Semantics Overview Sharon L. Bolding, PhD Jan 16, 2007

1. Semantics OverviewSharon L. Bolding, PhD Jan 16, 2007

2. Historic context • Cross-disciplinary field: philosophy, linguistics, computer sciences, cognitive sciences • Goal is to represent meaning of knowledge unambiguously so that it can be understood, shared and used by computational agents • Computational focus emerged in the 1980s AI community work in expert systems and formal semantics, such as Situational Theory

3. Historic context cont. • Philosophy: Socrates questioning, Plato’s study of epistemology, the nature of being • Aristotle shifted the debate to terminology, development of logic as a precise method for reasoning about knowledge • Middle Ages: Anselm of Canterbury and the existence of God, theories of reference and mental language, Scholastic logic • Semantic Network: First used by Porphyry in the 3rd century to represent Aristotle’s hierarchy of species

4. Supreme genus Substance Differentiae Immaterial Material Body Spirit Subordinate genera Differentiae Inanimate Animate Subordinate genera Living Mineral Differentiae Sensitive Insensitive Animal Plant Subordinate genera Differentiae Rational Irrational Human Beast Species Individuals Socrates Plato Aristotle Etc. Semantics—Classic Taxonomy Tree, Porphyry

5. A more modern view McGuinness Berners-Lee

6. Definitions • An ontology is a specification of a conceptualization— Tom Gruber • Knowledge engineering is the application of logic and ontology to the task of building computational models of some domain for some purpose — John Sowa • Knowledge representation means that knowledge is formalized in a symbolic form, that is, to find a symbolic expression that can be interpreted —Klein and Methlie • The task of classifying all the words of language, or what’s the same thing, all the ideas that seek expression, is the most stupendous of logical tasks. Anybody but the most accomplished logician must break down in it utterly; and even for the strongest man, it is the severest possible tax on the logical equipment and faculty — Charles Sanders Peirce • A data model describes data, or database schemas - an ontology describes the world — Adam Farquhar, Stanford

7. Semantics—Ontologies Heart Human Hair Color attribute part Black part attribute Blond part attribute Brown … and attributes. Ontologies have relationships…

8. Ontology based technologies • Search • Artificial Intelligence • Natural Language Processing (NLP) • Semantic Web • Speech generation • Automatic translation systems • Profiling & finding people

9. Keyword Search ≠ Semantic Search Keywords = Words, not context Semantic Search = Concepts + Context Semantics in Technology Taxonomy - A structure of known human knowledge for a specific domain, organized into categories and subcategories Ontology - Defines meanings and relationships for each category Example: Semantics and Search

10. Servers: Semantic Analysis of Sources Multiple ontologies used to semantically analyze and rank content into an index Users: Categories as Search Criteria Build queries using categories from multiple ontologies How search technology uses ontologies

11. Ontologies cont. • Add 1 Category to a query • 1 Category + 8 definitions • = 9 keyword searches at once! • Cross-fertilization • Ask about concepts, get relevant answers

12. Search using ontologies Index When the entire body of documents is mapped, it forms the index graph

13. Search using ontologies Compare The document to index graph

14. Search using ontologies Retrieve Documents by signature

15. Ontology in XML

16. Kinds of ontologies • An upper ontology defines base concepts supporting ontology development (SUMO) • A domain or classic ontology defines the terminology and concepts relevant to a particular topic or area of interest • A process ontology defines the inputs, outputs, constraints, relations, terms and sequencing information relevant to business processes (ISO PSL Process Specification Language) • A service ontology defines a core set of constructs for describing vocabularies and capabilities of services (OWL-S)

17. Example: Semantic Web • The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation—Time Berners-Lee

18. Benefits • Creates an “open world” scenario, where communications are enabled at a computational level • Because they are XML-based, ontologies can assist businesses in leveraging existing investments in markup, content and metadata • Creates policy-based applications for compliance • Supports less certainty creating informed answers, predictive analytics, etc... rather than binary absolutes • Reuse of existing knowledge, write once, revise

19. Example: Call Center Support Application • Text mining is the key idea powering the application • Background: Call center supports hardware business, service customers need tech support, product managers provide information into a KB system, phone support staff add real time information to the KB via phone records

20. Call Center cont. • Documentation comes from multiple sources, customers may have needs that have never been documented • Challenges: No feedback loop for product improvements, increased employee & service costs, increased customer dissatisfaction, employees demoralized • Solutions: Identify conflicting documents, show product relationships, impact of one issue on multiple components & features, traceability for compliance

21. How to create an ontology • Prerequisite: Learn XML. • Define domain terms and relationships • Concepts (classes, nouns) • Identify subclass and superclass relationships (verbs) • Identify attributes and properties (adj, adv) including exclusions • Identify any general properties, relations, functions • Restrict slot values (how terms may be entered) • Define individuals • Define interrelationships between individuals (fill in the slots) • Iterate to improve over time

22. Classes • A concept in the domain • A collection of elements with similar properties • Contains necessary conditions for membership • A node is a particular instance of a class • Has inheritance: True subclass relationships are the basis of formal “is-a” hierarchies, where the instance of the subclass is an instance of the superclass

23. Class hierarchy levels • Different modes of development: • Top down: general to specific • Bottom up: specifics organized in to general buckets • Combo: breadth at the top level, then depth at a few branches to test the design • Class inheritance is transitive: • A is a subclass of B • B is a subclass of C • Therefore A is a subclass of C • (See McGuinness and Noy paper in syllabus)

24. Example: Mercury • Is it a planet, a car, an element, or a god? • Car, then exclude god, planet, element • Car, then has physical and spatial attributes • Car, then has value and utility • Question: does it have psychological attributes (the kind of car I drive, says what?) Do I care?

25. Ontologies • Questions?