Metadata for Web-based Information Management through Ontology

1. Metadata for Web-based Information Managementthrough Ontology

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 2011

3. Where are we now? • Web 1.0: info-centric • Web 2.0: user-centric • Web 3.0: semantic-centric … www.digitalrhetoric.org/course/web1to3.jpg Dickson Chiu - update 2011

4. 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 • Especially too much user provided content on Web 2.0 • etc. • Again, humans combine these information easily • even if different terminologies are used! Dickson Chiu - update 2011

5. 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 2011

6. 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 2011

7. 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 2011

8. 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 2011

9. 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 2011

10. 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 2011

11. 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 2011

12. 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 2011

13. 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 2011

14. 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 2011

15. Some Technologies of Semantic Web • RDF • XML • URI • SPARQL • XDI • XRI • SWRL • XFN • OWL • API • OAUTH • … Dickson Chiu 2011

16. Stamp Example – Google Search • Now, suppose I Google for all red stamps • Not very intelligent… Red stamps Stamps from Cambodia (Khmer Rouge) Stamps from the Red Sea Stamps from the 140th anniversary of the Red Cross Stamps with red dragons Dickson Chiu 2011

17. Stamp Example – Structural Meaning • Not very intelligent, but how can a computer know what I mean? • When we structurally describe that a stamp is a stamp and red is a color. • Describing data in a structured way can best be done in a database. • Different databases can be connected. Dickson Chiu 2011

18. Stamp Example – All about a Stamp In 1980 you could buy this stamp for 1 cent Now it’s worth 3 euros This is a stamp This stamp is from the United Kingdom This stamp is used between 1978 - 1981 The picture on the stamp is a PO Box This stamp is designed by John Bryan Dunmore Dickson Chiu 2011

19. XML • Meaning is about understanding. • To understand we need a language. • A language starts with words. • Things mean something in words. • Online, we describe things with XML. Dickson Chiu 2011

20. XML - Example <?xml version="1.0" encoding="ISO-8859-1"?> <collection name=”My stamp collection"> <stamp> <title>Red dragon</title> <country>China</country> <year>1984</year> </stamp> <stamp> <title>PO Box</title> <country>England</country> <year>1992</year> </stamp> </collection> Dickson Chiu 2011

21. RDF and RDF Schema • Resource Description Framework (RDF) • We can’t understand words alone • RDF is a data model for objects and relations between them • RDF Schema is a vocabulary description language • In addition, online grammar is required • Describes classes and properties of RDF resources • Provides semantics for generalization hierarchies of properties and classes • With RDFSchema we can define concepts and make simple relations between them. Dickson Chiu 2011

22. RDF Example This stamp is from England Predicate object subject hence from Europe. Dickson Chiu 2011

23. RDF Schema Example Country Stamp from in Continent Dickson Chiu 2011

24. OWL • But, RDF schema is limited. • A language needs more expression and logic to make good reasoning possible. • relations between classes • e.g., disjointness • cardinality • e.g. “exactly one” • richer typing of properties • That’s why OWL (The Web Ontology Language) was invented. • characteristics of properties (e.g., symmetry) • BOTH OWL and RDF are standards of www.w3.org Dickson Chiu - update 2011

25. SWRL • Finally, to reason, you need rules. • Rules are formulated in SWRL (Semantic Web Rule Language) Dickson Chiu 2011

26. SWRL Example <ruleml:imp> <ruleml:_rlabruleml:href="#example1"/> <ruleml:_body> <swrlx:individualPropertyAtomswrlx:property="hasParent"> <ruleml:var>x1</ruleml:var> <ruleml:var>x2</ruleml:var> </swrlx:individualPropertyAtom> <swrlx:individualPropertyAtomswrlx:property="hasBrother"> <ruleml:var>x2</ruleml:var> <ruleml:var>x3</ruleml:var> </swrlx:individualPropertyAtom> </ruleml:_body> <ruleml:_head> <swrlx:individualPropertyAtomswrlx:property="hasUncle"> <ruleml:var>x1</ruleml:var> <ruleml:var>x3</ruleml:var> </swrlx:individualPropertyAtom> </ruleml:_head> </ruleml:imp> • I got this stamp from my uncle. • The rule for calling someone my uncle is that one of my parents has a brother. brother son of mother or father I Dickson Chiu 2011

27. SPARQL • Suppose, I want to search for a specific stamp. • “I want all the red stamps, designed in Europe, but used in the U.S.A., between 1980 and 1990” • We can use SPARQL (Protocol and RDF Query Language). Dickson Chiu 2011

28. URI • Because the web is decentralized and data is in many places, not only language is important. • Exchange of data between different machines is key. • To make a connection a machine needs a source. For this, we use resource identifiers. • Best known resource identifier is the URI • which consists of a name (urn) and a location (url) Dickson Chiu 2011

29. XRI & XDI • URIs have international limitations and the need for data-exchange between machines is rapidly growing. • There is a successor: XRI (Extensible Resource Identifier) • There is a standard for sharing, linking and synchronizing data. • This standard is called XDI (XRI Data Interchange). Dickson Chiu 2011

30. OAuth API • However, data is often protected. • We need consent and a key to gain access. • The key to certain data is described in an API (an application programming interface). • An open standard for accessing (authentication) the API is OAuth. Dickson Chiu 2011

31. Berner-Lee’s Architecture ??? ??? ??? SWRL OWL  Semantics+reasoning ?  Relational Data ?  Data Exchange • Relationship between layers is not clear • OWL extends of RDF / schema Dickson Chiu - update 2011

32. 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 2011

33. 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 2011

34. 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 2011

35. 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://www.meetu.hk/ Which one is better? Why? Key issue => capturing and applying domain knowledge Dickson Chiu - update 2011

36. 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 2011

37. 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 2011

38. 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 2011

39. 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 2011

40. Semantic basede-Marketplace Conceptual Model Dickson Chiu - update 2011

41. Overall e-Negotiation Process Design Methodology Requirements elicitation phase Decision phase Dickson Chiu - update 2011

42. 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 2011

43. 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 2011

44. 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 2011

45. 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 2011

46. 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 2011

47. 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 2011

48. 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 2011

49. 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 2011

50. 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 2011