A SURVEY ON AGENT-BASED ONTOLOGY ALIGNMENT

Maxim Davidovsky

, Vadim Ermolayev

and Vyacheslav Tolok

Department of Mathematical Modeling, Zaporozhye National University, Zhukovskogo 66, Zaporozhye, Ukraine

Department of Information Technologies, Zaporozhye National University, Zhukovskogo 66, Zaporozhye, Ukraine

Keywords: Ontology, Ontology Alignment, Ontology Matching, Ontology Mapping, Intelligent Agent, Meaning

Negotiation.

Abstract: Ontologies today are increasingly used as consensual knowledge representations in many distributed

applications. However, if a system of knowledge based nodes is decentralized, the ontologies at those nodes

differ. Therefore the alignment of knowledge representations is required. One of the promising approaches

to solve this heterogeneity is the use of agents for aligning knowledge representations. The paper presents a

brief survey of the approaches to agent-based ontology alignment. The analysis of these approaches is

grounded on the analysis of the requirements to ontology alignments by typical applications that address

semantic heterogeneity in open and decentralized settings.

1 INTRODUCTION

Nowadays ontologies are used in many applications

for knowledge management, e-commerce,

information retrieval and sharing, etc. One of the

promising approaches in application scenarios that

require operating knowledge representations is the

use of intelligent software agents capable of

processing ontologies in order to achieve specific

goals. Typically in open and decentralized systems,

such as the Semantic Web (Berners Lee et al., 2001)

different agents possess varying ontologies. These

semantic differences entail wrong processing or

possible misunderstanding between agents.

Ontologies may evolve in time – differently at

different processing nodes. Moreover, different

parties in an application encounter are not aware of

the changes occurred in ontology evolution. The

heterogeneity of knowledge representations

amplified by the distortion caused by uncoordinated

changes makes the use of knowledge in distributed

intelligent applications a challenging problem. A

possible solution is the use of ontology matching

that discovers possible mappings between concepts

forming respective ontologies (Euzenat and Shvaiko,

2007). The result of matching is ontology alignment.

It may be argued that the challenge mentioned

above is artificial and arises only because the

scenarios are over-complicated by the use of

distributed ontologies. Unfortunately this is not true

and ontologies are a means to solve a number of

problems such as domain analysis, knowledge

sharing and reuse, etc (Bermejo-Alonso et al., 2006),

(Huhns and Singh, 1997). Consequently, ontologies

are a means to provide a semantic foundation for

solving interoperability challenge in open distributed

settings, using agent paradigm in particular. A

number of proposed solutions is based on the use of

a common shared ontology(-ies) that can be

instantiated by agents for a particular encounter

(Van Aart et al., 2002), (Tamma, Wooldridge and

Dickinson, 2002), (Mascardi et al., 2007). The two

major uses of ontologies are outlined by Dong,

Hussain and Chang (2008).

The first one assumes that ontologies

conceptualize the protocols for guiding agents’

behaviour in interactions. The problem in this case is

the change of interaction protocols to apply new

negotiation goals and strategies reflecting the

varying nature of environment(s). Different agents

can also use different negotiation terms causing

ambiguity or misinterpretation of communicated

content in interactions.

Consequently the second case is the use of

ontologies as shared vocabularies for resolving

content heterogeneity problems in agents

communications, for example to carry out

translations between ontologies (Obitko, 2007).

A typical difficulty however is that a central or

shared ontology or vocabulary is rarely available.

355

Davidovsky M., Ermolayev V. and Tolok V..

A SURVEY ON AGENT-BASED ONTOLOGY ALIGNMENT.

DOI: 10.5220/0003748603550361

In Proceedings of the 4th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2012), pages 355-361

ISBN: 978-989-8425-96-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

So, the communicating nodes in a decentralized

system have to align their knowledge representations

and respective interpretations. Many of the

published solutions rely on the existence of a

centralized authority that provides such aligned

interpretations. In agent-based approaches a

specialized agent offering an ontology alignment

service is typically involved (e.g. (Cardoso, Teixeira

and Oliveira, 2008)). However, it is typically

considered that the ontology mappings leading the

required alignment are somehow available off the

shelf.

Interestingly, ontology alignment in its essence

is the problem that has the same nature as the

interoperability challenge in open and decentralized

systems. So, it may be expected that it may be

solved using a similar approach – i.e. having a good

solution technique for ontology alignment will

facilitate solving the mentioned interoperability

challenge. In this paper the frameworks and existing

agent-based solutions for ontology alignment are

surveyed. The paper starts with the explicit formal

definition of ontology alignment. It continues with

the analysis of the known challenging applications

that involve semantic heterogeneity and require

interoperable solutions. Our focus in examining

those cases is the severity of the requirement of

using the alignments of knowledge representations.

Finally, the paper overviews and analyses the agent-

based frameworks that aim to solve ontology

alignment. Some conclusions on the state of the art

in the field are drawn out of those analyses.

2 ONTOLOGY ALIGNMENT

AND APPLICATIONS

The analysis of the literature on ontology alignment

reveals some terminological ambiguity. Some

authors e.g. Abolhassani, Hariri and Haeri (2006),

Euzenat (2004a) denote ontology alignment as a

process of finding correspondences between

ontology entities. Other publications e.g. Euzenat

and Shvaiko (2007), Gargantilla and Gomez-Perez

(2004) define this process as ontology matching and

regard alignment as a result of matching. The key

concept of “ontology” is also denoted in different

ways depending on the viewpoint. In our research

the following notions are used.

Following Euzenat and Shvaiko (2007), an

ontology is formally denoted as a tuple

=∈⊥≤= ,,,,,,,, VTIRCo

where

C is the set of

concepts (or classes);

is the set of relations

(object and datatype properties);

is the set of

individuals;

is the set of datatypes;

is the set

of values;

≤

is a reflexive, anti-symmetric and

transitive relation on

(

)( )( )

TTRRCC ×∪×∪

called

specialization, that form partial orders on

C and

called concept hierarchy and relation hierarchy

respectively;

⊥

is an irreflexive and symmetric

relation on

(

)

(

)( )

TTRRCC ×∪

∪

called exclusion;

∈

is a relation over

()( )

RVCI ×∪×

called

instantiation;

is a relation over

()

VIPI ∪××

called

assignment; (the sets

VTIRC ,,,,

are pairwise

disjoint).

Hereby, ontology matching is denoted as a

process of finding correspondences (or mappings)

between the elements of

VTIRC ,,,,

. Mapping (or

mapping rule (Euzenat and Shvaiko, 2007)) is a

tuple

neem ,,, ℜ

′

where:

′

are the elements

TIRC ,,,

of respective ontologies

and

′

;

{

}

⊇

⊃

≡

⊆⊂

ℜ

,,,,

is a set of relations; and

a confidence value (typically in the range of

[]

1,0

According to the specification of OWL

(www.w3.org/standards/techs/owl#w3c_all), that is

a de facto standard ontology representation language

and

′

are represented in OWL as classes,

datatypes, object properties, data properties,

annotation properties or named individuals.

A good survey of ontology-based applications is

(Gargantilla and Gomez-Perez, 2004). The

applications of agent-based ontology alignment are

surveyed by Euzenat and Shvaiko (2007), ontology

matching applications in particular. A

comprehensive summary of ontology matching

techniques and applications is (Scharffe et al., 2007).

Based on these we figure out the following several

typical applications, specifically those using agent

orientation, and analyse to which extent they require

ontology alignment.

Information retrieval and knowledge sharing:

A number of proposals address Information retrieval

(IR) supported by intelligent software agents – for

example (Zuo, 2006), (Mohammadian and Jentzsch,

2004), (Finin et al., 2005).

A well founded framework based on a multi-

agent system for querying heterogeneous data

sources integrated using ontologies was developed

in the SEWASIE project (Dongilli, Fillottrani,

Franconi and Tessaris, 2005). In IR intelligent

software agents are used for extracting information

or knowledge satisfying the semantics and the

context of a user query. Alignments are needed for

correlating query structure and semantics with

ICAART 2012 - International Conference on Agents and Artificial Intelligence

356

information resource schemas and metadata. The

critical characteristic here is high recall as it is

important not to miss any potentially relevant

information while non-relevant can be sifted out in

subsequent steps.

Data/ information integration: Ontology

alignments are used in this application for

integrating data stored in separate, partial and

heterogeneous sources into a single asset. Integration

of the schemas of different databases can also be

regarded in this category. A multi-agent architecture

for data integration based on ontologies is presented

in (Medcraft, Schiel and Baptista, 2003). A method

for specifying schema mappings and agents actions

in XML data integration task is described in

(Brzykcy et al., 2008). A detailed problem statement

and solution is elaborated in (Nagy and Vargas-

Vera, 2010). For this kind of applications the degree

of automation is very important as data/information

integration is a laborious task. Besides the effort to

be spent, the involvement of a human in the loop

may be the cause of errors.

Dynamic data/information fusion:

Agent-

based solutions are used in fusion scenarios for

dynamic creation of semantically adequate samples

from heterogeneous sources. (Sobh, 2009) proposes

a multi-agent model for carrying out information

fusion from multiple sensors in dynamic

environment. An in-depth analysis of the

information fusion problem together with respective

solutions and implementations in the field of

geographical information processing are presented in

(Duckham and Worboys, 2007). As argued by these

authors, the critical parameters for those solutions

are speed and level of automation as information

fusion is usually carried out in real-time (e.g. signal

processing from multiple sensors).

Human-machine dialogues: Alignments are

used in human-agent interaction to provide mutual

understanding between a user and an agent. They

can be used for intelligent human-machine

dialoguing in order to obtain a formalizable set of

requirements, structures, queries, etc. from informal

or poorly structured user descriptions. As a rule such

dialogs are run in iterative way. (Brasoveanu et al.,

2010) argue the importance of using generic

multimodal ontologies on the Semantic Web and

propose an approach to enhance human-agent

interaction based on multimodal ontologies.

(Guzzoni et al., 2007) propose a toolkit-based

approach for modeling human-agent interaction.

Their toolset provides a means to model different

aspects of an intelligent assistant such as: ontology-

based knowledge structures; service-based primitive

actions; composite processes and procedures; natural

language and dialog structures. (Tijerino et al.,

2004) report a framework for human-agent

collaboration for the purpose of problem solving on

the Semantic Web. In human-machine dialogue

scenarios the most critical features are adaptability,

integrativity, and scalability that allow enhancing

human-machine mutual understanding.

Ontology evolution, versioning, refinement,

instance migration: Ontology evolution and

refinement are the necessary conditions for

adequately representing knowledge for dynamically

changing domains. Agents use ontology matching to

determine the knowledge that remains adequate to

the changes for reuse in a new ontology version or in

a new ontology. A typical subtask is instance

migration in order to populate the obtained target

ontology ABox based on the analysis of the

structural differences between the sorce and the

target TBoxes (Davidovsky, Ermolayev and Tolok,

2010). Packer, Gibbins and Jennings (2009) present

an approach for ontology evolution and knowledge

acquisition based on agent collaboration. The

approach enables agents augmenting their ontologies

by selecting and sharing the fragments of ontologies

that correspond to a particular domain of interest, or

even narrower – to a particular concept. The

highlights of the approach are complexity and

concept acquisition cost reduction by sharing only

those concepts and relationships that relate to a

particular case. The agent-based approach for

ontology evolution is presented in (Li and Yang,

2008) where the process of ontology refinement is

driven by negotiation rounds among agents. The

approach is applied to supply chain case study.

Similarly to data/information integration task, the

importance of a high level of automation and

decreasing human effort is emphasised for this kind

of applications.

Web service composition: Agents draw up

compositions of services conforming the

requirements and privileges of a user or an agent.

Agents use alignments between ontologies

describing service interfaces (or profiles) in order to

compose web services by connecting their

interfaces. The aspects of ontology reconciliation

with respect to Web services and their composition

are elaborated in (Li and Yang, 2008), (Paurobally,

Tamma and Wooldridge, 2007), (Huang, Zavala,

Mendoza and Huhns, 2005). An important

requirement for such systems is the capability of

adaptation and integration for providing compliant

access and making the use of aggregate and atomic

services more convenient.

A SURVEY ON AGENT-BASED ONTOLOGY ALIGNMENT

357

Table 1: Requirements for ontology alignment in typical applications in open and distributed settings. (Legend: c – crucial,

n – necessary, a – advisable, u – uncritical).

Characteristics

Applications

speed

level of

automation

resource

consumption

adaptability

integrativity

scalability

precision

recall

Information retrieval and knowledge sharing n c a u n a a c

Data/information integration u c u n n a c c

Dynamic data/information fusion n c a n n n a n

Ontology evolution, refinement, instance migration u c u a a a c c

Human-machine dialogues n a n c c c a a

Web service composition n c u c c a c c

The requirements for the described typical

software applications that require ontology

alignment are summarized in table 1. The

characteristics along which the requirements are

assessed are implementation independent. In the

next section we focus on the solutions of the

ontology alignment problem that use agent

orientation. The plethora of non-agent oriented

approaches have been surveyed by other authors –

for example (Chuttur, 2011), (Vázquez-Naya et al.,

2009), (Zhdanova et al., 2004), (Euzenat and

Shvaiko, 2007), (Euzenat et al., 2004b).

3 AGENT-BASED SOLUTIONS

FOR ONTOLOGY ALIGNMENT

Many influential publications, for example (Berners

Lee et al., 2001), envision that intelligent software

components, like agents, need to be used together

with ontologies for making semantic technologies

accepted and effective in open and decentralized

scenarios. For such agent based solutions,

comprising industrial applications, the heterogeneity

problem is the challenge that has to be faced.

Ontology alignments are a means to solve the

challenge. The problem has received substantial

attention in the literature. In this section the

frameworks aiming at solving ontology alignment

problem are surveyed. Attention is paid to the basic

theoretical formalism and the fitness of the solution

for the applications summarized in table 1.

(Schorlemmer et al., 2007) presents a formal

foundation for ontology alignment regarding it as a

product of meaning negotiation between intelligent

software agents. The focus is the introduction of

general alignment interaction models. The approach

is grounded on Barwise and Seligman’s theory of

information (Barwise and Seligman, 1997) and uses

their notion of information flow as a basic

formalism. Alignment is defined as a system of

classifications and infomorphisms and obtained by

meaning coordination between agents

Ag and

Ag through the information channel

ACA

⎯⎯←⎯→⎯ , where C is the classification

determined by the meaning coordination done

before;

A ,

A – respective classifications;

f ,

– respective infomorphisms. The IF-based approach

has been implemented as the IF-Map method for

automated ontology mapping (Kalfoglou and

Schorlemmer, 2002).

Atencia and Schorlemmer (2008) argue that

semantic alignment in most cases is strongly relative

to a particular interaction between agents and even

more strongly depends on a particular state of this

interaction. Hence, the context of an interaction

should be taken into account. This observation is

also concurred by a number of approaches

addressing the problem (e.g. (Besana and Robertson,

2006)). Information flow theory is sufficiently

general and abstract to be applied in almost all

application scenarios.

A variety of alternative approaches are grounded

on Argumentation Frameworks (AF) introduced in

(Dung, 1995) and widely adopted in Artificial

Intelligence in broad (Rahwan and Simari, 2009)

and in Multi-Agent Systems (MAS) applications

(Maudet, Parsons, and Rahwan, 2007).

Argumentation is used for ontology alignment by

agents to determine acceptable mappings in

negotiations. Argumentation-based solutions for

negotiations between agents using different

ontologies are considered in (Euzenat et al., 2006)

with a focus on presenting an argumentation

framework for arguing about ontology alignments.

Dung defined argumentation framework as a pair

attacksARAF ,=

where

is a set of

ICAART 2012 - International Conference on Agents and Artificial Intelligence

358

arguments,

attacks is a binary relation on

and

()

BAattacks , signifies that argument

attacks

argument

. (Bench-Capon, 2003) extends it to the

Value-based AF (VAF) as a 5-tuple

PvalVattacksARVAF ,,,,=

where

V is a non-

empty set of values,

val

is a function which maps

the elements of

to the elements of V and

the set of possible audiences. An audience represents

one ordered set of values that states if an attack

succeeds or fails according to the values the

arguments promote. The use of VAF allows

prescribing assigning different strengths to

arguments depending on the values and accounting

for different interests and preferences over

arguments with respect to a particular agents

audience. VAF is used as a common ground for the

approaches described in (Laera, Tamma, Euzenat,

Bench-Capon and Payne, 2006), (Trojahn, Moraes,

Quaresma and Vieira, 2008), and (Isaac et al., 2008).

In (Trojahn et al., 2008) VAF is used for alignment

compositions and is complemented with a set of

confidence degrees and a mapping of those degrees

to arguments representing the confidence of an agent

in some argument. (Isaac et al., 2008) present a

Voting-based VAF (V-VAF) and a Strength VAF

(S-VAF). S-VAF extends the VAF with a function

that maps elements of

to real values within

[

]

1,0

representing the strength of the argument. V-VAF is

defined by adding a notion of support (a reflexive

binary relation over

disjoint to attacks) which

allows counting arguments as defenders (or co-

attackers) within a particular attack. Taking into

consideration the obtained counts, voting allows

determining whether an attack is successful or not.

An interesting comparison of AFs is presented in

(Trojahn et al., 2009) which analyzes VAF, S-VAF

and V-VAF and evaluates them using ontologies

from the Ontology Alignment Evaluation Initiative

(OAEI, oaei.ontologymatching.org) evaluation data

set. (Maio and Silva, 2010) propose an approach

based on the Bipolar AF (BAF) by (Cayrol and

Lagasquie-Schiex, 2005). Similarly to V-VAF, BAF

extends the AF with the capability of representing

the support relation between arguments. In BAF the

support relation allows indicating the arguments

which are assumed to be independent of the attack

relation. (Trojahn and Euzenat, 2010) presents both

theoretical and empirical study of ontology

consistency state depending on argumentation-

grounded alignments. Comparing to approaches

based on IF-theory, AF-based ones better fit to those

application scenarios where the agent paradigm is

commonly used – in information retrieval, human-

machine dialogues and web-service composition.

However, it is a promising approach also for such

tasks as ontology evolution, refinement and instance

migration especially in decentralised settings where

manipulating of distributed heterogeneous

ontologies is a necessary subtask.

(Ermolayev et al., 2005) elaborate a strategy for

automated meaning negotiation. Similarly to

(Atencia and Schorlemmer, 2008) their approach

aims at aligning ontologies by parts (contexts) that

are relevant to a particular negotiation encounter.

Negotiations imply iterative reduction of semantic

distance between the contexts. An agent uses

propositional substitutions which may reduce the

distance and support them with argumentation. The

process is stopped when the distance reaches a

commonly accepted threshold or the involved parties

exhaust their propositions and arguments. As

opposed to the above-mentioned AF-based

approaches this framework addresses the entire

process of semantic reconciliation between

ontologies and does not require off-the-shelf

mappings. The approach is oriented to a specific task

of meaning coordination between a query submitter

and a mediator agent in distributed information

retrieval. However, essentially it does not use any

application dependent features and could therefore

be used in other applications of ontology alignment.

In a summary it has to be noted that the majority

of agent-based solutions use negotiation techniques

as the most natural and well-proven mechanism for

agent interaction. Several basic theoretical

approaches with different expressive power are

exploited. However the most widely used formalism

is the Dung’s Argumentation Framework (or its

derivatives).

4 CONCLUSIONS

The paper presented a brief survey of the approaches

to agent-based ontology alignment. The analysis of

these approaches is grounded on the analysis of the

requirements to ontology alignments by typical

applications that address semantic heterogeneity in

open and decentralized settings.

All the solutions that have been surveyed are

rather abstract ones that can be used for different

applications than they are application dependent.

The majority of the reviewed frameworks still

wait for their implementation and experimental

validation. Trojahn (2009) and Isaac (2008) report

their experimental setups and evaluation results.

Kalfoglou (2002) also reports implementation and

A SURVEY ON AGENT-BASED ONTOLOGY ALIGNMENT

359

application case study. With respect to the rest of

the frameworks it is hard to assess and compare the

fitness of their approaches to the range of typical

applications of ontology alignment.

ACKNOWLEDGEMENTS

The research presented in this paper has been

supported in part by DataArt. The company has

provided the travel grant for attending and

presenting at ICAART 2012.

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