Ontology

1. Ontology

2. An Ontology Example

3. In OKBC

4. Knowledge Representation Language • There is no logical difference between a graphical and a textual rendition of an ontology. • Knowledge representation language typically based on a particular logic, with the logic itself being a language with a syntax and a semantics. • We call the language in which the ontology is represented a logic-based language.

5. Machine-interpretable • High-end ontology languages are backed by a rigorous formal logic, which thereby makes the ontology machine-interpretable. • Software supported by ontologies moves up to the human knowledge/conceptual level; humans do not have to move down to the machine level. The computer and its software can interpret the semantics of the model directly—without direct human involvement.

6. Two Definitions of Ontology • An ontology defines the common words and concepts (the meaning) used to describe and represent an area of knowledge. • An ontology is an engineering product consisting of "a specific vocabulary used to describe [a part of] reality, plus a set of explicit assumptions regarding the intended meaning of that vocabulary"—in other words, the specification of a conceptualization.

7. Describe • Describing an area of knowledge is the act of expressing, in either written or spoken words, the important points about a specific area of knowledge. • When describing an area of knowledge—a domain—we describe the important things in the domain, their properties, and the relationships among the things. If we were to elaborate our description, we may even include rules about the domain.

8. Describe • Therefore, a description is or can be an ontology. The concepts included: • Classes (general things) in the many domains of interest • Instances (particular things) • The relationships among those things • The properties (and property values) of those things • The functions of and processes involving those things • Constraints on and rules involving those things

9. Representation • The levels of representation needed for models: • Syntax • Structure • Semantics • Pragmatics

10. Structure • Database schema is primarily a way to both describe and prescribe the structure of a database. • By prescribe, we mean that the objects of the database—the tables, columns, rows, and values—are required to adhere to the structure of the schema.

11. Conceptual models, such as those written in UML, are also concerned with structure.

12. Structure can typically be represented by a node-and-edge graph theory.

13. Semantic • Semantic interpretation is the mapping between some structured subset of data and a model of some set of objects in a domain with respect to the intended meaning of those objects and the relationships between those objects. • To have the computer assist in the dissemination of the knowledge embedded in a document—truly realize the Semantic Web—we need to at least partially automate the semantic interpretation process.

14. Semantic • We need to describe and represent in a computer-usable way a portion of our mental models about specific domains. Ontologies provide us with that capability. • This is a large part of what the Semantic Web is all about. The software of the future will be able to use the knowledge encoded in ontologies to at least partially understand, to semantically interpret, our Web documents and objects.

15. Software engineering and computer science has evolved higher-level languages that are much more aligned with the human semantic/conceptual level. Ontologists want to push it even farther.

16. By machine semantic interpretation, we mean that by structuring the symbols humans supply, the machine will conclude via an inference process roughly what a human would in comparable circumstances.

17. Mapping from Syntax to Semantics • The relationship between an alphabet and its construction rules for forming words in that alphabet is mapped to formal objects in the semantic model for which those symbols and the combinatoric syntactic rules for composing those symbols having a specific or composed meaning.

18. The mappings between semantics levels-- can also be viewed as simply the expansion of the semantics from more simple to more complex elaborations The machine semantics is very primitive, simple, and inexpressive with respect to the complex, rich semantics of humans.

19. But by designing a logical knowledge representation system and ontologies, and getting the machine to infer conclusions that are extremely close to what humans would in comparable circumstances, we will have imbued our systems with much more human-level semantic responses than they have at present.

20. Pragmatics • 語用學所研究的是符號和符號使用者之間所產生的種種現象、關係與特質 • 語用學討論記號使用者如何及為何使用某特定記號

21. Pragmatics • Pragmatics sits above semantics and has to do with the intent of the semantics and actual semantic usage. • There is very little pragmatics expressed or even expressible in programming or databases languages. • The little that exists in some programming languages like C++ is usually expressed in terms of pragmas, or special directives to the compiler as to how to interpret the program code.

22. Pragmatics for Semantic Web • Pragmatics will increasingly become important in the Semantic Web, once the more expressive ontology languages such as RDF/S and OWL are fully specified and intelligent agents begin to use the ontologies that are defined in those languages. • Intelligent agents will have to deal with the pragmatics (think of pragmatics as the extension of the semantics) of ontologies.

23. Intelligent Agent • For example, some agent frameworks, such as that of the Foundation for Intelligent Physical Agents (FIPA) standards consortium, use an Agent Communication Language that is based on speech act theory, which is a pragmatics theory about human discourse that states that human beings express their utterances in certain ways that qualify as acts, and that they have a specific intent for the meaning of those utterances.

24. Communication Acts of the Agents • In these high-end agents, state transition tables are often used to express the semantics and pragmatics of the communication acts of the agents. • A communication act, for example, would be a request by one agent to another agent concerning information (typically expressed in an ontology content language such as Knowledge Interchange Format [KIF])[9]—that is, either a query (an ask act, a request for information) or an assertion (a tell act, the answer to a request for information).

25. Pragmatic Web • When developers and technologists working in the Semantic Web turn their focus to the so-called web of proof and trust, pragmatic issues will become much more important, and one could then categorize that level as the Pragmatic Web. • Although some researchers are currently working on the Pragmatic Web, in general, most of that level will have to be worked out in the future.

26. Syntactic, Semantic, and Pragmatic layers for Human Language

27. FIPA agent messages: Request and agree

28. Intelligent Agent Syntax, Semantics, and Pragmatics

29. Expressing Ontologies Logically • Ontologies are usually expressed in a logic-based knowledge representation language, so that fine, accurate, consistent, sound, and meaningful distinctions can be made among the classes, instances, properties, attributes, and relations. • Some ontology tools can perform automated reasoning using the ontologies, and thus provide advanced services to intelligent applications such as conceptual/semantic search and retrieval (non-keyword based), software agents, decision support, speech and natural language understanding, knowledge management, intelligent databases, and electronic commerce.

30. Ontological Engineering • The recent computational discipline that addresses the development and management of ontologies is called ontological engineering. • Ontological engineering usually characterizes an ontology (much like a logical theory) in terms of an axiomatic system, or a set of axioms and inference rules that together characterize a set of theorems (and their corresponding formal models)-all of which constitute a theory.

31. Ontological Engineering • In the technical view of ontological engineering, an ontology is the vocabulary for expressing the entities and relationships of a conceptual model for a general or particular domain, along with semantic constraints on that model that limit what that model means. • Both the vocabulary and the semantic constraints are necessary in order to correlate that information model with the real-world domain it represents.

32. Theorems are proven from axioms using inference rules. Together, axioms, inference rules, and theorems constitute a theory.

33. Example: Portion of an ontology represented as axioms and inference rules

34. Example: Ontology of Electronic Commerce in English.

35. Three components of the meaning of natural languages like English

36. Term • The first component, at the lower left, is the terms, that is, the symbols (the labels for the concepts) or the words of English and the rules for combining these into phrases and sentences (the syntax of English). • In themselves, they have no meaning until they are associated with the other components, such as other angles of "Concepts" and "Real-World Referents."

37. Term • For each referred term, there is an associated thing in the world and there is a concept in our human mental model that stands for (or "represents") that real thing in the world. • That is why there is a dotted line between Term and Real-World Referent. There is no direct link. Humans need a concept to mediate between a term and the thing in the world the term refers to.

38. A thesaurus generally works with the left-hand side of the triangle (the terms and concepts), while an ontology in general works more with the right-hand side of the triangle (the concepts and referents)

39. Thesaurus • A thesaurus is developed primarily as a classification space over a domain, a set of domains, or even over the entire world, such as Roget's 1916 thesaurus-for the purpose of conceptual navigation, search, and information retrieval. • Therefore, the semantics of the classification space can remain relatively weak, characterizing the simple semantic relations among conceptual labels (terms), and so structured mostly taxonomically by broader-than and narrower-than relations. • All you really need to know about a term node in a thesaurus is that it is semantically distinct from other nodes (hence, removing ambiguity), and it is broader than or narrower than certain other terms. • No complicated notion of the meaning has to be captured and represented.

40. Ontology • An ontology does try to capture and represent the meaning of a domain, a set of domains, or the entire world, because it attempts to explicitly simulate the meaning that a human being has in his or her mental model of the domain, set of domains, or the world. • Furthermore, an ontology is meant to be used directly by many kinds of software applications that have to operate on and so have knowledge about the domains represented by the ontology-including sometimes applications that have not yet been thought of. • Finally, an ontology is meant to be extended, refined, and reused, traits that it shares with its semantically weaker cousin, the thesaurus. • Unlike the thesaurus, however, an ontology tries to express precise, complex, consistent, and rich conceptual semantics.