Logic Based Methodology

Jimenez-Ruiz et al. [79] present a methodology9 _{which provides Ontology Engineers}

with a precise set of guidelines to follow in order to ensure that certain logical properties such as safety, the meaning of the extracted terms remains the same, and coverage, everything the ontology says about the extracted terms is in the module; see Section 3.4.2) for the definitions.

The methodology is split into two phases: offline and online, see Figure 5.2. The two phases are detailed below:

Offline Phase The Ontology Engineer has the ontology, T, that is being developed. The next step is for the Ontology Engineer to specify the signature,S, that is to be reused from the external ontologies; each element of the signature is associated to the external ontology it was taken from. Thus, S=S1]. . .]Si; where each Si ⊆S represents the symbols from the external ontologyT_i0.

In the next step the Ontology Engineer needs to decide how each Si will be reused. Will it be generalised (>-locality) or specialised (⊥-locality)10? This step is required to ensure safety.

Online Phase In this step the Ontology Engineer imports the locality based module from each of the external ontologies identified in the previous step. Once the external ontology is loaded, implying that the Ontology Engineer is committing

9

This methodology is implemented as a Prot´eg´e plugin

10

Figure 5.2: Logic based methodology. Taken from [79]

to this particular version, there is an opportunity forSi to be extended. Then the actual module,T_Si, is extracted from the external ontology; which is guaranteed to cover the elements specified bySi.

The final step is to import the module previously extracted into the ontology; thus we now have a new ontologyT ∪ T_Si. It is possible that this inclusion com- promises the safety guarantee of the other external ontologies. This is considered undesirable so amodule independenceguarantee is required; this states that given an ontologyT and two signaturesS1,S2thenT guarantees module independence

Chapter 6

Applying Ontology

Modularization to the Dynamic

Selection of Ontology Alignments

in Multi-Agent Systems

‘Arguments are to be avoided; they are always vulgar and often convincing.’ - Oscar Wilde

Summary This chapter considers the practical, and novel, application of ontol- ogy modularization to the problem of the dynamic selection of ontology alignments in multi-agent systems where modularization is used as as space reduction mechanism. First some preliminary information is given concerning agents, multi-agent systems and how ontology alignments can overcome the problem of semantic heterogeneity. Next the definition of the argumentation framework is given, including the value-based ar- gumentation framework that is used in the rest of the chapter. From this it is possible to show how argumentation can be used to argue over ontology alignments and, thus, how modularization can be used as a space reduction mechanism to this process. It is possible that there is information loss when applying modularization to this problem. This is discussed and two solutions are proposed. Finally, an evaluation is presented that shows that modularization successfully reduces the space, but does not have a negative impact on the quality of the alignment agreed.

6.1

Motivation

Interacting systems are now the norm in the everyday computing world, even trivial systems contain sub-systems, termed agents, that need to interact [146]. Furthermore, as these systems are likely to be distributed and have decentralised management, sys- tems such as the Internet, then it is challenging to impose global constraints upon the system. This type of environment is called an open environment.

An open environment places no constraints on an agent’s, i.e. a software compo- nent, ability to enter or leave the environment, furthermore no assumptions can be made about the agents that will be encountered now or in the future. We assume that each agent has its own ontology, its own model of the world. Each agent having its own ontology, about which no assumptions can be made, leads to the problem of se- mantic heterogeneity. That is agents have differing specifications or conceptualizations of concepts in their ontology [142].

In order to enable inter-agent communication semantic heterogeneity must be over- come. Effective communication within open and dynamic environments is dependent on the ability ofagents to reach a mutual understanding over a set of messages, where no prior assumptions can be made on the vocabulary used to communicate. Unlike small, closed environments (where all the components are known at design time), open, Web-scale environments are typically characterised by large numbers of services which are continually evolving or appearing, and where semantic heterogeneity is the norm.

Semantic heterogeneity can be divided into two levels: language and ontology [81]. Language level heterogeneity occurs when two ontologies are written in different lan- guages, for example description logic and first-order logic. Ontology level heterogeneity occurs when either there is a mismatch in the conceptualization of the ontology, for example a difference in the extensions of the same concept; or a difference in the way that the conceptualization was specified, for example giving the same name to two different concepts.

Thus, semantic heterogeneity means few assumptions can be made about the ser- vices on offer at any time, the way in which they are modeled, or the terminology or vocabulary that they use. In such cases, it becomes imperative to specify the explicit vocabularies or ontologies used to facilitate meaningful communication as environments open up, or the heterogeneity of large systems increases. This has been facilitated by the emergence of standards for representing ontologies and optimised reasoners capable of processing them within a tractable timeframe [134].

In addition, transactions should be interpreted by both service providers and con- sumers based on the underlying semantics of the messages themselves, and thus these agents should resolve any type of mismatch that may exist due to the use of different, but conceptually overlapping ontologies. However, this reconciliation has to be achieved automatically and at run-time (without human intervention) if such components are to transact as the size of the environment grows.

Early systems avoided the problem of ontological heterogeneity by relying on the existence of a shared ontology, or simply assuming that a canonical alignment, possi- bly defined at design time, could be used to resolve the mismatches. However, such assumptions work only when the environment is (semi-) closed and carefully managed, and no longer hold in open environments where a plethora of ontologies exist. How-

ever, semantic heterogeneity can be overcome by using ontology alignment (see Section 6.2.4). An ontology alignment defines a set of relations between the entities of two ontologies, thus, allowing entities in one ontology to be explicitly related to entities in another ontology. Unfortunately, however, the techniques for ontology alignment generation either take a long time, for example [76], or are user-led, for example [101]. These constraints prevent the agents from dynamically generating the alignments as and when they are needed.

However, we can assume that the ontology alignments exist somewhere in the envi- ronment, for example from an Ontology Alignment Service (OAS) [84], thus, the agents will now be able to acquire the relevant alignment when needed. Relevant alignments are those that align the ontologies of the two agents who wish to cooperate. There is a problem, however, as it is possible that more than one alignment exists between the two agent ontologies, and so the agents now have the problem of deciding which to use. Laeraet al.[85] define a framework, based on argumentation (see Section 6.3). Argumentation allows the agents to argue over the ontology alignments and reach a mu- tually acceptable agreement. They are able to argue for or against a mapping based on their preferences. However, the complexity of this process can reach Πp₂−complete [37] making it impractical.

In this Chapter we propose a new task for ontology modularization (see Section 3.2) that is to reduce the search space that the agents have to argue over in order to reach an agreement over ontology alignments. However, before it is necessary to consider what agents and multi-agent systems are, as well as characterising the environment they operate in. This is detailed in Section 6.2 along with an outline of the problem of semantic heterogeneity and an explanation of how ontology alignments overcome it. Section 6.3 provides an introduction to argumentation and details the value-based argumentation framework. It is then possible in Section 6.4 to show how agents can argue over ontology alignments and how modularization can act as a space reduction mechanism. An illustrative example is given in Section 6.5. The application of modu- larization to argumentation over ontology alignments can lead to information loss and this is outlined in Section 6.6, alongside two possible solutions. Finally, an evaluation of applying ontology modularization to the problem of arguing over ontology alignments is provided in Section 6.7.