoGen and
Text2Onto. In this section, we discuss briefly the
usability of each tool. Concerning the input type, all
the tools accept simple text files (.txt), Text2Onto
and Terminae accept also PDF files (.pdf). For
OntoGen, there are additional input file types that
need to be pre-processed, such as Named Line-
Document and Bag of Words. Terminae and
OntoGen need preprocessing efforts. Starting with
Terminae where the linguistic tool extract terms
automatically from the corpus based on their
occurrences. Meanwhile, the rest of the steps are
processed manually which is too resource
demanding and too time consuming. For this reason,
this tool is discarded. Furthermore, we face some
difficulties while using OntoGen. We could not
control the system that generates sequences of terms
that are not well related. In addition to this, the
suggestion of concepts is limited to single-word
terms, proposed only from the input documents (no
external resources), and the relations extraction is
limited as well to taxonomic. Meanwhile, OntoGen
provide a visualization and exploration of concepts
only and not of the whole ontology. OntoGen is
discarded too. We finished our experiments by
Text2Onto. According to (Gherasim, 2013),
Text2Onto is an ontology learning tool that covers
the entire process of extracting OWL ontologies.
Furthermore, it provides a long list of proposed
concepts and relationships along with their weights
in a tabular form. Meanwhile, Text2Onto does not
have any mechanism to filter the concepts irrelevant
to goal (Hatala, 2012). The user input is limited to
removing concepts and relationships extracted from
the supplied course. In Text2Onto, the visualization
of the structure of the resulted ontology is missing.
Regarding the external resources, Text2Onto uses
WordNet to improve and enrich the algorithms of
pattern-based relation extraction. However, some
authors found that WordNet lacks the richness of
named relations (Fouad, 2015). For this reason, they
decided to use online ontologies as an alternative to
WordNet. Regarding the limitations of Text2Onto,
this tool still answers the main requirements of our
work: automatic extraction, usability, scalability,
and reusability. Based on this selection, we proposed
to apply a reengineering phase that consists of
evaluating the ontology extracted using Text2Onto,
correcting the detected errors, refine the ontology
model and finish by enrich the semantic relations
and axioms. We will study deeply this point in
further works.
Table 2: List of experimented tools.
Tool
Terminae
OntoGen
Text2Onto
User Input
Add, remove,
modify
Add, remove,
modify
Remove
Visuali-
zation
Not available
Concepts
Not available
External
Resources
Not available
Not available
WordNet
4.3 Ontology Extraction Process
In this section, we present the main phases of the
criminal domain-specific ontology extraction
process using Text2Onto. Actually, the process is
composed of two main phases: linguistic
preprocessing and extraction of modeling primitives.
In the following, we discuss briefly each phase and
the algorithms used to achieve the resulted domain-
specific ontology.
Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts
477
4.3.1 Preprocessing
The purpose of the preprocessing phase (Figure 6) is
to prepare the corpus and remove the ambiguity by
filtering out worthless symbols and words, in order
to extract meaningful textual content from the input
documents. In Text2Onto, there is a combination of
machine learning approaches with basic linguistic
processing such as tokenization or lemmatizing and
shallow parsing (Cimiano, 2005). In addition to this,
Text2Onto benefits from GATE by the integration of
JAPE that provides finite state transduction over
annotations based on regular expressions (Mädche,
2001).
Figure 6: Preprocessing phase in Text2onto.
4.3.2 Extraction of Modelling Primitives
In this section, we describe briefly the extraction
phase of the ontology modelling primitives. For this
purpose, Text2Onto implements series of algorithms.
Five main modeling primitives are considered in this
tool: concepts, instances, taxonomies, general
relations and disjoint axioms. In this section, the
extraction process of each primitive is discussed
briefly. For extracting concepts, three algorithms are
implemented. Based on experiments,
TFIDFConceptExtraction algorithm is selected. 486
single and multi-word concepts are extracted such as:
Probation, Criminal, Crime, Term Penalty and
Violence. Concerning the taxonomies (subclass-of
relations), Text2Onto provides three algorithms to
classify concepts based on Vertical Relations,
WordNet, and Patterns. For better results, the three
algorithms are combined.
Table 3: Excerpt of the hierarchies extracted using
Text2Onto.
Domain
Divorcee
Offender
Death penalty
Regarding the Instances, Text2Onto identifies
proper nouns as instances. Technically, it filters the
terms tagged as Instance from the GATE result.
Long list of instances are extracted such as Lebanon,
April and Friday. Text2Onto relies on
SubcatRelationExtraction algorithm to extract
general relations. This algorithm uses syntactic
pattern matching technique to extract general
relations.
Table 4: Excerpt of general hierarchies extracted using
Text2Onto.
Label
Domain
Range
involve
Residence
Placement
require
Activity
License
exceed
Offence
Bound
For the disjointness axioms, they are extracted in
Text2Onto based on lexico-syntactic patterns.
Table 5: Excerpt of disjoint axioms extracted using
Text2Onto.
Domain
Range
Confidence
Measure
Penalty
0.013
Felony
Disposal
0.013
Person
Association
0.06
4.3.3 Ontology Visualization
After applying the algorithms of Text2Onto, the
results are exported, as output, in OWL format.
Subsequently, we have looked for an ontology
visualization tool to visualize the resulted ontology.
Different tools are tested such as OWLViz
2
, a plug-
in for Protégé, and COE cmap tool
3
, and OWLGrEd
4
.
The resulted ontology is visualized correctly in
OWLGrEd (Figure 7).
Figure 7: Ontology visualization using OWLGrEd.
2
http://protegewiki.stanford.edu/wiki/OWLViz
3
http://coe.ihmc.us/
4
http://owlgred.lumii.lv/
ICAART 2017 - 9th International Conference on Agents and Artificial Intelligence
478
5 RELATED WORK
There are many works in the literature that proposed
the (semi-)automatic building of ontologies from
textual resources using ontology learning methods
and tools. The most related works are (Francesconi,
2010) in the legal domain and (Ortiz, 2007) in the
political domain. In the work of (Francesconi, 2010),
the authors have used two different tools for term
extraction: GATE for English texts and T2K for
Italian. The rest of the phases, such as evaluation of
terms and link them to concepts, extraction of
lexical relations were processed under the
supervision of ontology engineers and domain
experts. For the work of (Ortiz, 2007), the authors
applied Text2Ontofor creating domain ontology
from texts. They concentrated mainly in their study
on concepts extraction. In addition to this, the
authors proposed a reengineering methodology
based mainly on reusing online ontologies. What
differs our work first is the domain application,
which is the Lebanese criminal code. The context of
the code is composed of legal norms written in legal
language. Secondly, we used Text2Onto to extract
all the essential elements of a domain-specific
ontology. Finally, we expect to build an expressive
domain-specific ontology for reasoning system,
which is difficult using only an ontology learning
tool, for this reason we have proposed a
reengineering approach, based not only on online
ontologies, to correct the errors and to enrich the
extracted ontology with relations and axioms in
order to make it more expressive.
6 DISCUSSION
The aim of this paper if to extract domain-specific
ontology elements from texts using ontology
learning tool. Text2Onto is selected for this purpose.
The tool applies an automatic extraction process
based on list of algorithms and NLP techniques
using GATE applications. In addition to this, the
results can be exported as OWL ontology ready to
edit and update in ontology editor frameworks such
as Protégé. After applying list of algorithms to
extract the elements of the domain-specific ontology,
we obtained some results to discuss. Starting with
concepts, the tool extracted 486, single and multi-
word, concepts. The domain expert filtered the list
and removed the errors. We can resume the
identified errors in some examples. Some verbs like
stay, incur and abort were identified as concepts by
Text2Onto. Some domain-specific concepts were
identified as instances such as Confiscation,
Detainee and Terrorist. For the instances, the
extraction is limited because of the corpus quality.
Actually, the experiment is based on criminal code
written in legal language, which is authoritative and
contains legal speech acts accompanied by rituals of
various types. Text2Onto identified only 20 semantic
relations and 86 disjoint axioms. A reengineering
phase is needed to enrich the extracted ontology.
From this perspective, the reengineering
methodology is proposed to correct, enrich and
refine the resulted ontology and to build correct,
complete and more expressive domain-specific
ontology.
7 CONCLUSIONS
In this paper, we have briefly described the field of
ontology learning from textual resources as a
bottom-up approach for building a domain-specific
ontology for the criminal law. The mechanism of
ontology learning process from unstructured text
was identified. Furthermore, we have presented an
overview of the existent ontology learning methods
and tools. We also discussed our work followed by a
summarizing comparison of the ontology learning
tools used in our experiments. Based on the
experiments, Text2Onto is selected as a tool for the
ontology learning process. In fact, this tool answers
the main requirements of the study. Using
Text2Onto, the main elements of the domain-specific
ontology are extracted (concepts, taxonomies,
relations and axioms). The results were essentials,
but inexpressive. A reengineering process is needed
to build a more expressive ontology.
ACKNOWLEDGEMENTS
This project has been funded with support from the
European Union with the European Regional
Development Fund (ERDF), the National Support
from the National Council for Scientific Research in
Lebanon (CNRS) and Lebanese University
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