Ontology

1. Ontology • An introduction and overview

2. 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. 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 • There are also reports from some courses in Växjö dealing with various things about ontology

4. 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. 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. 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. Example -schedule

8. Schedule - syntax • Simple table structure • Data base schema

9. Schedule - instance • A row in the schedule • Is in fact a representation of a fact.

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

11. 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. 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. Course ontology • This generic ontology has the following syntax: • Name:<string> • Purpose: <Temporal/causal/result…> relation between <list of entities> • Temporal expression: <ddd dd mmm hh - hh> • Room-domain: <list of lecture rooms> • Person-domain: <list of teachers> • Activity-domain: <list of activities>

14. 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. 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. 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. 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. Example: Shop-bots B2C-site B2C-site B2C-site B2C-site • Agents that find the best shopping opportunity. Shop-bot Wrapper

19. 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. 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. 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. About ontologies • A brief introduction

23. 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. 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. 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. 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. 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. 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. 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. 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. 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. Features of Word-Net • Free of charge • Multilingual European version also exists (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. Example

34. Example (con’t)

35. CYC 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. 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.

38. 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. From home-page

40. Next picture

41. 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. (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. Example in (KA)2 • Class: research-topic • Attributes: • Name: <string> • Description: <text> • Approaches: <set-of keyword> • Research-groups: <set-of research-group> • Researchers: <set-of researcher> • Related-topics: <set-of research-topic> • Subtopics: <set-of research-topic> • Events: <set-of events> • Journals: <set-of journal> • Projects: <set-of project> • Application-areas: <text> • Products: <set-of product> • Bibliographies: <set-of HTML-link> • Mailing-lists: <set-of mailing-list> • Webpages: <set-of HTML-link> • International-funding-agencies: <funding-agency> • National-funding-agencies: <funding-agency> • Author-of-ontology: <set-of researcher> • Date-of-last-modification: <date>

44. 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. Knowledge management • An application

46. 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. 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. 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. 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. 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.