Ontology-based Services to help solving the heterogeneity problem in e-commerce negotiations

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Abstract

In a dynamic environment of e-commerce negotiations where transactions involve interaction among different enterprises, using different representations and terminologies, a common understanding is crucial. This paper combines the use of ontologies and agent technologies to help in solving the heterogeneity problem in e-commerce negotiations. The result is an implementation of the Ontology-based Services, which apply a methodology that assesses lexical and semantic similarity among concepts represented in different ontologies. A solution integrating the Jade platform and OWL format is presented as well as an Ontology Interaction Protocol, which is combined with the FIPA Contract Net Protocol.

Introduction

Online e-commerce marketplaces for buying and selling products are omnipresent and bring several suppliers and buyers together. Each supplier and buyer has its own format, concepts and characteristics to represent products. Even if both supplier and buyer use an ontology, they may use ontologies that differ significantly either syntactically or semantically. One of the problems of using different ontologies is that there are different representations and terminologies and there is not a formal mapping between high-level ontologies.

In B2B transactions, it is a simpler task if the enterprises involved in the transaction have homogeneous representation structures as well as the same discourse domain, thus the use of a common ontology provides a solution for the communication problem. The use of a common ontology guarantees the consistency and the compatibility of the shared information in the system. However, in real-life situations, real problems involve heterogeneity and ontologies often developed by several people continue to evolve over time. Moreover, domain changes or changes in the conceptualisation might cause modifications on the ontology. This will likely cause incompatibilities [1] and makes the negotiation and cooperation process difficult.

Heterogeneity is both a welcome and an unwelcome feature for system designers. On the one hand heterogeneity is welcomed because it is closely related to system efficiency. On the other hand, heterogeneity in data and knowledge systems is considered an unwelcome feature because it proves to be an important obstacle for the interoperation of systems. The lack of standards is an obstacle to the exchange of data between heterogeneous systems [2] and this lack of standardization, which hampers communication and collaboration between agents, is known as the interoperability problem [3].

Heterogeneity in this paper, means agents communicating using different ontologies. Four types of heterogeneity are distinguished by [2]: (i) paradigm heterogeneity, which occurs if two systems express their knowledge using different modeling paradigms; (ii) language heterogeneity, which occurs if two systems express their knowledge in different representation languages; (iii) ontology heterogeneity, which occurs if two systems make different ontological assumptions about their domain knowledge; and (iv) content heterogeneity, which occurs if two systems express different knowledge. Paradigm and language heterogeneity are types of non-semantic heterogeneity and the ontology and content heterogeneity are types of semantic heterogeneity.

In our proposed system each agent has its own private ontology even though it is about the same knowledge domain, but each ontology was developed by different designers and may express knowledge differently.

In the literature, ontologies are classified into different types based on different ideas. Ref. [4] presents two typologies, according to the level of formality and according to the level of granularity. According to the level of formality, three ontology types are specified: (i) informal ontology is the simplest type: it is comprised of a set of concept labels organized in a hierarchy; (ii) terminological ontology consists of a hierarchy of concepts defined by natural language definitions; and (iii) formal ontology further includes axioms and definitions stated in a formal language.

According to the level of granularity, six ontology types are specified: (i) top-level ontology defines very general concepts such as space, time, object, event, etc., which are independent of a particular domain; (ii) general ontology defines a large number of concepts relating to fundamental human knowledge; (iii) domain ontology defines concepts associated with a specific domain; (iv) task ontology defines concepts related to the execution of a particular task or activity; (v) application ontology defines concepts essential for planning a particular application; (vi) meta-ontology or generic or core ontology defines concepts which are common across various domains – these concepts can be further specialized to domain – specific concepts.

In our proposed system, the ontologies are classified in the level of formality as terminological ontologies because they include concepts organized in a hierarchy and the concept definitions are expressed in natural language. According to level of granularity they are classified as domain ontologies, in our case in the specific cars’ assembling domain.

Different tools and techniques for mapping, aligning, integration, merging [5], [6], [7], [8], [9] of ontologies are available but there is no automatic way to do that. It is still a difficult task and for the success of these processes it is necessary to detect ontology mismatches and solve them. Recent research about ontological discrepancies have been done [2], [10], however none of the available tools tackle all the types of discrepancies [11]. Unfortunately, ontologies are not a panacea unless everyone adheres to the same one, and no one has yet constructed an ontology that is comprehensive enough. Moreover, even if one did exist, it probably would not be adhered to, considering the dynamic and eclectic nature of the Web and other information sources [12].

Similarity evaluations among ontologies may be achieved if their concept’s representations share some components. If two ontologies have at least one common component (relations, hierarchy, types, etc.) then they may be compared. Usually characteristics provide the opportunity to capture details about concepts. In our approach we are using relations and types of characteristics as common components in all the ontologies. There is a set of relations and characteristics that have to be known and used by all the ontologies for initial tests. The concepts are also linked by a number of relations. The approach proposed in this paper combines the use of a methodology that assesses lexical and semantic similarity among concepts represented in different ontologies. The lexical measures are used to compare attributes and relations between concepts.

This paper presents the Ontology-based Services implementation for providing useful advices on how to negotiate specific items, leading to appropriate conversations and making agreements possible. If all agents in our multi-agent system share a common ontology, it is possible to follow the JADE’s proposal and to code this common ontology in the source of all registered agents. However, as it is an open multi-agent system where different agents can join and leave the platform, it has to be flexible concerning agents’ ontologies. The solution we propose is to use [13] (Web Ontology Language) in combination with a parser to retrieve information. The implementation of a negotiation round combines the FIPA Contract Net Protocol [14] with an additional protocol called Ontology Interaction Protocol, which includes agents representing enterprises (customer and supplier) interacting with the proposed Ontology-based Services Agent.

The remainder of this paper is structured as follow: Section 2 describes the agent-based approach to Electronic Commerce, presenting the system architecture. Section 3 explains the heterogeneity problem and the proposed ontologies. Section 4 points out JADE’s ontology support as well as the approach of integrating ontologies in OWL format into a JADE platform. Section 5 presents the negotiation according with the combination of FIPA Contract Net Protocol (CNP) and the proposed Ontology Interact Protocol (OIP). Section 6 introduces the proposed methodology for the lexical and semantic similarity. Section 7 briefly presents some related work. Finally, Section 8 gives some conclusions.

Section snippets

Agents in e-commerce

Due to the fast growth of Business-To-Business (B2B) e-commerce, the demand for agents is growing because agents are able to, on behalf of their owners, locate and retrieve information and make reasonable decisions based on the owner’s profile. Agents negotiate with multiple suppliers, monitor multiple auctions, and use intelligent strategies to find the best deal for the users. Agents can also represent companies/organizations in a B2B context. This shows not only how agents can collaborate

Heterogeneity problem in e-commerce

Consider the following simple negotiation example: the CEAg needs to buy a “wheel” (a simple machine consisting of a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle in vehicles) and an enterprise agent offers “wheel” (a handwheel that is used for steering). These two components belong to the same cars’ domain, they are syntactically the same but semantically different, and probably the agents will negotiate under these components. Otherwise, when the CEAg needs

Ontology support in JADE

JADE proposes the Content Reference Model (CRM), which is a classification of all possible elements that occur in the discourse domain. For JADE agents, ontological information is represented in terms of Java objects while Protégé stores an ontology in various formats. If a Protégé project is intended to be translated into such a representation, the CRM has to be considered a priori during the ontology definition stage. It plays an important role since it determines certain constraints. The

Negotiation

The implementation of a negotiation round combines the FIPA Contract Net Interaction Protocol with an additional protocol called “ontology interaction protocol”, as shown in Fig. 10. The former represents the general scenario of agents trading goods or services proposed by FIPA. Alike other interaction protocols, it structures complex tasks as aggregations of simpler ones. The latter implements the message flow necessary for solving the problems of interoperability, including the interaction of

Ontology mapping

Ontology mapping is the process of finding correspondence between the concepts of two ontologies (similarity). If two concepts correspond, they mean the same thing, or closely related things [24].

Our approach aims at finding correspondences between concepts based on the concept names, their characteristics, relations and the description of the concept.

We will use the similarity found out to help in the negotiation, without an ontology merging or an ontology alignment. The ontology alignment

Related work

Several related works [31], [32], [33] in the multi-agent systems area have been developed and different approaches have been implemented trying to solve the heterogeneity problem in agents’ communication. Among them, the most closely related to our approach is [34].

Ref. [33] describes an approach to ontology negotiation that allows Web-based information agents to resolve mismatches in real time without human intervention. The terms exchanged consist primarily of the query content and document

Conclusion

With the aid of JADE, which serves as a communication platform and as API, a multi-agent system was developed. The application describes a car assembling domain, a B2B scenario where customers can buy components of a car from several suppliers. Four types of agents participate in the platform: (i) the Customer Enterprise Agent, which is interested in purchasing a component according to its specific needs; (ii) Supplier Enterprise Agents, willing to sell their products or services, who makes an

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    This is an extension and revision of the paper “Combining Ontologies and Agents to help in Solving the Heterogeneity Problem in E-Commerce Negotiations”, presented at the International Workshop on Data Engineering Issues in E-Commerce (DEEC 2005).

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