Legal-Onto: An Ontology-based Model for Representing the

Knowledge of a Legal Document

Thinh H. Nguyen

1,2

, Hien D. Nguyen

1,2 a,*

, Vuong T. Pham

, Dung A. Tran

and Ali Selamat

5,6 b

Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam

Vietnam National University, Ho Chi Minh City, Vietnam

Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Vietnam

Faculty of Software Engineering, University of Information Technology, Ho Chi Minh City, Vietnam

Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove,

Rokitanskeho 62, Hradec Kralove 50003, Czech Republic

Keywords: Ontology, Law Document, Knowledge Representation, Semantic, Intelligent System, Information Retrieval.

Abstract: In the legal knowledge domain, legal norm documents are general rules which are mandatory for people in a

certain field. Many regulations are affecting to activities in a field. Ontology is an effective approach for

representing practical knowledge domains. In this paper, an integration ontology for representing the

knowledge of a law document is proposed. This model is integrated of ontology about relational knowledge

and the graph of keyphrases as a conceptual graph. It can represent semantic of contents in the law document.

Based on this integrated model, the improvement method of self-attention network by language-oriented

semantic analyzing is studied for intellectual retrieval on the law document. Moreover, the proposed method

is applied to construct an intelligent support system for knowledge querying on Vietnam Land Law. It can

help users to query some meaning of terminology in land law and some land-related administrative procedures.

1 INTRODUCTION

In the legal knowledge, legal norm documents which

issued by the government contain general rules that

are mandatory for people in a certain field (Casellas,

2011, Sartor et al., 2011). For each field, many

regulations affect to activities in that field. Those

regulations have a complicated relationship with

other regulations: the regulations which are issued by

the administrative unit depend on the regulations of

the superior unit, and also are affected by the

regulations of other related units in that field.

In Vietnam, the system of legal documents has

many levels: 1/ The highest validity is Constitution;

2/ Codes/Laws and resolutions of National Assembly;

3/ Sub-law documents for instructing the detail of the

law established by Vietnam National Assembly.

Thus, a support system for intellectual retrieval on

law knowledge is very necessary for people.

* Corresponding author.

https://orcid.org/0000-0002-8527-0602

https://orcid.org/0000-0001-9746-8459

Nowadays, there are many systems organize the

database of law documents (Leone et al., 2018, Fawei

et al., 2019). Szostek and Zatucki (2022) introduced

some information tools in the administration of

justice. The CEN Workshop on an Open XML

Interchange Format for Legal and Legislative

Resources (Metalex) developed the standards for

representing sources of law and references to sources

of law in XML (Sartor et al., 2011). It can answer to

the urgent request to normalize the abundance of local

legal XML dialects. Nonetheless, those systems still

have some limitations to represent the semantic of

those documents. This led to current searching

systems on law domain only can retrieve some

articles in law document and have not yet retrieve

deeper in content, such as administrative procedures

about a determined service.

426

Nguyen, T., Nguyen, H., Pham, V., Tran, D. and Selamat, A.

Legal-Onto: An Ontology-based Model for Representing the Knowledge of a Legal Document.

DOI: 10.5220/0011066300003176

In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineer ing (ENASE 2022), pages 426-434

ISBN: 978-989-758-568-5; ISSN: 2184-4895

In this paper, an ontology for representing the

knowledge of a law document is proposed. This

model is the integration of ontology Rela-model,

which is useful to represent relational knowledge

domains (Do et al., 2018), and the graph of key

phrases as a conceptual graph (Shi et al., 2018). The

integration model, called Legal Rela-model, has the

foundation including concepts in law domain,

relations between concepts, inference rules of this

domain and relations between key phrases, concepts

in law document and database storing law contents.

This model can represent complex relations between

concepts in a law document to retrieve some required

knowledge to people. Besides, the method for

intellectual retrieval on the law document is

proposed. The improvement of self-attention network

(Vaswani et al., 2017) is presented by language-

oriented semantic analyzing in Vietnamese (Nguyen

et al., 2020a). This technique is used to extract key

phrases of a law document.

Moreover, the proposed method is applied to

construct an intelligent support system for querying

on Vietnamese land law (Nguyen et al., 2020c) with

its knowledge base is organized by ontology Legal

Rela-model. This system can help users to query

some meaning of terminology in land law and some

land-related administrative procedures. It also tested

by major lawyers and got positive feedback from

users.

The next section presents some related work

about methods for organizing the document

repository, especially for law documents. Section 3

proposes a knowledge model for representing the

content of a law document, called Legal Rela-model,

based on the integration of ontology Rela-model and

the conceptual graph of key phrases. Section 4

designs the method for solving problems about

querying knowledge content of the law document by

Vietnamese. Section 5 shows the architecture of a

support system in land resource for querying on

Vietnamese Land Law and its testing results. The last

section concludes and presents some future work.

2 RELATED WORK

The law document is a general rule of conduct,

commonly binding on agencies, organizations and

individuals nationwide or within a certain

administrative unit (Vietnam Ministry of Justice,

2011, Nguyen et al., 2022). With a determined

domain, there are many documents related together

impacting to that domain. Ontology is an effective

approach to organize semantic document repository

(Huynh et al., 2019, Doan et al, 2003). However,

those methods are not suitable to organize law

documents.

LIDO is an ontology for Legal Informatics

Document (Sartor, 2019). This ontology can be

represented the legal actions affecting the document,

the legal temporal events, the structure of the legal

resource, and the semantic structure of the legal

document organization.

Huynh et al. (2019) constructed the integrating

method of an ontology describing domain knowledge,

and a database of document repository. This method

includes a model of domain knowledge for various

information retrieval tasks, called The Classed

Keyphrase based Ontology (CK-ONTO).

Nonetheless, this graph-based measure has not been

used to evaluate the semantic relevance in documents.

Ngo et al. (2021) designed a system for

Vietnamese legal text processing by leveraging the

strength of traditional information retrieval methods

(BM25), pre-trained masked language models

(BERT), and legal domain knowledge. They also

proposed a novel data augmentation method which is

based on legal domain knowledge in the legal textual

entailment. However, the proposed method does not

represent the semantic of the legal document.

The chatbot in (Nguyen et al., 2020c) was

designed to tutor some administrative procedures,

such as how to get a printing license. However, this

system cannot support to query the content in a law

document related to the working domain.

Statistical relational learning (SRL) and graph

neural networks (GNNs) are two powerful

approaches for learning and inference over graphs.

Typically, they are evaluated in terms of simple

metrics such as accuracy over individual node labels.

The study in (Embar and Srinivasan, 2021) proposed

a sampling framework to tractably compute the

values of aggregate graph queries (AGQ). That

method only works on information of social network

and cannot be used for organizing the meaning of a

legal document.

Ontology is a useful method for representing the

knowledge domain and searching on it (Do et al.,

2020). This study presents a method for organizing

the content of a law document and its meaning in each

article by integrated ontology. It is the foundation to

design techniques for querying some meaning of law

terminology and some administrative procedures in

Vietnamese.

Legal-Onto: An Ontology-based Model for Representing the Knowledge of a Legal Document

427

3 KNOWLEDGE MODEL OF

RELATIONS FOR LAW

DOMAIN

The system of Vietnamese legal documents includes:

• The Constitution: the highest legal-valued

document for constructing other documents.

• Codes/Laws and resolutions of National

Assembly: In a determined domain, this document is

a general rule of conduct, commonly binding on and

applied repeatedly to agencies, organizations and

individuals nationwide or within a certain

administrative unit in this domain.

• Sub-law documents: Those are documents

instructing the detail of the law established by

National Assembly. Some of sub-law documents are:

Decrees of the Government, Decisions of Prime

Minister, Circulars or Joint circulars of ministers who

are related to the scope of the law, Decisions of

provincial-level People’s Committees, etc.

This section presents a model to represent a law

document by its content and its meaning. This model

is improved based on Rela-model, called Legal Rela-

model. This ontology is an integration between

ontology Rela-model representing the knowledge of

law and a conceptual graph representing relations

between legal key phrases. Moreover, the ontology

Legal Rela-model is connected to the structure of

Vietnamese legal document.

At the article 11 of the circular of Vietnam

Ministry of Justice (2011), the structure of a law

document is one of the followed kinds:

a) Part, Chapter, Section, Article, Clause,

Point; or

b) Chapter, Section, Article, Clause, Point; or

c) Section, Article, Clause, Point; or

d) Clause, Point.

Based on those structures, the database for

contents of a law document can be organized.

Through that database, the knowledge of the law

document can represent by ontology Legal Rela-

model.

Definition 3.1: The ontology for representing a

legal document, called Legal Rela-model, consists of

components as follows:

K = (C, R, RULES) + (Key, Rel, weight)

In which:

 (C, R, RULES) is a structure of Rela-model

(Do et al., 2018, Nguyen et al., 2020b), where C is a

set of concepts, but each concept in C has been

improved its internal structure to organize its law

information; R is a set of relations, those relations are

between concepts, key phrases and database storing

the content of the law document; and RULES is a set

of inference rules of the knowledge domain.

 (Key, Rel, weight) is a conceptual graph

representing the relations between key phrases of

legal documents. In which, Key is a set of key phrases

of the law document, Rel is the set of arcs, and weight

is a map from Key to binary similarly vector.

3.1 C – The Set of Concepts

The law includes general rules constituted based on

concepts which are taken for granted. In the real-

world, a concept in a legal document is defined based

on its structure and relations in articles of the law

document. The followed definition is about the

structure of a concept in each law document d.

Definition 3.2: The structure of a concept

Each concept in C consists of five elements:

(Name, Content, InnerRel, Phrases, Attributes)

where:  Name: The name of the concept in the law.

 Content: Content or meaning of the concepts.

 InnerRel: List of articles in the document d

related to the corresponding concept.

 Phrases: The list of key phrases related to

concepts in each article of the document d.

 Attributes: List of components (or other

concepts) which are the foundation to build

the corresponding concept (if necessary) in

the document d.

Example 3.1: With the Vietnam Land Law 2013

in (Vietnam National Assembly, 2013), the

components of the concept “Certificate of land use

rights” are:

Component Content

Name Certificate of land use rights

Content

Certificate of land use rights is a legal

certificate in which the State certifies the

lawful land use rights and ownership of houses

and land-attached assets of the person who has

land use rights and ownership of houses and

lan

-attached assets.

InnerRel

Article 3, Point 16.

Article 11, Point 1.

Article 75, Point 1.

Article 97, Point 1,2.

Article 100, Point 1,2,3,4,5.

Phrases

land use rights, ownership of houses, land-

attached assets, lan

-attached houses

Attributes

Land use rights,

Inheritance land-attached assets,

Donation land-attached assets,

Hand-over of land-attached gratitude house,

Transfer of land use rights,

Purchase of residential land-attached houses,

Liquidation of residential land-attached

houses.

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428

3.2 R – The Set of Relations

Set R is classified to three kinds of relations:

R = R

concept

 R

keyphrases

 R

database

 R

concept

is a set of relations between concepts in

C. Those relations are “is-a”, “has-a”, “a-part-of”,

and other relations between concepts.

concept

 C  C

Some properties of each relation r 

concept

are considered: symmetric, transitive.

 R

keyphrases

is a set of relations between key

phrases in the law document. It also includes some

relations between key phrases and a concept which

are characteristic to determine the concept’s meaning.

keyphrases

 Key  Key

and R

keyphrases

 Key  C.Phrases

 R

database

is a set of relations between concepts

and keyphrases which are connected to database of

the law document.

3.3 RULES – The Set of Rules

The rules in the RULES-set represent the constraint

and inferring relation between keyphrases, and

concepts. Using deductive rules helps to reduce

workload of a knowledge engineer when building

ontology data. The RULES-set deduces the direct or

indirect relationships between key phrases or

concepts which are used to determine the semantic

similarity among key phrases and concepts.

A rule r  RULES is a deductive rule on facts

related to key phrases and concepts. It can be

described as follows:

r: {f

, f

,…, f

} {g

, g

,…, g

}

with {f

, f

,…, f

}

are hypothesis facts and {g

,…, g

}

are goal facts of the rule. There are three

kinds of facts:

Table 1: Kinds of Facts.

Kind Meanin

ecification

Show a property of

a relation

[<rel> is < property >]

rel  R is a relation.

Relations between

concepts.

[<c

><rel><c2>]

, c

C

Relations between

key phrases.

[<k

><rel><k



Relations between

key phrases and a

conce

[<k><rel><c.Phrases>]

 Key, c  C

Example 3.2: Some rules in the domain:

: if [ is symmetric] and [k

 k

] then [k

 k

]

: if [ is transitive] and [k

 k

] and [k

 k

] then

 k

]

3.4 (Key, Rel, weight) – The

Conceptual Graph

The structure of Rela-model (C, R, RULES)

organizes the knowledge of a law document.

However, in the practice, when retrieval a content of

law, there are some main key phrases in the query

sentence has been connected to the knowledge

through their semantic. In this study, the semantic of

key phrases are organized by a conceptual graph.

Definition 3.3: Given a document law d. The

structure of the graph representing relations between

key phrases in the document d is a tube:

(Key, Rel, weight)

where:  Key = {k | k is a key phrase of the legal

document}.

 Rel = {e = (k

, k

)  Key  Key | k

are k

are key phrases appearing in the same article of the

law document}

 weight: Key  R  R is a map to compute

the similarly binary vector for each key phrases in

Key. (R is the set of real number).

The measure for similarly key phrases is computed

by the tube (tf(v, d), idf(v, d)), where tf(v, d) is the

term frequency representing the frequency of a key

phrase v in a document d, and idf(v, d) is the inverse

articles frequency representing the specificity of the

key phrase v in the document d. The formulas of (tf(v,

d), idf(v, d)) are established as follows (Le et al.,

2019):

( , ) : (1 ) (1)

max{ | ' }

tf v o c c

nvKey

 



where, n

v,o

is the number of occurrences of the key

phrase v in the document d,

c  [0, 1] is a parameter which is the minimum

value for every key phrases.



()

card ( )

(, ): log

1card{ ()| ( )}

ar Article d

keyphrases ar

idf v d

ar Article d v keyphrase ar













 







(2)

where, Article(d) is the set of articles of the law

document d.

keyphrase(ar) is the set of key phrases of the

article ar in the document d.

Legal-Onto: An Ontology-based Model for Representing the Knowledge of a Legal Document

429

Figure 1: The structure of Legal Rela-model.

Figure 1 represents the structure of the ontology

Legal Rela-model. In this structure, the Rela-model is

combined with the graph of key phrases via key

phrases and their relations. When key phrases are

objects in the land law, those relations between them

are behaviours of them which were determined in

law.

4 KNOWLEDGE QUERYING ON

A LEGAL DOCUMENT

Let K be a knowledge domain of the law document d

as ontology Legal Rela-model, and the database of the

document d. When a query is inputted to retrieve the

knowledge from K , the searching system will process

to extract some main key phrases and compare with

the structure of the knowledge model’s components

through relations between key phrases leading to

corresponding concepts. In the matching process,

inference rules of the knowledge base help to deduce

more relations related to the content of the query and

retrieve results for the inputted query. Some of main

problems are as follows:

(1) Problem 4.1: Classifying the inputted query.

From the query inputted as Vietnamese text, this

problem extracts the main key phrases of the query to

determine the meaning of the query and classify it.

(2) Problem 4.2: Retrieving suitable articles in the

document and searching the content of concepts

based on matching the key phrases. Based on

extracted key phrases, a method to compare the

similarity between the meaning of the key phrases

and the content in the knowledge base is proposed.

For the problem 4.1, in Vietnamese, the structure

of a sentence includes subject and predicate. In this

study, we only mention to the declarative sentence

type. Besides, the query sentence is also classified

into five kinds: queries about concepts/definitions in

the Land Law, queries about procedures of this law,

and queries about some knowledge related to current

results. The solution for this problem is designed

similarly to (Nguyen et al., 2021). Hence, this section

presents the method to retrieve suitable articles in the

document and searching the content of concepts

based on matching the key phrases in the problem 4.2.

After extracting the key phrases and intents of the

utterance, the system will match those key phrases

with the content of the knowledge for defining and

comparing texts based on ontology Legal Rela-

model. The matching technique for the search engine

can be designed based on the solution in (Nguyen et

al., 2021), but it has some improvements as Figure 2.

Figure 2: The matching technique for search engine.

After classifying of the inputted query, this

process also extracts main key phrases of the query.

Those key phrases are used to retrieve the suitable

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430

content from the knowledge base in Problem 4.2. The

key phrases dictionary is established from the

knowledge base of the search system and from the

experts in the law domain, such as lawyers, senior law

employees, senior lecturers in the law domain.

Example 4.1: Some key phrases in the dictionary

of the Vietnamese Land Law 2013 in (Vietnam

National Assembly, 2013):

 Some individuals in the dictionary: Cadastral

map, Transfer of land use rights, Certificate of land

use rights, kinds of agricultural land (Land for

cultivation of annual crops, Land for cultivation of

perennial trees, Production Forest land, Aquaculture

land, etc.), non-agricultural land (Residential land,

Land for construction of offices, Land used for

public purposes, etc.).

 The synonyms of a key phrases in the

dictionary: “What is” is equivalent to “Define”,

“How to use” is equivalent to “Usage”.

The extracted key phrases of the utterance are

compared with the dictionary to create a set of key

phrases. The search engine uses those key phrases to

retrieve the knowledge of law from the knowledge

base. The system also recommends some related

knowledge through relations of obtained results and

inference rules of the knowledge base.

Algorithm 4.1: Given the law document d which

represented by the ontology Legal Rela-model.

Input: a query q.

Output: The set of knowledge content in the

document d which matches the meaning of query q.

Step 1:

Extract keyphrases from the query sentence

and establish set of key phrases of q.

W := keyphrases(q)

Expand the set of key phrases W based on

relations in R

keyphrases

Update the set W.

Mapping keyphrases in W to the sub-graph G

of the conceptual graph (Key, Rel, weight)

with a weight vector for each key phrases.

S t e p 2 : Knowledge := {} // set of results.

Concept :={};

For each phrase  G do

 Use relations in R

keyphrases

and inference

rules in RULES for linking phrase

with a corresponding concept c  C.

 Update c into Concept.

Expand the set of concepts in Concept:

 Based on relations in R

keyphrases

Update the set W.

 Retrieve knowledge from components of

each concept c  Concept

Update Knowledge.

S t e p 3 :

Unification of facts in the knowledge model

and compare the meaning using Problem

Update Knowledge.

Step 4: Return results in Knowledge

The searching for the knowledge content returns

a set of knowledge based on the meaning of an

inputted query. The system determines the meaning

of this query from its extracted keywords and

comparing by using stored knowledge.

5 THE QUERYING SYSTEM FOR

VIETNAM LAND LAW

Land resource is one of important resource in

Vietnamese economic. It is attracted by investors

because of its stability and increasing of value during

the time (Tran, 2013). Legality is one of key factors

determining the value of a land.

Figure 3: The architecture of the querying system on land law.

Legal-Onto: An Ontology-based Model for Representing the Knowledge of a Legal Document

431

The land law is a document promulgated by a

state agency, and contains general rules for

conducting, commonly binding on agencies,

organizations in the domain of land (Vietnam

National Assembly, 2013, Tran, 2013). It will have

some sub-law documents to detail instruct the process

of this law. In this section, a querying system for the

land law in Vietnam is designed. Its knowledge base

is constructed based on the Vietnamese Land

(Vietnam National Assembly, 2013) by using

ontology Legal Rela-model. This system can help

users to query some meaning of terminology in land

law and some land-related administrative procedures.

The structure of this system is presented in Figure 3.

5.1 Dataset and Organizing the

Knowledge Base of Land Law

Document

The Vietnamese Land Law 2013 includes 14 chapters

with 212 articles (Vietnam National Assembly,

2013). It is a general rule for all working in the land

domain. The content of this law is organized by a

database as the structure of Chapter – Section –

Articles – Paragraph - Point. Ontology Legal Rela-

model is used to represent it’s content and meaning.

There were 625 collected queries to training for

classifying by Problem 4.1. It includes 521 in-scope

queries covering three intent classes and 104 out-of-

scope queries. Table 2 classifies the training queries

and the results for training them in collected dataset.

Table 2: Classification of queries.

Class Meaning Training Tested

Concept

Require to determine

definition or attributes of

a concept

211 54

Procedure

Require to determine list

of documents for a

procedure in land law.

93 24

knowledge

Require knowledge

related to obtained results

107 32

Out of scope

Queries related to real-

estate but they do not use

land law.

83 21

Total 494 131

In-scope data were collected from the frequently

asked questions (FAQs) in land law by the consulting

of major lawyers (FAQs, 2022). The intents were

grouped on the basis of the scope of the system in this

study. Out-of-scope data were collected from FAQs

related to real-estate but they do not use the

knowledge of land law.

5.2 Search Engine

When a query sentence is inputted, its key phrases are

extracted. Using the knowledge as ontology Legal

Rela-model, some rules and relations will be applied

to get more some related key phrases and their

relations. By the problem 4.2, the set of knowledge

content in the document d which matches the

meaning of those key phrases is retrieved. The results

are articles or content from the Land Law.

5.3 Experimental Results

This study implements the experiments about

querying on some meaning of terminology in land

law and some land-related administrative procedures.

Its knowledge content is splitted into 05 kinds:

 Kind 1: Organize to manage land resource.

 Kind 2: The legal position of land users.

 Kind 3: Agricultural land.

 Kind 4: Non-agricultural land.

 Kind 5: Documents of Land-related procedures

When users inputt their queries, the system

classifies those queries into concepts, procedures or

related knowledge; then it retrieves results for users.

The process of theis system was checked by a lawyer

and a law lecturer in land resource. Table 3 and Figure

5 show the testing results for each topic.

Table 3: Results of Querying on Vietnam Land Law.

Content Queries

Number of correct results

Prop.

(%)

Concept Procedure

Knowledge

Total

1 42 11 9 6 26 62%

2 48 10 10 7 27 56%

3 59 19 15 5 39 66%

4 36 9 8 5 22 61%

5 24 5 7 4 16 67%

Total 209 54 49 27 130 62%

Table 4 compares our system with some good

systems, Aleph and AimeLaw, in ALQAC-2021

(Automated Question Answering Competition) at the

task 1 - Legal Document Retrieval, and the task 2 -

Legal Text Entailment (ALQAC-2021, 2021).

 Task 1 - Legal Document Retrieval: The

requirement of this task is the retrieval of all the

articles that are relevant to a statement.

 Task 2 - Legal Text Entailment: This task is

built for yes/no question answering systems for legal

queries. The system will answer whether the

statement is true or false.

Tieu et al. (2021) built Aleph as an article ranking

model by finetuning their own pre-trained model

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432

VNLawBERT with a binary classification problem

(Chau et al., 2020). It makes negative samples by

choosing the closest candidate with the gold samples.

AimeLaw in (Ngo et al., 2021) is an approach of

combining scores of BM25 with Domaint Invariant

Supporting Model and Deep CNN Supporting Model

using weighted sum function.

Figure 5: The precision of the querying system on each

content of Vietnamese Land Law.

Table 4: Compare systems in tasks.

stem Task 1 Task 2

Aleph 88.07% 69.89%

AimeLaw 80.61% 69.89%

Our s

stem 62% 62%

Although the precision of our querying system

gets more than 60%, it can retrieve the concepts in the

document with related articles. Besides, some kinds

of content in the Land Law have the precision more

than 65%, such as Agricultural Land and Documents

of Land-related procedures.

Moreover, the strengthen point of the built system

is the organizing of the knowledge domain about the

law document. It can represent the complex relations

between legal entities in the document. It has more

rooms to develop a querying system for supporting of

a certain law field with many related legal documents.

6 CONCLUSIONS

This study proposed an integrating ontology for

representing the knowledge of a law document, called

Legal Rela-model. This model is integrated of

ontology Rela-model and the graph of key phrases as

a conceptual graph (Shi et al., 2017). The Legal Rela-

model includes concepts in the law domain, relations

between concepts, inference rules of this domain and

relations between key phrases, concepts in the law

document which connects to database storing law

contents. The method for intellectual retrieval on this

document is also studied by extracting key phrases

and matching the content of articles in the law

document.

In addition, an intelligent support system for

querying on Land Law of Vietnam National

Assembly (2013) is constructed. Its knowledge base

is organized by ontology Legal Rela-model. The

designed system can help users to query some

meaning of land law terminology land-related

administrative procedures. The testing results show

that the precision of the current method is more than

60%.

Moreover, the law domain of a field includes

many related documents. The advantage of Rela-

model is the ability to integrate multiple knowledge

domains. Thus, using ontology Legal Rela-model, the

connection between legal documents can be

represented in which each document is organized by

this ontology. This will make a completely legal

document system for a certain field.

ACKNOWLEDGMENT

This research was supported by The VNUHCM-

University of Information Technology's Scientific

Research Support Fund.

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