Ontology-Based Solution for Building an Intelligent Searching

System on Traffic Law Documents

Vuong T. Pham

1,2,4 a

, Hien D. Nguyen

3,4 b,*

, Thinh Le

3,4

, Binh Nguyen

2,4 c

and Hung Q. Ngo

Institute of Data Science and Artificial Intelligence, Sai Gon University, Ho Chi Minh City, Vietnam

Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam

University of Information Technology, Ho Chi Minh City, Vietnam

Vietnam National University, Ho Chi Minh City, Vietnam

Technological University Dublin, Dublin, Ireland

Keywords: Knowledge Base, Searching System, Traffic Law, Law on Road Traffic, Legal Document.

Abstract: In this paper, an ontology-based approach is used to organize the knowledge base of legal documents in road

traffic law. This knowledge model is built by the improvement of ontology Rela-model. In addition, several

searching problems on traffic law are proposed and solved based on the legal knowledge base. The intelligent

search system on Vietnam road traffic law is constructed by applying the method. The searching system can

help users to find concepts and definitions in road traffic law. Moreover, it can also determine penalties and

fines for violations in the traffic. The experiment results show that the system is efficient for users' typical

searching and is emerging for usage in the real-world.

INTRODUCTION

Nowadays, transportation is a need for everyone.

Almost every adult has a vehicle - the traffic is

increasingly complicated, especially road traffic. In

Vietnam, there are more than three million traffic law

violations, with more than 14,500 traffic accidents in

2020 (National Traffic Safety Committee, 2020).

Some cases have resulted in injuries or deaths. The

reason for those cases is that people have low

awareness of the rules of traffic law.

Ontology is an effective approach to representing

knowledge (Jakus et al., 2013). This model has been

used to organize knowledge in education and

healthcare (Do et al., 2018). Moreover, several

studies adopt ontologies to represent the knowledge

of legal documents, while other studies use ontology

to organize legal knowledge (Valente and Breuker,

1992, Fawei et al., 2019). However, they did not

mention the traffic law for searching its content and

determining penalties for violations.

* Corresponding author

https://orcid.org/0000-0002-3879-9677

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

https://orcid.org/0000-0001-5249-9702

https://orcid.org/0000-0001-8246-8392

This paper proposes a method for building the

knowledge base for Vietnam road traffic law

(Vietnam National Assembly, 2008, Vietnam

Government 2019). This method is applied to

construct a search system in this law. The designed

system supports users in finding the content of the law

related to their queries, and it can determine penalties

for violations in road traffic via this law. In addition,

the system helps to raise people's awareness about

traffic law.

The primary value of the designed system is the

ability to search for penalties and fines for road traffic

offenses based on the keywords of the inputted query.

Therefore, the system's knowledge base is organized

as a relational ontology, which includes concepts,

entities, their relations, and the rules of Vietnam Law

on road traffic. In order to do that, the knowledge

domain about road traffic law is collected and

classified into knowledge components: concepts,

relations, and rules.

The following section presents related works for

constructing relational ontology, especially in the law

Pham, V., Nguyen, H., Le, T., Nguyen, B. and Ngo, H.

Ontology-Based Solution for Building an Intelligent Searching System on Trafﬁc Law Documents.

DOI: 10.5220/0011635500003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 1, pages 217-224

ISBN: 978-989-758-623-1; ISSN: 2184-433X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

217

domain, and several search systems on legal

documents. Section 3 proposed an improved model of

Rela-model to represent the knowledge of the road

traffic code in Vietnam. Section 4 builds an

architecture and searching problems of an intelligent

querying system on Vietnam traffic code. The

designed system can support finding the content of

law related to the query and penalties for road traffic

offenses. The last section concludes the results of this

paper and gives some future works.

RELATED WORK

There are many studies to organize legal knowledge.

For example, Valente and Breuker (1992) stated three

approaches for the legal knowledge base: the logic

approach, the case-based approach, and the pragmatic

approach. Those approaches are used to build legal

ontologies and documents for data-retrieving systems

(Sator et al., 2011).

Ontology LIDO for Legal Informatics Document

is built based on the standard CEN Metalex (Sartor et

al., 2019). It represents legal actions that affect the

document, the legal temporal events, the structure of

the legal resource, and the semantic structure of

organization of legal documentss.

Ngo et al. (2021) proposed a method of data

augmentation based on legal domain knowledge for

the legal textual entailment. This method is used to

design a system for Vietnamese legal text processing.

Nguyen et al. (2022c) also proposed a training data

augmentation procedure and an unsupervised

embedding learning method to retrieve the legal

document. However, those proposed methods only

show the articles of a specified query and does not use

legal knowledge to explain its results clearly.

Pham et al. (2019) built an ontology-L for

representing the Law of Public Investment and

designed a consultant system for estimating the costs

of a project based on this law. In addition, an

intelligent chatbot was designed to tutor some

administrative procedures in printing licensing based

on the ontology Rela-Ops model (Nguyen et al.,

2020a). However, those methods are challenging to

apply in searching the content of a law document

related to the working domain.

There are some legal search systems in Vietnam,

such as the National Database of Legal Documents

(2022) of the Ministry of Justice and law library

(2022). However, these systems generally only allow

users to search for documents or entities with

keywords. However, they cannot help users find a

deeper search for legal documents in the real world.

For example, in traffic law, users need to search for

penalties and fines for a violation based on rules in

the legal document. Therefore, the current systems

are not suitable for supporting users in practice.

This study tends to build an intelligent search

system based on the ontology of the Vietnam road

traffic code. This ontology can be used to represent

the content of the law code and to deduce based on

the inference rules extracted from the code.

KNOWLEDGE BASE OF

VIETNAMESE TRAFFIC LAW

3.1 The Structure of the Vietnamese

Law on the Road Traffic

This section gives more details about the structure of

Vietnamese law on road traffic and the knowledge

model of the system. Through Vietnam National

Assembly (2015), the system of legal documents in

Vietnam has the following 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 National Assembly.

In general, a law document has a structure with

three parts: heading, content, and ending. The

heading shows the national name, the crest, number,

and sign of the document, enact place and date, type

and name of the document, and the basis of the

document. The content is a list of parts, chapters,

articles, clauses, and points. The ending is the signing

of the person that implements the document.

Inside the content, part is the highest level, then,

in order are chapters, sections, articles, clauses and

points. Through (Vietnam Ministry of Justice, 2011),

based on the type of document, there will be different

structures, for example some documents have

chapters, articles, clauses, and points but there is no

section. Each part, section, or chapter defines a

different factor. Below chapter are articles and

clauses which are used to define concepts, principles,

penalties, or regulations. If a clause needs more than

a sentence to define it, there will be several points in

addition to it.

Concepts in legal documents have two parts,

concept names and their definitions. For the offences,

each principle, penalty, or regulation which are

defined in articles and clauses of the legal document,

they always have the subject (the person or

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organization that participate or engage in the event),

a fact (or action) and penalties if there is any.

In particular, the Vietnamese traffic law has the

same structure as stated. Two legal documents

currently implement and have most effect in the

social are: Law on road traffic (Vietnam National

Assembly, 2008) which prescribes interpretation of

concepts, road traffic rules, regulations for vehicles

and users on the road traffic; Decree of

Administrative of penalties for road traffic offences

and rail transport offences (Vietnam Government,

2019) (known as Decree 100) which states penalties

and fines for administrative violations of road traffic.

In addition, there is National Technical Regulation on

Traffic Signs and Signals (Vietnam Ministry of

Transport, 2019) to define and describe the road

traffic signs.

3.2 Knowledge Model for Road Traffic

Law

Ontology Rela-model is a useful ontology

representing the knowledge of relations. This model

includes three components about concepts, relations

between concepts (Nguyen et al., 2015). It is effective

to represent knowledge domains in education,

consultant the finance method based on the

investment law. Rela-model includes three

components which are used to represent concepts,

relations between concepts and inference rules of the

knowledge domain.

For representing the knowledge of a legal

document, Rela-model has been improved the

structure of its concept-component being suitable the

legal domain (Nguyen et al., 2022a). The knowledge

model for Vietnamese road traffic law is based on the

concepts or entities and their relations. Each relation

of them defines an action or event of road traffic.

Based on those relations and rules of law on road

traffic, the issues about retrieving the information of

offences and their penalties have been also proposed.

Definition 2.1: The knowledge model for

representing the legal domain of road traffic is

improved from ontology Rela-model, named Traffic-

Law model. This model consists of three components

as follows:

(C, R, Rules)

In which, C is the set of concepts or entities of road

traffic law, R is the set of relations between

concepts/facts, Rules represent the inference rules to

specify the relation between concepts or determine

offences and their penalties. The structure of Traffic-

Law model is summarized as Figure 1.

Set C is the set of concepts and entities in road

traffic law. There are three kinds of concepts in C:

users and vehicles of road traffic; traffic signs and

signals; road infrastructure. Based on those kinds,

each concept c ∈ C has the structure:

(Name, Meaning, Attrs, Keywords)

where, each element has the type and meaning for

specifying the corresponding concept as Table 1:

Figure 1: The Traffic-Law model.

Ontology-Based Solution for Building an Intelligent Searching System on Trafﬁc Law Documents

219

Table 1: Structure of a concept.

Element T

pe Meanin

Name Tex

ame of the concepts

eanin

Tex

Meaning of the concepts.

Attrs Dict

List of attributes of the

concepts.

Keywords Set

Set of keywords

determined or related to

the concepts.

Example 1: The concepts “Electric motorcycle” in

(Vietnam Government, 2019) is described.

Element Content

Name Electric motorcycle

Meaning

“a two-wheel vehicle operated by

an electric engine with power not

exceeding 4 kW and maximum

speed not exceedin

50 km/h”

Attrs

ttrs = [kind, type, legal]

• kind: road traffic vehicle

• type: two-wheel vehicle

• legal: [Article 3, Clause 1, Point

d, Decree No. 100/2019/ND-CP]

Keywords

Motorcycle; electric; two-wheel

vehicle

Set R is the set of relations between concepts in

set C. These relations determine a specific fact or an

action of the road traffic code. Each relation r ∈ R has

the structure:

(Name, Conc, Meaning, Prop,Keywords)

where, each element has the type and meaning for

specifying the corresponding relation as Table 2:

Table 2: Structure of a relation.

Element T

pe Meanin

Name Tex

ame of the relation.

Conc List

List of parameters as

concepts of the relation.

eanin

Tex

Meaning of the relation.

Prop Set

Set of properties of the

relation. This study only

mentions two main

properties on a binary

relation: transitive and

mmetric.

eywords Se

Keywords of the relation.

Example 2: The relation “comply” of two concepts

“car” (or “car-like vehicles”) and “traffic light”,

denoted comply (car, traffic light), means “Operators

of car and car-like vehicles failed to comply with the

traffic lights”. Its keywords are “comply”, “over”.

Set Rules is a set of inference rules. Those rules

deduce relations between concepts or determine

offences based on road traffic law. Each rule r ∈

Rules has the form

u(r) → v(r)

where, u(r) is the hypothesis facts of rule r and v(r) is

the result of rule r.

The Rules-set is classified two kinds of rules:

Rules = Rule

infer

∪ Rule

offence

In which, Rule

infer

is the set of rules inferring the

relation between concepts, and Rule

offence

is the set of

rules determining offences and penalties.

3.3 Some Problems for Searching on

Traffic Law

Using the improved Rela-model, the knowledge base

for road traffic law has been organized. Based on this

knowledge base, the problems for searching on the

law document are studied. There are two issues for

searching on law, which are searching for the

concepts or definition of the law, especially the law

explanation, and determining offences and their

penalties and fines through the law document. To do

this, two searching problems need to be solved for

designing the intelligent searching system on the law

document:

Definition 2: The searching problems of an

intelligent searching system Traffic-Law model are:

• Problem 1: Extracting the keywords from the

inputted query to search the concepts and

relations in the legal knowledge base related

to the keywords.

• Problem 2: Retrieve the knowledge from the

knowledge base matching extracted concepts

and relations.

For solving Problem 1, the inputted query needs

to be classified. The input can be classified into two

kinds: query about meaning of a concept (“what is?”)

and query about the penalties & fines of an offence

(“how much”, “penalty”, “fines”). After that, from the

kind of the query, its main keywords are extracted. In

addition, some similar words for extracted keywords

are also achieved. The similar keywords can be

collected from legal document sources, experts (as

lawyers or legal lecturers), or from dictionaries. With

extracted keywords and determined similar words,

concepts related to those keywords are determined by

using rules in Rule

infer

. The process also finds

inference rules used to deduce concepts and their

relations.

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Algorithm 3.1: Given a law document d which

is already represented using ontology based on

Traffic-Law model.

Input: The knowledge base K = (C, R, Rules)

as Traffic-Law model.

Query q.

Output: A set of keywords, relations, and rules

retrieved from query q and knowledge K

Algorithm.

Step 1: Classify the query using Vietnamese NLP

toolkit

Step 2: Extract keywords from the query q and

find similarly words based on the knowledge

base K.

W :=keywords(q)

Step 3: Classify the kind of query based keywords

in W.

Step 4: Expands W with similar keywords

collected from legal sources.

Step 5:

G :={} // Set of concepts

P :={} // Set of rules

For each keyword w ∈ W do

• Using Rule

infer

to search concepts and rules

related to w.

• From found concepts, determine required

keywords and add them to G.

• Add rules to P if not exists

Step 6: Return (G, P) are results of found

keywords and rules.

For solving Problem 2, after identifying the

concepts and relations, the article of legal documents

that states the offence is found by using rules in

Rule

offence

. Then, the information, penalties, and fines

of it are retrieved through the specified content of law

in the knowledge base. The process for solving this

problem is as follows:

Given the knowledge base K of road traffic law in

legal documents as Traffic-Law model. This

algorithm will determine the information, penalties,

or fines of an inputted query q.

Algorithm 3.2: Given a law document d which is

already represented using ontology based on Traffic-

Law model

Input: The knowledge base K = (C, R, Rules) as

Traffic-Law model, and a query q.

Output: Information, penalties, and fines of road

traffic offence for query q.

Algorithm.

Step 1: Retrieve set of keywords G from query q

based on Algorithm 3.1

Concept := {c ∈ C | c related to keyword in G}

Step 2:

Knowledge :={}

For each concept c ∈ Concept do:

• Using rules in Rule

offence

to find the offence

in the knowledge base K.

• Retrieve the information, penalties, and

fines of the determined offence from the

specified law document.

• Update the results into Knowledge.

Step 3: Return Knowledge.

THE SEARCHING SYSTEM OF

VIETNAMESE LAW ON ROAD

TRAFFIC

4.1 Requirements of a Searching

System on Legal Documents

The intelligent searching system on legal documents

needs to be supported the understanding of users

about the legal domain. In road traffic law, moreover,

the ability for solving of necessary issues of the

searching system, this system has some criteria of

intelligent software evaluation in searching (Nguyen

et al., 2020b, Giakoumakis and Xylomenos, 1996):

o Portability: This is the level of difficulty to

work with the same project with different machines.

o Installation: The requirements of software,

hardware for the simulator, and how straightforward

is the installation in a supported system.

o Usability: this criterion shows whether the

content is suitable and detailed with the current law

domain and whether it is updated and easily to use in

the practice.

o Understandability: this is one of the most

important characteristics of intelligent law searching

software quality. This system has to help users

understand the law content in legal documents. It can

influence users’ feelings about software and reliability

of software evolution in reuse or maintenance.

Besides, the process for building this system is

worked through the constructing of a knowledge-

based system (Nguyen et al., 2022b). At first, the

databse of traffic regulations will be collected, and

orgnaized by Traffic-Law model as the knowledge

base of this system. After that, the searching

mechanism is designed through problems on traffic

Ontology-Based Solution for Building an Intelligent Searching System on Trafﬁc Law Documents

221

law searching and their alogrithms. Finally, the user

interface and testing of this system will be processed.

4.2 The Dataset of Traffic Regulations

The traffic regulation dataset is a combination of 2

documents:

1. Vietnam National Assembly, Law on Road

Traffic (known as 23/2008/QH12).

2. The Decree of Administrative of penalties

for road traffic offences and rail transport

offences (Vietnam Government, 2019),

abbreviated as Decree 100.

From both documents, there are 175 articles

collected. The general structure of these documents

is: Chapter – Section – Article – Clause – Point.

Traffic-Law model is used as an ontology to represent

this knowledge.

By default, questions about Vietnamese

transportation are classified into many intents. There

intents include but not limited to:

• Querying about concepts: These queries ask

definitions of concepts in the law. The system extracts

the apporiate article for the required concept.

• Querying about penalties: These queries ask

about the penalty or fines for a traffic violation, such

as running the red light, driving contrariwise, etc.

• Querying about procedures: The system give a

proceduce in traffic law, such as fine payment

procedure, the procedure for issuing driving licenses,

etc.

• Querying about signs: This function support

user to retrieve the information of an inputted sign.

This function related to image processing.

However, because the scope of this study, only the

kinds of querying about concepts and penalties are

focused in this paper. There are 160 practical

collected queries related to road traffic regulation.

These queries will be augmented and used for training

query intent classification in Problem 1.

4.3 The Architecture of the Searching

System on the Traffic Law

The architecture of the search system on traffic law is

presented in Figure 2. The system consists of the user

interface, the knowledge base, and the search engine.

The knowledge of the road traffic codes is

collected from (Vietnam Government, 2019, Vietnam

Table 3: Query Classification.

Class Meanin

Quantit

Concept

Require identifying the

meanin

of a concept.

Penalties

Require identifying the

fine of an offence.

Out of

scope

Queries that do not belong

to above kinds.

Total 160

National Assembly, 2008). These facts and entities of

those documents are organized as a knowledge base

by the improved ontology Rela-model and stored

inside a graph database. The similar words are

manually established via the collection of intellectual

experts and their experiences.

When a user inputs the query, the search engine

will execute the extract keywords tasks by Problem 1,

which are classifying the query, checking typo,

removing stop words, checking synonyms, and

checking equivalent keywords, to generate the query

values. From the extracted keywords, the similar

words will be determined through the knowledge base

Traffic-Law model. Those are used to search the

necessary knowledge by using inference rules of the

knowledge. In addition, their penalties and fines are

also retrieved by Problem 2. The result will be ranked

by the search engine before showing it in the user

interface.

4.4 Testing Results

Based on the knowledge base that has been organized

in Section 3 and the proposed architecture in Section

4.2, an intelligent searching system on Vietnam Road

traffic law is designed. This section presents some

testing results of the system through some kinds of

inputted queries.

Example 3: The inputted query q

= “What is

motorcycle?”

The system will extract keywords from the query

: “What is”, “motorcycle”. From that, it returns the

results as follows:

“Motorcycle means a motor vehicle that has two

or three wheels with a cylinder capacity of 50 cm

higher, maximum speed over 50 km/h, and net weight

not exceeding 400 kg.”

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Figure 2: The architecture of an intelligent searching system on the Vietnam road traffic.

The word “what is” is used to classify the query

into the kind of declaring the meaning of a concept.

The keyword “motorcycle” helps to find the concept.

The result is retrieved from Article 3, Clause 3.31 of

National Technical Regulation on Traffic Signs and

Signals (Vietnam Ministry of Transport, 2019).

Example 4: The inputted query q

= “The fines of

operator of motorbike driver who does not wear

helmet”

The keywords of the query q

are “fines”, “not

wear”, helmet”, “operator of motorbike”. The word

“fines” is used to classify the query into stating

penalties and fines of offences. The word “operator

of motorbike” consists of “motorbike” that is similar

to the word “motorcycle”. The word “helmet” is in the

keywords of the concept “motorcycle helmet”.

Hence, the concepts of the query q

are “operator of

motorcycle” and “motorcycle helmet”. The relational

keyword is “not wear”. With the concepts and

relation, the rules were used to match them and find

the result.

The result is returned:

“Through article 6, Decree 100/2019/ND-CP:

Penalties imposed upon operators of mopeds and

motorcycles (including electric motorcycles) and the

like violating road traffic rules.

2. A fine ranging from VND 200,000 to

VND 300,000 shall be imposed upon a vehicle

operator who commits any of the following

violations:

i) The operator or the passenger on the vehicle

does not wear a motorcycle helmet or does not wear

it properly;”

The designed system can do some common

searching on road traffic law. It is effective in finding

usual penalties and fines from road traffic law. This

system was tested on a set of 137 queries about the

road traffic codes. The results are shown in Table 4:

Table 4: Results for testing of queries.

Kind Quantity Correct Rate

Queries about

concepts /

definitions

54 42 78%

Queries about

penalties and fines

83 61 73%

Total 137 103 75%

CONCLUSION AND FUTURE

WORK

This paper proposed an ontology-based model for

representing legal knowledge in the Vietnam road

traffic codes. This model is improved based on

ontology Rela-model in the structure of concepts,

relations, and inference rules. Through the designed

knowledge base, several searching issues on the

Vietnam road traffic codes are proposed, such as

extracting keywords and inferring the matched result

for inputted query. Moreover, the architecture of an

intelligent search system on road traffic law has been

constructed. This system can do several common

search queries, such as finding concepts/definitions in

the law and determining penalties for violations in the

road traffic. At the moment, most knowledge is

Ontology-Based Solution for Building an Intelligent Searching System on Trafﬁc Law Documents

223

collected by manual collection method. The next

work is the improvement of the collection method

within by using an automatic method.

In the future, the system can be involved other

legal aspects such as commercial law, civil law, etc.

Further, the system can be used to provide an e-

learning system for legal aspects. The abilities to use

AI to identify entities and concepts from an image or

use voice recognition to identify searching input are

also features considered to add more to the system.

ACKNOWLEDGMENT

This research was supported by The VNUHCM-

University of Information Technology's Scientific

Research Support Fund.

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