1. nov 2002© Per Flensburg Ontology An introduction and overview

2. nov 2002© Per Flensburg The concept of ontology Ontology have something to do with meaning It is used within data bases and artificial intelligence. Often called “semantics” since it deals with meaning of lingusitic expressions These concepts are here used synonymously.

3. nov 2002© Per Flensburg Sources Main source for these slides: Dieter Fensel: Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce lnteresting articles about semantics and ontology: http://w3.msi.vxu.se/~per/IVC743/Semantics.html http://w3.msi.vxu.se/~per/IVC743/Semantics.html There are also reports from some courses in Växjö dealing with various things about ontology

4. nov 2002© Per Flensburg Definition (från AI) In the simplest case, an ontology describes a hierarchy of concepts related by subsumption relationships; in more sophisticated cases, suitable axioms are added in order to express other relationships between concepts and to constrain their intended interpretation.

5. nov 2002© Per Flensburg More definitions Fensel: shared and common understanding of a domain that can be communicated between people and heterogeneous and widely spread application systems. Fensel again: ontologies describe the static domain knowledge of a knowledge-based system.

6. nov 2002© Per Flensburg More about ontologies A language for defining ontologies is syntactically and semantically richer than common approaches for databases. The information that is described by an ontology consists of semi-structured natural language texts and not tabular information. An ontology must be a shared and consensual terminology because it is used for information sharing and exchange. An ontology provides a domain theory and not the structure of a data container.

7. nov 2002© Per Flensburg Example -schedule

8. nov 2002© Per Flensburg Schedule - syntax Simple table structure Data base schema

9. nov 2002© Per Flensburg Schedule - instance A row in the schedule Is in fact a representation of a fact.

10. nov 2002© Per Flensburg Schedule - Data Description The number of the week, according to standard ISO 321-543-432-645.a The day expressed as weekday, number and month Is a meta-meta description in relation to the fact

11. nov 2002© Per Flensburg Ontology for IVC743 Name: Schedule at VXU Purpose: Temporal relation between the following entities: Room, Person and Activity. Temporal expression: Week, day and time in sep-oct Room-domain: All lecture rooms at Växjö university with a capacity of at least 35 persons Person-domain: PF, RL, Inge Andersson, Olle Dahlborg, Carina Hallqvist, Bertil Ekdahl, Anna Wingkvist, Gunnar Mosnik, Jonas Richardson Activity-domain: {a description of what is going to be dealt with in each lecture}

12. nov 2002© Per Flensburg Purpose The ontology can be used in many cases, not only for our schedule It says something about the content, not the form If you see an instance with a value not belonging to the ontology, you know something is incorrect If you are familiar with the ontology, no further explanations is needed in order to understand the meaning.

13. nov 2002© Per Flensburg Course ontology This generic ontology has the following syntax: Name: Purpose: relation between Temporal expression: Room-domain: Person-domain: Activity-domain:

14. nov 2002© Per Flensburg Initiatives Resource Description Framework (RDF) Semantic web XML Schemes, standard for describing the structure and part of the semantics of data. XSL, describing mappings between different presentation sheets.

15. nov 2002© Per Flensburg Intranets and semantics In a fast changing world knowledge becomes increasingly important Maintaining and accessing knowledge (organisational memory) is thus important. The knowledge is often weakly structured, stored in intranets and in different formats (picture, sound etc.) Knowledge management, which turn information into useful knowledge is thus heavily needed. Knowledge = Content

16. nov 2002© Per Flensburg Document management Key-word based retrieval provides lots of irrelevant information out of context. Extracting information requires human attention, both for extracting and integrating Maintaining weakly structured sources is time- consuming

17. nov 2002© Per Flensburg Semantic possibilities Search for content, not key-words Query answering instead of information retrieval Correct exchange of structured or semi- structured information via for instance XSL. Define view on documents or sets of documents, information fusion

18. nov 2002© Per Flensburg Agents that find the best shopping opportunity. B2C-site Example: Shop-bots B2C-site Shop-bot Wrapper

19. nov 2002© Per Flensburg Problems with current shop-bots A wrapper is needed for each place and type of bot. No flexibility in retrieving the information Information at the B2C-site must be provided in a structured form. Usually this information is provided in natural language also which inevitably will cause inconsistency problems

20. nov 2002© Per Flensburg Solution Using various XML-techniques provides better possibilities for translation between the bot and the site. However, they must share the same ontology. An ontology describes the various products and can be used to navigate and search automatically for the required information.

21. nov 2002© Per Flensburg Electronic commerce B2B Standard techniques, such as EDIFACT cumbersome and error-prone and not integrated with other documents. The XML-family of techniques can be used for describing syntax and semantics of data, but not for the business processes and for the products. Standard ontologies in combination with XLS-based translation services is thus needed.

22. nov 2002© Per Flensburg About ontologies A brief introduction

23. nov 2002© Per Flensburg Definition An ontology is a formal, explicit specification of a shared conceptualisation. A conceptualisation refers to an abstract model of some phenomenon in the world which identifies the relevant concepts of that phenomenon. Explicit means that the type of concepts used and the constraints on their use are explicitly defined. Formal refers to the fact that the ontology should be machine readable.

24. nov 2002© Per Flensburg Role of an ontology Facilitate the construction of a domain model by providing a vocabulary of terms and relations. Still the problem of translating between different ontologies persist. Also when you go outside the target for the ontology you are lost. The ontology might conserve a certain way of thinking.

25. nov 2002© Per Flensburg Types of ontologies Domain ontologies capture the knowledge valid for a particular type of domain Metadata ontologies like Dublin Core provide a vocabulary for describing the content of on-line information sources (Libraries). Generic or common sense ontologies aim at capturing general knowledge about the world, providing basic notions and concepts for things like time, space, state, event etc.

26. nov 2002© Per Flensburg More types Representational ontologies provide representational entities without stating what should be represented. A well-known representational ontology is the Frame Ontology which defines concepts such as frames, slots, and slot constraints allowing the expression of knowledge in an object-oriented or frame-based way. Method and task ontologies provide a reasoning point of view on domain knowledge such as hypothesis, cause-effect statements etc.

27. nov 2002© Per Flensburg Constructing ontologies Prerequisite: ontologies are small modules with a high internal coherence and a limited amount of interaction between the modules. Constructing a new ontology is a matter of assembling existing ones. Inclusion Restriction Polymorphic refinement

28. nov 2002© Per Flensburg Formal languages Various kind of formal languages are used for representing ontologies, among others Description logics Frame Logic First-order predicate logic extended with meta- capabilities to reason about relations.

29. nov 2002© Per Flensburg The Sensus system The basic idea is to use so-called seed elements which represent the most important domain concepts for identifying the relevant parts of a toplevel ontology. The selected parts are then used as starting points for extending the ontology with further domain specific concepts.

30. nov 2002© Per Flensburg Word-Net An on-line lexical reference system. English nouns, verbs, adjectives and adverbs are organized into synonym sets, each representing one underlying lexical concept. Different semantic relations link the synonym sets. WordNet contains around 100.000 word meanings organized in a taxonomy. http://www.cogsci.princeton.edu/~wn/

31. nov 2002© Per Flensburg Semantical relationships Synonymy : Similarity in meaning of words. Antonymy : Dichotomy in meaning of words Hyponymy : Is-a relationship between concepts. This is-a hierarchy ensures the inheritance of properties from superconcepts to subconcepts. Meronymy : Part-of relationship between concepts. Morphological relations which are used to reduce word forms.

32. nov 2002© Per Flensburg Features of Word-Net Free of charge Multilingual European version also exists ( http://www.let.uva.nl/~ewn ) http://www.let.uva.nl/~ewn Its large size (i.e., number of concepts) Its domain-independence Its low level of formalization The definitions are vague and limits the possibility for automatic reasoning support

33. nov 2002© Per Flensburg Example

34. nov 2002© Per Flensburg Example (con’t)

35. nov 2002© Per Flensburg CYC http://www.cyc.com/http://www.cyc.com/ Comes from AI Humans decide based on their common sense knowledge what to learn and what not to learn from their observations. CYC started as an approach to formalize this knowledge and provide it with a formal and executable semantics. Hundreds of thousands of concepts have been formalized with millions of logical axioms, rules, and other assertions.

36. nov 2002© Per Flensburg Features of CYC The upper-level ontology of CYC with 3000 concepts has been made publicly available. Most of the more specific concepts are kept secret CYC groups concepts into microtheories to structure the overall ontology. They are a means to express context dependency i.e., what is right in one context may be wrong in another CycL, a variant of predicate logic, is used as language for expressing these theories.

37. nov 2002© Per Flensburg

38. nov 2002© Per Flensburg TOVE (TOronto Virtual Enterprise) Task and domain-specific ontology. The ontology supports enterprise integration, providing a shareable representation of knowledge in a generic, reusable data model TOVE provides a reusable representation (i.e., ontology) of industrial concepts. http://www.eil.utoronto.ca/tove/toveont.html

39. nov 2002© Per Flensburg From home-page

40. nov 2002© Per Flensburg Next picture

41. nov 2002© Per Flensburg Characteristics It provides a shared terminology for the enterprise that each agent can jointly understand and use It defines the meaning of each term in precise and unambiguous manner as possible It implements the semantics in a set of axioms that will enable TOVE to automatically deduce the answer to many “common sense” questions about the enterprise It defines a symbology for depicting a term or the concept constructed thereof in a graphical context

42. nov 2002© Per Flensburg (KA) 2 – a case study on Knowledge Annotation Initiative of Knowledge Acquisition Community http://www.aifb.uni-karlsruhe.de/WBS/broker/KA2.html The process of developing an ontology for a heterogeneous and world-wide (research) community The use of the ontology for providing semantic access to on-line information sources of this community.

43. nov 2002© Per Flensburg Example in (KA) 2 Class: research-topic Attributes: Name: Description: Approaches: Research-groups: Researchers: Related-topics: Subtopics: Events: Journals: Projects: Application-areas: Products: Bibliographies: Mailing-lists: Webpages: International-funding-agencies: National-funding-agencies: Author-of-ontology: Date-of-last-modification:

44. nov 2002© Per Flensburg Procedure A lot of instances of the schema was developed and published on the home page Examples: specification languages knowledge acquisition methodologies agent-oriented approaches knowledge acquisition from natural language knowledge management

45. nov 2002© Per Flensburg Knowledge management An application

46. nov 2002© Per Flensburg Knowledge management deals with Acquiring Maintaining Accessing knowledge of an organization. Here we will apply it to internet and concentrate on the last issue: Search for knowledge.

47. nov 2002© Per Flensburg Search engines They have typically three parts: A webcrawler for downloading, an indexer for finding key terms and a query interface that retrieves answers to the proposed questions. They are all based on keywords. The indexing process of the web-pages is thus crucial for the retrieval.

48. nov 2002© Per Flensburg Search domain Consists of about 300 millions fix documents, but this is only about 20% of what is available in total. The rest (80%) is dynamically generated (example: Aftonbladet) Altavista provides it all, Google sort according to documents pointing at the actual document and Yahoo uses human invention.

49. nov 2002© Per Flensburg Dimensions in searching Precision : how many retrieved documents are really relevant? Recall : have I found all relevant information? Time : for the humans to find the desired information among the retrieved. Scattering :The information might be scattered over several pages with only implicit relations between them

50. nov 2002© Per Flensburg Ontobroker Define an ontology Use it to annotate/structure/wrap your web documents Somebody else can make use of Ontobroker’s advanced query and inference services to consult your knowledge. To achieve this goal, Ontobroker provides three interleaved languages and two tools.

51. nov 2002© Per Flensburg Languages It provides a representation language formulating ontologies. A subset of it is used to formulate queries, i.e. to define the query language. An annotation language is offered to enable knowledge providers to enrich web documents with ontological information.

52. nov 2002© Per Flensburg Representation language

53. nov 2002© Per Flensburg Some definitions Class definition: c[] defines a class with name c. Attribute definition: c[a=>> {c 1,...,c n }] implies that the attribute a can applied to the elements of c and an attribute value must be member of all classes c 1,...,c n. Is-a relationship: c 1 :: c 2 defines c 1 as a subclass of c 2 which implies that: all elements of c 1 are also elements of c 2 all attributes and their value restrictions defined for c 2 are also defined for c 1, and multiple attribute inheritance exists

54. nov 2002© Per Flensburg More definitions Is-element-of relationship: e : c defines e as an element of the class c. Rules like FORALL x,y x[a ->> y] > x]. If a is an attribute for x it is also an attribute for y FORALL x,y x:c 1 [a 1 ->> y] y:c 2 [a 2 ->> x]. The common set of attributes for x and y

55. nov 2002© Per Flensburg Annotation language Ontobroker provides an annotation language called HTML A The following HTML page states that the text string „Richard Benjamins“ is the name of a researcher where the URL of his homepage is used as his object id. Welcome to my homepge My name is Richard Benjamins. Cf XML!

56. nov 2002© Per Flensburg Query language The query language is defined as a subset of the representation language. The elementary expression is: X ∈ c Λ attribute(x) = v written in Frame logic: x[attribute -> v] : c

57. nov 2002© Per Flensburg Inference engine

58. nov 2002© Per Flensburg Summary Ontobroker only recognises pages that are annotated according to its rules. Principally you still have a database and a database schema in the bottom What can’t be expressed in first order predicat logic can’t be expressed.

59. nov 2002© Per Flensburg New idea (On2broker) The web Info agentData base Fact retrieval Ontologies XML, RDF etc. Query engine

60. nov 2002© Per Flensburg Cf, Skogsresurs Concept base Search agentURL Classification URL-database Profile Interesting links User www.skogsresurs.com

61. nov 2002© Per Flensburg Comments Facts are defined by the logic of the ontologies They are retrieved according to logical rules They are stored into formal databases Thus only facts possible to express in first order predicat logic is possible to retrieve. Also On2broker has severe efficiency problems if the number of facts extend 100 000

62. nov 2002© Per Flensburg IBROW: Dynamic reasoning Brokering dynamic reasoning services in WWW http://www.swi.psy.uva.nl/projects/IBROW3/home.html It will access libraries in the Internet, search for appropriate inference services, verify their requirements, request additional information from the customer if needed, adapt the inference services to the particular domain knowledge, plug them together, and execute them via CORBA. Therefore, the user no longer buys, downloads and installs software. Instead he uses it as a computational service provided via the network

63. nov 2002© Per Flensburg Use of IBROW In a business-to-business (B2B) context, IBROW technology can be used to construct half products, which then need further processing by industries before delivering end products to consumers. For example, a car manufacturer could be interested in a service that helps him to develop and/or adapt a new car design. In another scenario, the IBROW broker provides a service to configure the bare bones of a knowledge system, which then needs to be refined for end consumers based on their particular needs.

64. nov 2002© Per Flensburg Other use Yet another model would use IBROW technology to provide an underlying infrastructure to support knowledge engineers in selecting, testing, adapting, refining, and combining generic components into concrete systems.

65. nov 2002© Per Flensburg Electronic Commerce An application are for ontologies

66. nov 2002© Per Flensburg Types of products Intelligent information search agents (i.e., shopping agents) that help customers to find products. Intelligent information providers (i.e., on-line stores) that help vendors to present their goods in appropriate manner. Intelligent information brokers (i.e., on-line market places) that mediate between buyers and vendors.

67. nov 2002© Per Flensburg Shopbots Client (Browser) Shopbot Wrapper 1 On-line store 1 Wrapper 2 On-line store 2 Wrapper 3 On-line store 3

68. nov 2002© Per Flensburg Examples Bookblvd (http://www.bookblvd.com/ ),http://www.bookblvd.com/ Bottom Dollar (http://www.bottomdollar.com/ ),http://www.bottomdollar.com/ Buyer’s Index (http://www.buyersindex.com/ ),http://www.buyersindex.com/ CompareNet (http://www.compare.net/ ),http://www.compare.net/ Dealpilot (http://www.dealpilot.com/ ),http://www.dealpilot.com/ Jango (http://www.jango.com/ ),http://www.jango.com/ Junglee (http://www.junglee.com/)http://www.junglee.com/ MyShop (http://www.myshop.de ),http://www.myshop.de Shopfind (http://www.shopfind.com/ )http://www.shopfind.com/ Shopper (http://www.shopper.com ).http://www.shopper.com

69. nov 2002© Per Flensburg Why shopbots fail Web users do not want to pay because they are used to free service. Product providers do not want to fund the agent because of its ability to always find the cheapest source. Product providers would fund the agent if it manipulated the search results. This would eliminate objectivity however which is a requirement for high acceptance. In the end, most of them were bought by Internet portals I think Fensel principally has right, but in practice shop-bots have become a tremendous succes! I think Fensel principally has right, but in practice shop-bots have become a tremendous succes!

70. nov 2002© Per Flensburg Other types of bots Lifestyle finder recommends documents matching your interests based on your answers to a set of questions. Alexa: watches all its users and anticipates their preferences. An “intelligent” proxy which caches the pages a user will visit. Firefly asks for ratings of specific musical artists, correlates each user with others who share their tastes, and recommends songs or albums which their cohorts have rated highly.

71. nov 2002© Per Flensburg B2B 1:1, negotiation, often EDIFACT-based 1:N, A large company dictates, often EDIFACT N:M, a fragmented marketplace

72. nov 2002© Per Flensburg The fragmented marketplace Replace a trade agent Bring seller and buyer together on global base Require means for translation and representation Require means for content description

73. nov 2002© Per Flensburg Translation

74. nov 2002© Per Flensburg Representation and translation RDF provides a standard for describing semantics XML provides a standard for describing the structure of a document XML schema provides a standard for describing the semantics XSL provides a standard for describing mappings between terminologies But none provides a standard vocabulary

75. nov 2002© Per Flensburg Ontologies in B2B Corresponds to standardised product catalogues. Attempts: Common business library (http://www.commerce.net)http://www.commerce.net Commerce XML (http://www.oasis-open.org )http://www.oasis-open.org Dublin Core (http://www.indecs.org )http://www.indecs.org Rosettanet (http://www.rosettanet.org/ )http://www.rosettanet.org/ Etc.

76. nov 2002© Per Flensburg Branch portals Chemdex (www.chemdex.com)www.chemdex.com Life science products trading PaperExchange (www.paperexchange.com)www.paperexchange.com Paper industry spot market VerticalNet (www.verticalnet.com)www.verticalnet.com Generic portal

77. nov 2002© Per Flensburg VerticalNet Seller ontology 1 Seller ontology 2 Seller ontology 3 Buyer ontology 1 Buyer ontology 2 Buyer ontology 3 TransformationVisualisation Standard ontology Ontology mapping

78. nov 2002© Per Flensburg To sum it all up Putting it all together

79. nov 2002© Per Flensburg The techniques XML: Describing the structure of a document, cf. defining layout in a database DTD: Element declaration that define composed tags and value ranges for elementary tags Attribute declaration that define attributes of tags Entity declaration XSL: Describes how to render the document and the processing of the parts there, cf. calculating fields in a database. Can be used for translation

80. nov 2002© Per Flensburg The role of XSL XML DTD 1 XML DTD 2 XSL

81. nov 2002© Per Flensburg A document XML DTD XSL Name: Value on name Part: Value on part Quantity: Value on quant Price: Value on price Part: Value on part Quantity: Value on quant Price: Value on price Total price: Value on total Adress: Value on adr Ontololgy

82. nov 2002© Per Flensburg XML and semantics XML describe documents in tree format Thus the overlaying context is transferred to the underlying branches This is the only semantics that exists.

83. nov 2002© Per Flensburg RDF A subject is an entity that can be referred to by a address in the WWW (i.e., by an URI). A predicate defines a binary relation between resources and/or atomic values provided by primitive data type definitions in XML. An object specifies for a subject a value for a predicate. That is, objects provide the actual characterizations of the Web documents.

84. nov 2002© Per Flensburg Example of RDF Author(http://www.msi.vxu.se/~per/) = Xhttp://www.msi.vxu.se/~per/ Name(X) = Per Flensburg Email(X) = per.flensburg@msi.vxu.seper.flensburg@msi.vxu.se Claim(Anna)=(affiliation(Author( (http://www.msi.vxu.se/~per/))=Växjö university)

85. nov 2002© Per Flensburg Fine