Metadata for Web-based Information Management

1. Metadata  for Web-based Information Management

2. Towards a Semantic Web • WWW is an impressive success: • amount of available information (> 1 Giga-page) • number of human users (> 200 Mega-user) • The current Web represents information using • natural language (English, Hungarian, Chinese,…) • graphics, multimedia, page layout • Humans can process this easily • can deduce facts from partial information • can create mental associations • are used to various sensory information • (well, sort of… people with disabilities may have serious problems on the Web with rich media!) Dickson Chiu - update 2009

3. Need for understanding Web info • Tasks often require to combine data on the Web: • hotel and travel infos may come from different sites • searches in different digital libraries • etc. • Again, humans combine these information easily • even if different terminologies are used! Dickson Chiu - update 2009

4. What is the Problem? • Markup comprise • rendering information (e.g., font size and colour) • Hyper-links to related content • Semantic content is accessible to humans but not (easily) to computers… Consider a typical web page: Dickson Chiu - update 2009

5. What information can we see… WWW2002 The eleventh international world wide web conference Sheraton waikiki hotel Honolulu, hawaii, USA 7-11 may 2002 1 location 5 days learn interact Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed Tim berners-lee Tim is the well known inventor of the Web, … Ian Foster Ian is the pioneer of the Grid, the next generation internet … Dickson Chiu - update 2009

6. Information a machine may see… WWW2002 The eleventh international world wide web conference Sheraton waikiki hotel Honolulu, hawaii, USA 7-11 may 2002 1 location 5 days learn interact Registered participants coming from australia, canada, chile denmark, france, germany, ghana, hong kong, india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire Register now On the 7th May Honolulu will provide the backdrop of the eleventh international world wide web conference. This prestigious event … Speakers confirmed Tim berners-lee Tim is the well known inventor of the Web, … Ian Foster Ian is the pioneer of the Grid, the next generation internet … Dickson Chiu - update 2009

7. Solution: XML markup with “meaningful” tags? <name>WWW2002 The eleventh international world wide webcon</name> <location>Sheraton waikiki hotel Honolulu, hawaii, USA</location>… How about… <conf>WWW2002 The eleventh international world wide webcon</conf> <place>Sheraton waikiki hotel Honolulu, hawaii, USA</place> Then how about… <会议>WWW2002 The eleventh international world wide webcon</会议> <地点>Sheraton waikiki hotel Honolulu, hawaii, USA</地点> Dickson Chiu - update 2009

8. What Is Needed? • A resource should provide information about itself • also called “metadata” (data about data) • Metadata capture part of the meaning of data • metadata should be in a machine processable format • agents should be able to “reason” about (meta)data • metadata vocabularies should be defined Dickson Chiu - update 2009

9. What Is Needed (Technically)? • To make metadata machine processable, we need: • unambiguous names for resources (URIs) • a common data model for expressing metadata (RDF) • and ways to access the metadata on the Web • common vocabularies (Ontologies) • The “Semantic Web” is a metadata based infrastructure for reasoning on the Web • It extends the current Web (and does not replace it) Dickson Chiu - update 2009

10. Ontology: Origins and History • Ontology in Philosophy -a philosophical discipline—a branch of philosophy that deals with the nature and the organization of reality • Science of Being (Aristotle, Metaphysics, IV, 1) • studies being or existence as well as the basic categories thereof • trying to find out what entities and what types of entities exist • has strong implications for the conceptions of reality. Dickson Chiu - update 2009

11. Ontology in Computer Science • An ontology is an engineering artifact [Neches91]: • defines basic terms and relations comprising the vocabulary of a topic area • the rules for combining terms and relations to define extensions to the vocabulary • “An explicit specification of a conceptualization” [Gruber93] • Formal specification of a shared conceptualization (of a certain domain) [Borst 97]: • Shared understanding of a domain of interest • Formal and machine manipulable model of a domain of interest Dickson Chiu - update 2009

12. Ontology Elements • Concepts(classes) + their hierarchy • Concept properties (slots / attributes) • Property restrictions (type, cardinality, domain, etc.) • Relations between concepts (disjoint, equality, etc.) • Instances • E-R diagram / UML diagram ??? • Note: “Property”  “Slot”  “Relation”  “Relationtype”  “Attribute”  Semantic link type” Dickson Chiu - update 2009

13. Ontology Languages RDF Schema • RDF is a data model for objects and relations between them • RDF Schema is a vocabulary description language • Describes properties and classes of RDF resources • Provides semantics for generalization hierarchies of properties and classes Dickson Chiu - update 2009

14. Web Ontology Languages (2) OWL • A richer ontology language • relations between classes • e.g., disjointness • cardinality • e.g. “exactly one” • richer typing of properties • characteristics of properties (e.g., symmetry) • Logic • BOTH are standards of www.w3.org Dickson Chiu - update 2009

15. History of the Semantic Web • Web was “invented” by Tim Berners-Lee (amongst others), a physicist working at CERN • TBL’s original vision of the Web was much more ambitious than the reality of the existing (syntactic) Web: • TBL (and others) have since been working towards realising this vision, which has become known as the Semantic Web • E.g., article in May 2001 issue of Scientific American… “... a goal of the Web was that, if the interaction between person and hypertext could be so intuitive that the machine-readable information space gave an accurate representation of the state of people's thoughts, interactions, and work patterns, then machine analysis could become a very powerful management tool, seeing patterns in our work and facilitating our working together through the typical problems which beset the management of large organizations.” Dickson Chiu - update 2009

16. Adding “Semantics” • External agreement on meaning of annotations • E.g., Dublin Core (http://dublincore.org/) • Agree on the meaning of a set of annotation tags • Problems with this approach • Inflexible • Limited number of things can be expressed • Use Ontologies to specify meaning of annotations • Ontologies provide a vocabulary of terms • New terms can be formed by combining existing ones • Meaning (semantics) of such terms is formally specified • Can also specify relationships between terms in multiple ontologies Dickson Chiu - update 2009

17. Berner-Lee’s Architecture ??? ??? ???  Semantics+reasoning ?  Relational Data ?  Data Exchange • Relationship between layers is not clear • OWL DL extends “DL subset” of RDF Dickson Chiu - update 2009

18. The Role of Ontologies on the Web • Ontologies provide a shared understanding of a domain: semantic interoperability • overcome differences in terminology • mappings between ontologies • Ontologies are useful for the organization and navigation of Web sites • Ontologies are useful for improving the accuracy of Web searches • search engines can look for pages that refer to a precise concept in an ontology • Web searches can exploit generalization/specialization information • If a query fails to find any relevant documents, the search engine may suggest to the user a more general query. • If too many answers are retrieved, the search engine may suggest to the user some specializations. • General e-business automation based on understanding web resource in order to facilitate intelligent (software agent) processing Dickson Chiu - update 2009

19. Case study: Use of Ontology in an e-Marketplace D.K.W. Chiu, J.K.M. Poon, W.C. Lam, C.Y. Tse, W.H.T. Siu, W.S. Poon. How Ontologies Can Help in an E-marketplace, European Conference on Information Systems 2005 (ECIS 2005), May 2005 • Semantic Web vision is probably too ambitious • A more realistic current application that has a potential to become a killer application Dickson Chiu - update 2009

20. Motivation • Compare some general-purposed e-Marketplaces (auction based) • e-Bay (HK): www.ebay.com.hk • Yahoo Auction (HK): auctions.yahoo.com.hk • Taobao owned by Alibaba.com: http://www.taobao.com (See also Alibaba.com: http://china.alibaba.com/) • Compare special-purposed e-Marketplaces • Airtickets: http://www.qunar.com/ • Finding friends (!): http://hk.personals.yahoo.com/ Which one is better? Why? Key issue => capturing and applying domain knowledge Dickson Chiu - update 2009

21. What is an e-Marketplace? Suppliers e - Marketplace offers Aggregate requests Repository from Buyers, contact bids potential Suppliers, Ontologies and Concepts match Suppliers e - Negotiation data and Buyers, exchange offers Agreements - … bids and offers, generate e - Contract bids Buyers Dickson Chiu - update 2009

22. Problem Statements • 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 & ICT • Cross-over multi-disciplinary research IJSSOE: Dickson Chiu, Editor-in-chief http://www.igi-global.com/journals/details.asp?id=34268 Dickson Chiu - update 2009

23. Example Ontology Clothing and Sales Negotiation {unordered} attributes: Payment Terms Refunding Policy Discount deposit, installment, pay-upon-delivery, ... Delivery Total Amount Sale Order * * Shipping Payee Clothing Quantity Cost {ordered} attributes: Insurance Appearance Unit Cost Delivery Date small, medium, large, extra-large Insurer Insured Amount Premium Size Color {unordered} {unordered} Red Purple attributes: brick attributes: light red, crimson, ... purple, magenta, ... Dickson Chiu - update 2009

24. Objective and Solution Approach • 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… Dickson Chiu - update 2009

25. Semantic basede-Marketplace Conceptual Model Dickson Chiu - update 2009

26. Overall e-Negotiation Process Design Methodology Requirements elicitation phase Decision phase Dickson Chiu - update 2009

27. Requirement Elicitation Methodology • Traders select agreed ontology. • Traders relate requirements to concepts in the selected ontology. • 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. • The system checks the consistency of all the concepts, issues, and their dependencies (Cheung et al. 2002). • For a consistent plan, the system can proceed to elicit the possible alternatives; otherwise we have to re-iterate from step 3. • 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. Dickson Chiu - update 2009

28. 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. Dickson Chiu - update 2009

29. 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 Dickson Chiu - update 2009

30. 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 working concepts back to more concise real-life objects in specifying a agreement • E.g., component costs need not shown to business partner Dickson Chiu - update 2009

31. 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). • E.g., delivery date and quantity -> cost of production • Computational dependency • more obvious type of functional dependency • hardwired computational formula • E.g., insurance amount = percentage * cost of goods. Dickson Chiu - update 2009

32. 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. • E.g., customer tiering Dickson Chiu - update 2009

33. Indivisible Requirement Components for Tradeoff Evaluation • Indivisible Components of Issues • Cyclic dependencies among the concepts • Tradeoff Evaluation • Topological sort of semantic graph gives negotiation plan Dickson Chiu - update 2009

34. Understanding Possible Requirement Alternatives from Ontology • Alternative for requirements 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 Dickson Chiu - update 2009

35. Exploring more trading opportunities from Ontology • Improve the accessibility of automated agents to match functional specification • Intelligent software agents could represent buyers or sellers • e-marketplace acts as “broker” • Consider shared ontology attributes and constraints • Map for cross-sale • Group buyers or sellers together for higher market efficiencies • Better hints for data mining Dickson Chiu - update 2009

36. System Implementation Architecture Multiplatform Support Subsystem WAP SMS Gateway Gateway Multiplatform e-Negotiating Matching Subsystem Devices Internet Web Server e-Negotiation Messenger Process Generator task dependency issue Issue Dependency Task dependency Editor Organizer e-Negotiation process bids & offers ontology Issue e-Negotiation Executing Subsystem Ontology Maintenance Subsystem e-Negotiation Session Manager Ontology Editor Ontology ontology Generator revised ontology, issues Criteria Search Criteria & Issue ontology e-Negotiation Engine Issues Editor Data & existing Repository ontology e-Negotiation process Dickson Chiu - update 2009

37. <owl:Ontology rdf:about="#Clothing"> <rdfs:comment>Sample Clothing Ontology</rdfs:comment> <owl:Class rdf:ID="Clothing" /> <owl:Class rdf:ID="Appearance" /> <owl:Class rdf:ID="Color"> <rdfs:subClassOf rdf:resource="#Appearance" /> ... </owl:Class> <owl:ObjectProperty rdf:ID="hasAppearance"> <rdfs:domain rdf:resource="#Clothing" /> <rdfs:range rdf:resource="#Appearance" /> </owl:ObjectProperty> <owl:ObjectProperty rdf:ID="hasColor"> <rdfs:subPropertyOf rdf:resource="hasClothAppearance" /> <rdfs:range rdf:resource="#Color” /> ... </owl:ObjectProperty> <owl:DatatypeProperty rdf:ID="size"> <!-- Enumeration --!> <rdfs:domain rdf:resource="#Appearance"/> <rdfs:range> <owl:DataRange> <owl:oneOf> <rdf:List> <rdf:rest> <rdf:List> <rdf:rest><rdf:List> <rdf:rest><rdf:List> <rdf:rest rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-ns#nil"/> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Small</rdf:first></rdf:List></rdf:rest> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Medium</rdf:first></rdf:List></rdf:rest> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Large</rdf:first></rdf:List></rdf:rest> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Extra Large</rdf:first></rdf:List> </owl:oneOf></owl:DataRange></rdfs:range> </owl:DatatypeProperty> <owl:Class rdf:ID=" UnitCost"> … <owl:equivalentClass> <!-- unit cost depends on appearance --> <owl:Restriction> <owl:someValuesFrom rdf:resource="#Appearance" /> </owl:Restriction> </owl:equivalentClass> </owl:Class>… </owl:Ontology> OWL Listing Dickson Chiu - update 2009

38. Summary Dickson Chiu - update 2009

39. 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 Dickson Chiu - update 2009

40. 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 Dickson Chiu - update 2009

41. Question and Answer Thank you! Email: dicksonchiu@ieee.org Dickson Chiu - update 2009