Metadata for Web-based Information Management through Ontology Dickson K. W. CHIU Senior Member, IEEE & ACM Dickson Computer Systems Hong Kong kwchiu@acm.org, dicksonchiu@ieee.org 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

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!) Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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! Ontology Dickson Chiu - update 2011

What is the Problem? Consider a typical web page: 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: Ontology Dickson Chiu - update 2011

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 … Ontology Dickson Chiu - update 2011

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 … Ontology Dickson Chiu - update 2011

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> Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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) Ontology Dickson Chiu - update 2011

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. Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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.” Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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

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

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

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

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

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> <title>PO Box</title> <country>England</country> <year>1992</year> </collection> Dickson Chiu 2011

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 RDF Schema we can define concepts and make simple relations between them. Dickson Chiu 2011

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

RDF Schema Example Country Stamp from in Continent Dickson Chiu 2011

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 Ontology Dickson Chiu - update 2011

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

SWRL Example I mother or father I got this stamp from my uncle. <ruleml:imp> <ruleml:_rlab ruleml:href="#example1"/> <ruleml:_body> <swrlx:individualPropertyAtom swrlx:property="hasParent"> <ruleml:var>x1</ruleml:var> <ruleml:var>x2</ruleml:var> </swrlx:individualPropertyAtom> <swrlx:individualPropertyAtom swrlx:property="hasBrother"> <ruleml:var>x3</ruleml:var> </ruleml:_body> <ruleml:_head> <swrlx:individualPropertyAtom swrlx:property="hasUncle"> </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. son of brother I mother or father Dickson Chiu 2011

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

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) URI URN Red PO Box URL http://www.mystampcollection.com/redpobox Dickson Chiu 2011

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

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

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

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” Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

What is an e-Marketplace? Suppliers e - Marketplace offers Aggregate requests Repository bids from Buyers, contact potential Suppliers, Ontologies and Concepts match Suppliers e - Negotiation data offers and Buyers, exchange Agreements - … bids and offers, generate e - Contract bids Buyers Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

Example Ontology Clothing and Sales Negotiation Quantity Purple Red Discount Total Amount Refunding Policy Color Size Appearance Clothing Unit Cost Payee Insured Amount Insurer Premium {unordered} attributes: deposit, installment, pay-upon-delivery, ... {unordered} attributes: brick red, crimson, ... {ordered} attributes: small, medium, large, extra-large attributes: light purple, magenta, ... Delivery Date Sale Order * Delivery Shipping Cost Payment Terms Insurance Ontology Dickson Chiu - update 2011

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… Ontology Dickson Chiu - update 2011

Semantic based e-Marketplace Conceptual Model Ontology Dickson Chiu - update 2011

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

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. Ontology Dickson Chiu - update 2011

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 Dickson Chiu - update 2011

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 Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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. Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

System Implementation Architecture Multiplatform Support Subsystem WAP Gateway SMS Internet Messenger Web Server e-Negotiation Executing Subsystem e-Negotiation Session Manager Ontology Generator e-Negotiating Matching Subsystem Process Generator Task Organizer Issue Dependency Editor issue dependency task Ontology Maintenance Subsystem Search Engine Criteria & Issues Editor ontology Criteria Issue bids & offers e-Negotiation process revised ontology, issues existing Data & Repository Multiplatform Devices Ontology Dickson Chiu - update 2011

OWL Listing <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: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: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> Ontology Dickson Chiu - update 2011

Summary Function Traditional e-marketplace problem Contributions 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 Dickson Chiu - update 2011

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 Dickson Chiu - update 2011

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 Ontology Dickson Chiu - update 2011

Summary Dickson Chiu 2011

Limitations of Current IM Technologies Searching information Keyword-based search engines Extracting information human involvement necessary for browsing, retrieving, interpreting, combining Maintaining information inconsistencies in terminology, outdated information. Viewing information Impossible to define views on Web knowledge Dickson Chiu 2011

Ontology based IM Information / knowledge will be organized in conceptual spaces according to its meaning. Automated tools for information maintenance and knowledge discovery Semantic query answering Query answering over many documents Defining who may view certain parts of information (even parts of documents) will be possible. Dickson Chiu 2011

Agent-base IM An agent is a computer system that is capable of flexible, autonomous action on behalf of its user or owner in order to meet its design objectives in a designated environment. Many other definitions … Your own personal (digital) automatic assistant knows about your preferences builds up knowledge base using your past can combine the local knowledge with remote services: hotel reservations, airline preferences dietary requirements medical conditions calendaring etc It communicates with remote information (i.e., on the Web!) All the above can be facilitated with ontology Dickson Chiu 2011

Intelligent Agents & Ontology Metadata Identify and extract information from Web sources Ontologies Web searches, interpret retrieved information Communicate with other agents Logic Process retrieved information, draw conclusions Dickson Chiu 2011

Agent: B2C Electronic Commmerce A typical scenario: user visits one or several online shops, browses their offers, selects and orders products. Ideally humans would visit all, or all major online stores; but too time consuming Current shopbots required too much programming Software agents that can interpret the product information and the terms of service. Pricing and product information, delivery and privacy policies will be interpreted and compared to the user requirements. Information about the reputation of shops Sophisticated shopping agents will be able to conduct automated negotiations Dickson Chiu 2011

Example: Database Integration Databases are very different in structure, in content Lots of applications require managing several databases after company mergers combination of administrative data for e-Government biochemical, genetic, pharmaceutical research etc. Most of these data are now on the Web The semantics of the data(bases) should be known how this semantics is mapped on internal structures is immaterial Dickson Chiu 2011

Example: Digital Libraries It is a bit like the search example It means catalogs on the Web librarians have known how to do that for centuries goal is to have this on the Web, World-wide extend it to multimedia data, too Ontology encodes metadata But it is more: software agents should also be librarians! help you in finding the right publications Dickson Chiu 2011

Content Management via Metadata album pages How to build an inventory for collection and search of content objects? How to deal with multiple content object types? What contextual navigation should exist between these content objects? How can we use metadata technique as the solution? artist bios album reviews

Example Album Ontology concert calendar album pages artist bios TV listings album reviews Ontology Dickson Chiu - update 2011

Video Content Ontology Example BSIM0012

Thank you! Question and Answer Email: dicksonchiu@ieee.org Ontology Dickson Chiu - update 2011