How Ontologies can Help in an e-Marketplace Dickson K. W. CHIU Senior Member, IEEE Dickson Computer Systems Hong Kong Poon, Joe Kit Man Lam, Wai Chun Tse, Chi Yung Sui, William Hi Tai Poon, Wing Sze Department of Computer Science, University of Hong Kong

Ontology for e- MarketplaceECIS Introduction FunctionTraditional e-marketplace problemContributions of Ontology Match- making Match-making is often ineffective because of the rigid definition of products of limited attributes. Shared and agreed ontology provides common, flexible, and extensible definitions of products and requirements for match- making and subsequent business processes It is difficult to specify complex product requirements because the relationships among attributes and values are ignored. Complicated requirements can be decomposed into simple concepts for streamlining the elicitation of options User interactions are limited to mainly manually, which is time consuming. Accessible by automated agents through Semantic Web specifications for more business opportunities Recom- mendation Recommendations are often only possible within the same category. Ontology helps elicit alternatives for recommendation. Pre-set formulae for every type of product are needed for evaluation. Ontology help recommendation by evaluating offers in terms of flexible overall scaling Cross-sale and grouping of buyers and sellers with similar requests are difficult. Matching grouping of buyers and sellers as well as cross-sale possible by inference with the ontology. Negotiation No implicit ordering of alternatives.Implicit ordering of alternatives is elicited via inheritance. Manual negotiation or inadequate negotiation support cause inefficient process and ineffective recognition. Machine understandable semantics facilitate negotiation and automatic configuration of products and services as specified.

Ontology for e- MarketplaceECIS Background of Research D.K.W. Chiu, S.C. Cheung, P.C.K. Hung, and H.F. Leung. Semantic Web Technologies for e-Negotiation. HICSS38 D.K.W. Chiu, S.C. Cheung, P.C.K. Hung, S.Y.Y. Chiu* and K.K. Chung*. Developing e-Negotiation Process Support with a Meta-modeling Approach in a Web Services Environment, Decision Support Systems, accepted. (*FYP students) Peliminary Version at ICWS'03, June th Pacific Asia Conference on Information Systems, Sept 2002 D.K.W. Chiu, S.C. Cheung, P.C.K. Hung, and H.F. Leung. Constraint-based Negotiation in a Multi-Agent Information System with Multiple Platform Support, HICSS37, Jan S.C. Cheung, P.C.K. Hung and D.K.W. Chiu. On e-Negotiation of Unmatched Logrolling Views, HICSS36, Jan S.C. Cheung, P.C.K. Hung and D.K.W. Chiu. A Meta-model for e-Contract Template Variable Dependencies Facilitating e-Negotiation, ER2002, Oct 2002

Ontology for e- MarketplaceECIS Motivation Are there currently significant practical use of the Ontology from Semantic Web? Match-making and beyond Software requirement engineering / negotiation Model and solve practical problems with CS and technologies Cross-over multi-disciplinary research

Ontology for e- MarketplaceECIS Objectives How to elicit negotiation requirements? Semantic Web => Ontologies => help negotiators’ mutual understanding of issues, alternatives, and tradeoffs Address semantic requirements of negotiation Reduce cost and improve effectiveness of negotiation (avoid combinatorial explosion of issues) Development of an effective and efficient negotiation plan Applications: e-Marketplace, Web-service negotiation, agent negotiation, requirement negotiation…

Ontology for e- MarketplaceECIS e-Negotiation Portal at e-Marketplaces Buyers Suppliers e-Marketplace Aggregate requests from Buyers, contact potential Suppliers, match Suppliers and Buyers, exchange bids and offers, generate e-Contract Repository Ontologies and Concepts e-Negotiation data Agreements-… bids offers

Ontology for e- MarketplaceECIS Semantic based e-Marketplace Conceptual Model

Ontology for e- MarketplaceECIS Overall e-Negotiation Process Design Methodology Requirements elicitation phase Decision phase

Ontology for e- MarketplaceECIS Requirement Elicitation Methodology 1. Traders select agreed ontology. 2. Traders relate requirements to concepts in the selected ontology. 3. System checks dependencies of concepts that constitute all the requirements from the (refined) ontology map. Mutually dependent clusters of concepts determine the indivisible groups of requirements that have to be considered together so that effective tradeoff can be evaluated. 4. The system checks the consistency of all the concepts, issues, and their dependencies (Cheung et al. 2002). 5. For a consistent plan, the system can proceed to elicit the possible alternatives; otherwise we have to re-iterate from step According to the dependencies, the system can formulate a precedence graph of the requirements and requirements groups. Based on the precedence graph, an efficient decision plan can be determined.

Ontology for e- MarketplaceECIS Decision Phase Methodology The system searches for the matching offers based on the trader’s preference attempt to rank them for the trader to choose Trader may accept any matched offers or change his reservation price and attempt a negotiation with those offers in order to seek for a more favorable one. If no matching offers are found, the system identifies near misses and also attempts to rank them for the trader to choose. Trader change his mind to accept a near miss or choose a near miss for negotiation. During negotiation, the system supports the user to make and evaluate offers / counter-offers based on the decision plan (from previous slide) in a negotiation session as follows (Chiu et al. 2005). Should new requirement issues arise in the decision phase (say, due to incomplete specification), the trader can we can go back to analyze the new issue and its relationships to the existing ones. In real-life, the formulation of a decision plan may involve several iterations. This reflects the traders may not be able to understand all the inter-relationships among the issues in one shot.

Ontology for e- MarketplaceECIS Sample Ontology (Clothing)

Ontology for e- MarketplaceECIS Understanding Requirements from Ontologies Perform graph search algorithm on the semantic map Key requirements are preliminary identified in the first round (e.g., unit price, quantity) For each identified requirement 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 a cost is refined into constituent costs that sum up to it. Incomplete Ontologies Introduce new concepts into the ontology map Relate it with to existing ones

Ontology for e- MarketplaceECIS Understanding Requirements from Ontology (Cont) Perform graph search algorithm on the semantic map For each identified concept c, Examine every un-visited node n adjacent to c in the ontology map. For each such node n, see if the new concept is relevant to the negotiation problem. Repeat until no more related new concepts can be identified. Only after successful deal do we need to consider combining newly identified concepts back to specify a more concise agreement

Ontology for e- MarketplaceECIS Understanding Dependencies of Requirements from Ontologies Functional dependency borrowed from fundamental relational database concepts motivate this research The alternative for an issue is determined by the alternatives(s) of other issue(s). cost of production depends on delivery date and quantity. Computational dependency – more obvious type of functional dependency hardwired computational formula E.g. insurance amount = percentage * cost of goods.

Ontology for e- MarketplaceECIS Understanding Dependencies of Requirement from Ontology Requirement dependency (constraint satisfaction) Only after the determinant value is known can viable alternatives be determined. E.g., whether a customer may pay by credit card, bank draft, or remittance is evaluated according to the total amount. Classification dependency A special type of requirement dependency in which the classification of another issue is dependent on the outcome of an agreed issue.

Ontology for e- MarketplaceECIS Indivisible Requirement Components for Tradeoff Evaluation Indivisible Components of Issues Cyclic dependencies among the concepts Tradeoff Evaluation

Ontology for e- MarketplaceECIS Understanding Possible Requirement Alternatives from Ontology Alternative for requirement are often in discrete values cannot be expressed in numerical values not quantized in normal practices because of difficulties in recognizing them, e.g., color for simplicity and convenience (size => S, M, L, XL) The elicitation of options is streamlined when a complicated issue is decomposed into concepts (appearance => size + color + shapes) Ontology provide explicit ordering of them (size => S < M < L < XL) implicit ordering inheritance (“is-a”) hierarchies composition hierarchies

Ontology for e- MarketplaceECIS Exploring more trading opportunities from Ontology improving the accessibility of automated agents to match functional specification Agents could represent buyers or sellers e-marketplace acts as “broker” considering the shared ontology attributes and constraints mapping of cross-sale grouping buyers or sellers together for higher market efficiencies

Ontology for e- MarketplaceECIS System Implementation Architecture

Ontology for e- MarketplaceECIS OWL Listing Sample Clothing Ontology Small Medi um Large Extra Large … …

Ontology for e- MarketplaceECIS Conclusions  Formulation of negotiation plan with maturing of Semantic Web technologies  Elicitation of negotiation issues, issue dependencies, tradeoff, and alternatives  Control the openness of issues  Our algorithm verifies the completeness of elicited negotiation requirements  Negotiation processes are properly guided, recorded, and managed  For e-commerce activities are usually more structural and repeatable (as opposed to political negotiations)  Ontologies and plans are therefore reusable  Negotiation automation with agents / integration with EIS

Ontology for e- MarketplaceECIS Future Work  Formal models  Elicitation of semantic distances  enhancement of ontology-based matchmaking and recommendation algorithms  ontology-based cross-sale and up-sale  grouping of buyers and sellers for combined quantity deals  mobile clients and constraint-based requirement specification