Facilitating e-Negotiation Processes with Semantic Web Technologies




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НазваниеFacilitating e-Negotiation Processes with Semantic Web Technologies
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Motivating Example

Sale negotiation activities are the most common in e-commerce and particularly in e-marketplaces. Ontologies help e-commerce activities through mutual understanding and the facilitation of information exchange (Fensel et al. 2001).




Fig. 3. An Ontology for Sale Negotiation of Rubber Gloves in UML Class Diagram
Fig. 3 presents an example ontology for the negotiation over a selection of concepts in a sale order of rubber gloves. Concepts are represented in rectangular boxes. A Sale Order may consist of multiple Order Lines, each of which describes the Quantity to be ordered, Appearance and Unit Cost. Appearance consists of Size and Color. The former may attains a value ranging from small to extra-large while the latter can be further classified into different specific color concepts, such as Red, Purple, and so on. Besides the Order Lines, a Sale Order is characterized by the information about Payment Terms, Discount, Refunding Policy, and the Total Amount of the order. Delivery involves three issues: Shipping Cost, Delivery Date, and the associated Insurance. In addition, directed lines show the dependent relationships among concepts and lines without arrows denote bi-directional relationships.




Fig. 3. An Ontology for Sale Negotiation of Rubber Gloves in UML Class Diagram
Planning is a critical part of negotiation. There is usually one major issue (e.g., price), and several minor issues (e.g., insurer) in any negotiation. In negotiation, there are three types of planning (Lewicki and Litterer 1985): (1) Strategic planning is used to define long-range goals and to position oneself toward long-rang goals, (2) Tactical planning is the process of developing short-range tactics and plans to achieve long-range goals, and (3) Administrative planning is the process by which both manpower and information are marshaled to make the negotiation proceed smoothly. Therefore, we have to establish a negotiation plan, depicting an order to discuss those issues. Traditionally, the functions of the negotiating plan (Marsh 1987) are to define the initial strategy with the supporting arguments. The initial strategy includes the order of issues to be negotiated. In the next section, motivated by this sale order example, we discuss how ontology helps formulate a negotiation plan. Furthermore, we discover different types of relations among negotiable concepts. Based on these relations, we can determine a negotiation plan that facilitates collaborative negotiation processes.

  1. Are Ontologies Helpful?

In this section, we discuss how ontologies help the overall formulation negotiation process. Though the use of ontologies in groupware and collaboration systems is not new, we show how ontologies can be applied in a much wider and important scope in negotiation processes.

1.3.Understanding Negotiation Issues from Ontologies

The difficulties during the exchange communication between users are the inconsistency in the represented value and how to make the data interchange meaningful. Thus, ontologies are becoming increasingly important as a component of online commerce offerings. Ontologies can present machine-understandable semantics of data to facilitate the negotiation about products, or help automatically configure products and services according to specified requirements. In particular, shared and agreed ontologies provide common definitions of the terms to be used in the subsequent negotiation processes.

We propose the following methodology extended from well-known graph search algorithms (Cormen 2001) to enhance the completeness of issues in requirement elicitation:




  1. Fig. 4. A Possible Negotiation Plan for Rubber Gloves Sale in UML Activity Diagram
    Issues are preliminarily identified in the first round.

  2. For each identified issue, check if an issue can be mapped directly to a concept. If not, see if an issue can be refined into a set of more specific concepts, which combined can represent the issue. A typical example is that a cost can be refined into constituent costs that sum up to it.

  3. Ontologies are often incomplete and therefore subject to further refinement. New concepts can be introduced to the ontology upon mutual agreement. However, the relation of a new concept to existing ones should be elicited to help understand the concept itself as well as determine potential dependence of issues for the negotiation.

  4. For each identified concept c, examine every un-visited node n adjacent to c in the ontology map.

  5. For each such node n, see if the new concept is relevant to the negotiation problem.

  6. Repeat step 4 and 5 until no more related new concepts can be identified.

  7. Only after successful negotiation do we need to consider combining newly identified concepts back to specify a more concise agreement, because we advocate negotiation centered on concepts.

1.4.Understanding Dependencies of Issues from Ontologies

As we are mapping issues to concepts in ontologies (as described in the above sub-sections), we also discover their inter-relationships at the same time. Based on concepts in databases and artificial intelligence of computer science, we identify the following typical categories of dependencies among issues.

  • Functional dependency – This is the main type of dependence that motivates this research. The concept is borrowed from fundamental relational database concepts (Elmasri and Navathe 2000). The alternative for an issue is de


    Fig. 4. A Negotiation Plan for Rubber Gloves Sale in UML Activity Diagram
    termined by the alternatives(s) of other issue(s). For example, cost of production depends on delivery date and quantity.

  • Computational dependency - This is a more obvious type of functional dependency, which has a hardwired computational formula. For example, insurance amount = percentage * cost of goods.

  • Requirement dependency (constraint satisfaction) – Only after the determinant value is known can viable alternatives be determined. For example, whether a customer may pay by credit card, bank draft, or remittance is evaluated according to the total amount. Therefore, only after the total amount is determined can the negotiation of payment method take place.

  • Classification dependency – This is a special type of requirement dependency in which the classification of another issue is dependent on the outcome of an agreed issue.

1.5.Indivisible Components of Issues for Tradeoff Evaluation and Negotiation Plan

Some concepts (and therefore issues) have to be negotiated together at the same time. It occurs when there are cyclic dependencies among the concepts. Such group of concepts is mutually dependent and therefore must be consider altogether for tradeoff as they cannot be individually or sequentially considered during negotiation. After eliciting the dependencies, we can therefore draw a precedence graph (Cormen 2001) of the issues and issue groups for formulating a negotiation plan.


Note that in the task “formulate plan”, we construct a detailed process to realize the activity “make offers and counter offers.” Fig. 4 gives a possible process for a scenario negotiating the sale of rubber gloves. The negotiation starts with the issues Size, Color, and Refunding Policy concurrently. Once the Size and Color are decided, the issues of Unit Cost, Quantity, and Delivery Date are then negotiated. The process succeeds with the computation of the Total Amount of the order.

1.6.Understanding Possible Alternatives for Issues from Ontologies

Often, alternative for issues cannot be expressed in numerical values. Alternatives are often in discrete values


Fig. 5. System Implementation Architecture

by its nature, such as country of origin, shipping company, and so on. Alternatives for other issues are often not quantized in normal practices because of difficulties in recognizing them. For example, color is specified by its common name or more professionally in a color code, but rarely expressed in the wavelength of constituent light-waves. In many other occasions, alternatives are not quantized for simplicity and convenience. For example, alternatives for size may be just small, medium, or large because either the issue is not important in the context or a precise value is not required.

When a complicated issue is decomposed into concepts, the elicitation of options can be much streamlined. For example, when the issue of appearance is decomposed into the concepts of size, color, and shapes, the alternatives of each concept can then be easily elicited.

Ontologies not only can provide sets of alternatives for issues from membership relations, but often also partial or even total explicit ordering of them (e.g., small < medium < large < extra-large). In addition, implicit (partial) ordering may be elicited via inheritance (“is-a”) or composition hierarchies. Thus, such extra knowledge provided by ontologies can further assist negotiators to evaluate offers against their preferences and determine which counter-offers are decreasing the indifferences rather than increasing them.


  1. System Architecture and Implementation

Fig. 5 Error: Reference source not foundshows the implementation architecture for our template driven e-Negotiation support system based on ontologies. The architecture is designed to support e-Negotiation processes instantiated from the e-Negotiation conceptual model in Fig. 1 Fig. 1and the methodology in Fig. 2. The design aims to provide flexible and reusable components. The proposed system is acting like


Fig. 5. System Implementation Architecture




Fig. 6. UML Activity Diagram for Making Offers and Counter-offers in a Negotiation Session

a negotiation embellisher who knows the values, beliefs, and constraints of both parties. Then, it seeks an efficient contract that both parties would prefer to the negotiation context they have created (Raiffa 1982).

The architecture is made up of four subsystems. The Ontology Maintenance Subsystem allows negotiation parties to specify and edit their negotiation issues and alternatives based on ontologies. The search engine selects the most appropriate ontologies based on a given set of criteria and issues. The retrieved ontology may be further revised using the Ontology editor to address all major required issues and alternatives. Revised ontologies as well as the issues and their alternatives thus derived may be stored in the repository for later retrieval. These data will be used by the e-Negotiation Matching Subsystem to determine a suitable e-Negotiation process based on the issue dependency supplied. The selected e-Negotiation process is then enacted through the e-Negotiation Executing Subsystem. The Multiplatform Support Subsystem provides front-end supports to multiple platform devices, such as WAP, SMS, and Web browsers.





Fig. 6. UML Activity Diagram for Making Offers and Counter-offers in a Negotiation Session

Fig. 6 depicts our design for maintaining a negotiation session in UML activity diagram. Each negotiation cycle starts with the identification of a set of interrelated issues to be next negotiated, according an agreed negotiation plan (as discussed in the previous sub-section). Each party will then prepare the reservation alternatives (reservation price) of these issues. After that, they may either make an offer to or wait for some offers from counterparties. If a party is not satisfied with the (counter-) offer, another counter-offer or a failure message will be received. A negotiation cycle finishes successfully if an acceptance notification of previous (counter-) offer is received. Finally, the negotiation process succeeds when all issues have been successfully negotiated.

Based on this architecture, we have developed an e-Negotiation support system with contemporary technologies, including Java applets, Java Server Pages, and Enterprise Java Beans. We are extending the system with support for ontologies with the OWL Web Ontology Language (OWL, 2004) (instead of DAML) because W3C has designed OWL as a standard (Web-Ontology Working Group 2004).

OWL has been proposed to provide three increasingly expressive sub-languages for specific communities of implementers and users, namely, OWL Lite, OWL Description Logics (OWL DL), and OWL full. OWL Lite supports the basic need for a classification hierarchy and simple constraints. For example, while it supports cardinality constraints, it only permits cardinality values of 0 or 1. Thus, OWL Lite provides an easier implementation and a quicker migration path for thesauri and other taxonomies. OWL DL supports maximum expressiveness while retaining computational completeness (all conclusions are guaranteed to be computed) and decidability (all computations will finish in finite time). OWL DL includes all OWL language constructs, but they can be used only under certain restrictions (for example, while a class may be a subclass of many classes, a class cannot be an instance of another class). OWL DL is so named due to its correspondence with description logic, a field of logic that forms the formal foundation of OWL. OWL Full supports maximum expressiveness and the syntactic freedom of the RDF, but with no computational guarantees. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary. Thus, ontology developers adopting OWL should consider which sub-language best suits their needs. More specifically, we employ OWL DL because it provides a standard set of elements and attributes with defined semantics, for defining terms and relationships in ontology. In addition, OWL DL contains a set of logic-based primitives that are specifically useful in intelligence informatics.

As such, a flexible NSS for different e-Commerce domains and different negotiation plan can be supported, without modifying the underlying system. The negotiators only need to define suitable ontology and derive an effective negotiation plan. This tremendously reduces the development time and costs, and therefore provides a big competition edge under this fast evolving digital economy.

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