1. Ontologies, Intelligent Software Agents on the Semantic Web Oscar Lin Athabasca University June 26, 2006 ITS 2006, Taipei

2. Outline 1. Overall of Ontologies 1. Overall of Ontologies 2. Importance of ontologies in the Semantic Web 2. Importance of ontologies in the Semantic Web 3. 3.

3. 1. Overall of Ontologies Ontologies are about vocabularies and their meanings, with explicit, expressive, and well-defined semantics --- possibly machine-interpretable. Ontologies are about vocabularies and their meanings, with explicit, expressive, and well-defined semantics --- possibly machine-interpretable. What does this statement mean? What does this statement mean? What’s a vocabulary? What’s a vocabulary? What’s a meaning? What’s a meaning? What is semantics? What is semantics? What does machine-interpretable mean? What does machine-interpretable mean? What is ontology and what are ontologies? What is ontology and what are ontologies?

4. Ontology Example Person Organization Employee Staff_Employee Group Management_Employee Division Department DirectorPresidentManager Vice_President Company isa Part_of isa

5. HR Ontology Example Person Organization Employee Staff_Employee Group Management_Employee Division Department DirectorPresidentManagerVice_President Company isa Part_of isa manages

6. HR Ontology Example Person Organization Employee Staff_Employee Group Management_Employee Division Department DirectorPresidentManagerVice_President Company isa Part_of isa manages Part_of Managed_by Part_of employs Employee_of Concepts: --- correspond to the mental concepts that human beings have when they understand a particular body of knowledge, or subject matter area or domain.

7. HR Ontology Example Person Address String Name String Birthdate String ssn String Organization Employee Staff_Employee Group Management_Employee Division Department Director President ---------------------------------- Manages classes Company Manager Vice_President Company isa Part_of isa manages Part_of Managed_by Part_of employs Employee_of

8. Concepts, Relations, Properties/Attributes, Value Range These concepts and the relationships between them are usually implemented as classes, relations, properties, attributes, and value (of the properties/attributes). These concepts and the relationships between them are usually implemented as classes, relations, properties, attributes, and value (of the properties/attributes). The relations between these entity-focused concepts, such as employee-of, managed_by, and manages. The relations between these entity-focused concepts, such as employee-of, managed_by, and manages. Finally properties or attributes are depicted, examples include address, name, birth-date, and ssn under the Person class. Finally properties or attributes are depicted, examples include address, name, birth-date, and ssn under the Person class. These properties or attributes have either explicit values or, more often, have value ranges. These properties or attributes have either explicit values or, more often, have value ranges. The value range for the property/attribute of employee_of, a property of the class Employee, for example, is the class Organization. The value range for the property/attribute of employee_of, a property of the class Employee, for example, is the class Organization. By range we mean that the only possible values for any instances of the property employee_of defined for the class Employee must come from the class Organization. By range we mean that the only possible values for any instances of the property employee_of defined for the class Employee must come from the class Organization.

9. Rendition of an Ontology (Open Knowledge Base Connectivity Language – OKBC) (defclass (defclass (is-a USER) (is-a USER) (role concrete) (role concrete) (single-sot managed_by (single-sot managed_by (type SYMBOL) (type SYMBOL) ;+ (allowed-classes Management_Employee) ;+ (allowed-classes Management_Employee) ;+ (cardinality 1 1) ;+ (cardinality 1 1) (create-accessor read-write)) (create-accessor read-write)) (single-slot part_of (single-slot part_of (type SYMBOL) (type SYMBOL) ;+ (allowed-parents Organization) ;+ (allowed-parents Organization) ;+ (cardinality 0 1) ;+ (cardinality 0 1)

10. There is no logical difference between a graphical and a textual rendition of an ontology (or any other model, for that matter). There is no logical difference between a graphical and a textual rendition of an ontology (or any other model, for that matter). An ontology is represented in a knowledge representation language (such as a Semantic Web language like RDF/S, DAML+OIL, OWL, or in an ontology language that predates the Semantic Web, such as Ontolingua/KIF/Common Logic, OKBC, CycL, or Prolog). An ontology is represented in a knowledge representation language (such as a Semantic Web language like RDF/S, DAML+OIL, OWL, or in an ontology language that predates the Semantic Web, such as Ontolingua/KIF/Common Logic, OKBC, CycL, or Prolog). Furthermore, such ontology languages are in turn typically based on a particular logic, with the logic itself being a language with a syntax and a semantics. Furthermore, such ontology languages are in turn typically based on a particular logic, with the logic itself being a language with a syntax and a semantics.

11. Sometimes, therefore, we call the language in which the ontology is represented a logic-based language. Sometimes, therefore, we call the language in which the ontology is represented a logic-based language. So ultimately it does not matter whether you use a graphical or a textual rendition of an ontology; both are exactly equivalent. So ultimately it does not matter whether you use a graphical or a textual rendition of an ontology; both are exactly equivalent. The important issue is that of the power of the underlying language used to represent the ontology. The important issue is that of the power of the underlying language used to represent the ontology.

12. Formal Logic --- The Value of Ontologies High-end ontology languages are backed by a rigorous formal logic, which thereby makes the ontology machine-interpretable. High-end ontology languages are backed by a rigorous formal logic, which thereby makes the ontology machine-interpretable. Machine-interpretable: the semantic of the model is semantically interpretable by the machine. Machine-interpretable: the semantic of the model is semantically interpretable by the machine. The computer and its software can interpret the semantics of the model directly --- without direct human involvement. The computer and its software can interpret the semantics of the model directly --- without direct human involvement.

13. Interaction with Computers at the Human Level Software supported by ontologies moves up to the human knowledge /conceptual level Software supported by ontologies moves up to the human knowledge /conceptual level Human do not have to move down to the machine level. Human do not have to move down to the machine level. Interaction with computers takes place at our level, not theirs. Interaction with computers takes place at our level, not theirs. This is extremely important point, and it underscores the value of ontologies. This is extremely important point, and it underscores the value of ontologies.

14. Ontology Definitions: Big O and Little o Merriam-Webster Online: Merriam-Webster Online: A branch of metaphysics with the nature and relations of being A branch of metaphysics with the nature and relations of being A particular theory about the nature of being or the kinds of existents A particular theory about the nature of being or the kinds of existents Big O: Philosophical discipline Big O: Philosophical discipline Little o: IT engineering discipline Little o: IT engineering discipline

15. IT Definitions of Ontology An ontology defines the common words and concepts (the meaning) used to describe and represent an area of knowledge. 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 (Guarino, 1998) -- - in other words, the specification of a conceptualization (Gruber, 1993). 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 (Guarino, 1998) -- - in other words, the specification of a conceptualization (Gruber, 1993).

16. Two Parts of the first Definition Describing and representing an area of knowledge Describing and representing an area of knowledge Defining the common words and concepts of the description Defining the common words and concepts of the description

17. Domain A domain is a subject matter are or area of knowledge A domain is a subject matter are or area of knowledge Examples: Examples: Medicine Medicine Automobile repair Automobile repair Financial planning Financial planning Machine tooling Machine tooling Business management Business management Physics Physics Textiles Textiles Geopolitics Geopolitics

18. 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. Is the act of expressing, in either written or spoken words, the important points about a specific area of knowledge. For example, in describing automobile repair, we would probably talk about the following: For example, in describing automobile repair, we would probably talk about the following: The kinds of cars, there are sedans, station wagons, sports cars, luxury cars, compacts, domestic and foreign cars) The kinds of cars, there are sedans, station wagons, sports cars, luxury cars, compacts, domestic and foreign cars) The types of engines (corresponding perhaps to the type of fuel used: gasoline, diesel, electric-powered, hybrid) The types of engines (corresponding perhaps to the type of fuel used: gasoline, diesel, electric-powered, hybrid) The particular engines (for example, a 1995-96 V-6 Ford Taurus 244/4.0 …) The particular engines (for example, a 1995-96 V-6 Ford Taurus 244/4.0 …) The manufacturer (Ford, General Motors, Chevrolet, …) The manufacturer (Ford, General Motors, Chevrolet, …) The things that constitute cars (engines, brake systems, cooling systems, electric systems, suspension, body, and so on) and their properties (an engine has 4, 6, 8 or 12 cylinders; brake pads have different compositions such as semi-metallic or nonferrous material) The things that constitute cars (engines, brake systems, cooling systems, electric systems, suspension, body, and so on) and their properties (an engine has 4, 6, 8 or 12 cylinders; brake pads have different compositions such as semi-metallic or nonferrous material)

19. Description When describing an area of knowledge --- a domain, we describe the important things in the domain, their properties, and the relationships among the things. 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, such as the following diagnosis rule, which specifies how to determine what is wrong with an automobile system in order to repair it: If we were to elaborate our description, we may even include rules about the domain, such as the following diagnosis rule, which specifies how to determine what is wrong with an automobile system in order to repair it: If the car won’t start and it doesn’t turn over, check and clean the battery connections. If the car won’t start and it doesn’t turn over, check and clean the battery connections.

20. A Description is or can be an Ontology Classes (general things) in the many domain of interest Classes (general things) in the many domain of interest Instances (particular things) Instances (particular things) The relationships among those things The relationships among those things The properties (and property values) of those things The properties (and property values) of those things Constraints on those things Constraints on those things Rules involving those things Rules involving those things

21. Represent a description What does representation mean? Representing means that we encode the description in a way that enables someone to use the description. What does representation mean? Representing means that we encode the description in a way that enables someone to use the description. A description consists of words and phrases in a natural language (such as English or Chinese), that is, vocabulary/terminology and sentences that combine terminologies to express relationships among the terms. A description consists of words and phrases in a natural language (such as English or Chinese), that is, vocabulary/terminology and sentences that combine terminologies to express relationships among the terms. Use vocabulary and terminology as equivalent and use term for the individual word Use vocabulary and terminology as equivalent and use term for the individual word

22. Representing an Area of Knowledge Representing means that we represent the description using terms and sentences. Representing means that we represent the description using terms and sentences. We define the terms and we combine those defined terms in ways that elaborate more of the meaning about the area of knowledge. We define the terms and we combine those defined terms in ways that elaborate more of the meaning about the area of knowledge.

23. Representing Ontologies We use the terms of the natural-language description as labels for the underlying concepts --- that is, the meaning of the area of knowledge consisting of classes, properties, and relationships. We use the terms of the natural-language description as labels for the underlying concepts --- that is, the meaning of the area of knowledge consisting of classes, properties, and relationships. Typically, we represent or codify the ontology in a logical, knowledge representation language rather than a natural language, because we want to represent our description as clearly, precisely, and unambiguously as possible, and natural language can be very ambiguous. Typically, we represent or codify the ontology in a logical, knowledge representation language rather than a natural language, because we want to represent our description as clearly, precisely, and unambiguously as possible, and natural language can be very ambiguous.

24. Description Logic A knowledge representation formalism A knowledge representation formalism Sometimes called a Sometimes called a terminological logic, terminological logic, classification logic, classification logic, concept logic, or concept logic, or term subsumption logic term subsumption logic Based on a subset of first-order predicate logic that is Based on a subset of first-order predicate logic that is a declarative formalism for the representation and expression of knowledge and a declarative formalism for the representation and expression of knowledge and sound, tractable reasoning methods founded on a firm theoretical (logical) basis. sound, tractable reasoning methods founded on a firm theoretical (logical) basis.

25. Frame-based Knowledge Representation A knowledge representation formalism for expressing ontological information derived originally from the AI language called KL-1, which itself is one of the earliest formalization of the notion of semantic network. A knowledge representation formalism for expressing ontological information derived originally from the AI language called KL-1, which itself is one of the earliest formalization of the notion of semantic network.

26. Syntax, Structure, Semantics, and Pragmatics Objectives Objectives What makes one ontology better than another, What makes one ontology better than another, What features ontologies (especially those characterized as conceptual models and logical theories) provide What features ontologies (especially those characterized as conceptual models and logical theories) provide How they provide them How they provide them The importance of ontologies from the perspective of an IT manager or technical lead who must address emerging Semantic Web technologies for incorporation into the systems and practices of your company’s infrastructure and their impact on your information strategies for the future. The importance of ontologies from the perspective of an IT manager or technical lead who must address emerging Semantic Web technologies for incorporation into the systems and practices of your company’s infrastructure and their impact on your information strategies for the future.

27. Syntax A program language, just like a natural language like English, has a formal syntax. A program language, just like a natural language like English, has a formal syntax. Syntax is about order and format Syntax is about order and format In the Web work, XML has a syntax In the Web work, XML has a syntax A document that is marked up using XML is either syntactically correct or not, with respect to the syntax of XML A document that is marked up using XML is either syntactically correct or not, with respect to the syntax of XML

28. Structure

29. Semantics 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. 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. Typically, the model lies in the mind of the human. We have the semantics of (some part of) the world in our minds. It is very structured and interpreted. Typically, the model lies in the mind of the human. We have the semantics of (some part of) the world in our minds. It is very structured and interpreted. When we view a textual document, we see symbols on a page and interpret those with respect to what they mean in our mental model; that is, we supply the semantics (meaning). When we view a textual document, we see symbols on a page and interpret those with respect to what they mean in our mental model; that is, we supply the semantics (meaning). If we wish to assist in the dissemination of the knowledge embedded in a document, we make that document available to other human beings, expecting that they will provide their own semantic interpreter (their mental models) and will make sense out of the symbols on the document pages. If we wish to assist in the dissemination of the knowledge embedded in a document, we make that document available to other human beings, expecting that they will provide their own semantic interpreter (their mental models) and will make sense out of the symbols on the document pages. So, there is no knowledge in that document without someone or something interpreting the semantics of that document. So, there is no knowledge in that document without someone or something interpreting the semantics of that document. Semantic interpretation makes knowledge out of otherwise meaningless symbols on a page. Semantic interpretation makes knowledge out of otherwise meaningless symbols on a page.

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

31. How are the other Model Types ? In formal language theory, one has a syntax and a semantics for the objects of that syntax (vocabulary). E.g. the syntax of programming languages and database structure. In formal language theory, one has a syntax and a semantics for the objects of that syntax (vocabulary). E.g. the syntax of programming languages and database structure. Ontologies try to limit the possible formal models of interpretation (semantics) of those vocabularies to the set of meanings you intend. Ontologies try to limit the possible formal models of interpretation (semantics) of those vocabularies to the set of meanings you intend. None of the other model types with limited semantics --- taxonomies, database schemas, thesauri, and so on --- does that. None of the other model types with limited semantics --- taxonomies, database schemas, thesauri, and so on --- does that. These model types assume that humans will look at the “vocabularies” and magically supply the semantic via the built-in human semantic interpreter: your mind using your mental models. These model types assume that humans will look at the “vocabularies” and magically supply the semantic via the built-in human semantic interpreter: your mind using your mental models.

32. Ontologies want to shift some of that “semantic interpretative burden” to machines and have them eventually mimic our semantics --- that is, understand what we mean --- and so bring the machine up to the human, not force the human to the machine level. Ontologies want to shift some of that “semantic interpretative burden” to machines and have them eventually mimic our semantics --- that is, understand what we mean --- and so bring the machine up to the human, not force the human to the machine level. That is why, for example, we are not still programming in assembler. Software engineering and computer science has evolved higher-level languages that are much more aligned with the human semantic/conceptual level. Ontologies want to push it even farther. That is why, for example, we are not still programming in assembler. Software engineering and computer science has evolved higher-level languages that are much more aligned with the human semantic/conceptual level. Ontologies want to push it even farther.

33. Machine Semantic Interpretation We mean that by structuring (and constraining) in a logical, axiomatic language (i.e., a knowledge representation language, which we discuss shortly) the symbols humans supply, the machine will conclude via an inference process (again, built by the human according to logical principles) roughly what a human would in comparable circumstances. We mean that by structuring (and constraining) in a logical, axiomatic language (i.e., a knowledge representation language, which we discuss shortly) the symbols humans supply, the machine will conclude via an inference process (again, built by the human according to logical principles) roughly what a human would in comparable circumstances.

34. Given a formal vocabulary – alphabet, terms/symbols (logical and non-logical), and statements/expressions (and, of courses, rules by which to form expressions from terms) --- one wants the formal set of interpretation models correlated with the symbols and expressions (i.e., the semantics) to approximate those models that a human would identify as those he or she intended. Given a formal vocabulary – alphabet, terms/symbols (logical and non-logical), and statements/expressions (and, of courses, rules by which to form expressions from terms) --- one wants the formal set of interpretation models correlated with the symbols and expressions (i.e., the semantics) to approximate those models that a human would identify as those he or she intended.

35. Mapping Between Syntax and Semantics The syntax is addressed by proof theory The syntax is addressed by proof theory The semantics is addressed by model theory The semantics is addressed by model theory Symbols  Rules Symbols  Rules Syntax Simple Semantics Complex Semantics More Complex Semantics ------------------------------------------------------------------------------------------------------------------------ zDLKIL String Constant {“zDLKFL”  {“a”, “b”, “c”, …, Infinite “*S*”} 12323 Integer Constant {12323}  {1, 2, …, n} X Variable X | X  Universe of Discourse 4+3 Addition( Integer Type Constant, Integer, Type Constant) Not (X Or Y) Negation Boolean Type (Boolean Type Variable Inclusive Or Boolean Type Variable) An alphabet and its construction rules for forming words in the alphabet Is mapped to formal objects in the semantic model

36. A specific example of the mapping between the syntax and semantics of a Programming Language Syntactic objects are associated with their semantic interpretations, each of which specifies a formal set-theoretic domain and a mapping function that maps atomic and complex syntactic objects to semantic elements of the formal domain. Syntactic objects are associated with their semantic interpretations, each of which specifies a formal set-theoretic domain and a mapping function that maps atomic and complex syntactic objects to semantic elements of the formal domain.

37. Machine Semantics vs. Semantic Web The machine semantics is very primitive, simple, and inexpressive with respect to the complex, rich semantics of humans, but it’s a start and very useful for our information systems. The machine semantics is very primitive, simple, and inexpressive with respect to the complex, rich semantics of humans, but it’s a start and very useful for our information systems. The machine is not “aware” and cannot reflect. The machine is not “aware” and cannot reflect. It is a formal process of semantic interpretation that we have described --- everything is still bits. It is a formal process of semantic interpretation that we have described --- everything is still bits. But by designing a logical knowledge representation system (a language that we then implement) and ontologies (expressions in the KR language that are what humans want to model about our world, its entities, and the relationships among these entities), and getting the machine to infer (could be deduce, induce, adduce, and many other kinds of reasoning) conclusions that are extremely close to what humans would in comparable circumstance (assertions, facts, and so on), we will have imbued our systems with much more human-level semantic responses than they have at present. We will have a functioning Semantic Web. But by designing a logical knowledge representation system (a language that we then implement) and ontologies (expressions in the KR language that are what humans want to model about our world, its entities, and the relationships among these entities), and getting the machine to infer (could be deduce, induce, adduce, and many other kinds of reasoning) conclusions that are extremely close to what humans would in comparable circumstance (assertions, facts, and so on), we will have imbued our systems with much more human-level semantic responses than they have at present. We will have a functioning Semantic Web.

38. Pragmatics --- sits above semantics and has to do with the intent of the semantics and actual semantic usage. --- sits above semantics and has to do with the intent of the semantics and actual semantic usage. Become important in the Semantic Web, once intelligent agents begin to use the ontologies. Become important in the Semantic Web, once intelligent agents begin to use the ontologies. Intelligent agents will have to deal with the pragmatics of ontologies Intelligent agents will have to deal with the pragmatics of ontologies For example, some agent frameworks, such as that of the Foundation of 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. For example, some agent frameworks, such as that of the Foundation of 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. Intelligent agents are sometimes formalized in a framework called BDI, for Belief, Desire, and Intent. Intelligent agents are sometimes formalized in a framework called BDI, for Belief, Desire, and Intent.