1 TDT4215 Web Intelligence TDT4215 Web Intelligence Main topics: • Introduction to ontologies • Introduction to ontologies • Ontology applications classification framework Presenter: Presenter: • Stein L. Tomassen TDT4215 - Ontologies TDT4215 - Ontologies 2 Outline Outline • Ontology ABC – Terminology – Motivation • Semantic web • RDF and RDFS – Brief introduction to state-of-the-art ontology languages (except OWL -> next lesson). • Classification of ontology applications • Classification of ontology applications TDT4215 - Ontologies
3 Introduction to ontologies TDT4215 - Ontologies 4 Communication between people Communication between people TDT4215 - Ontologies
5 Ontology ABC Ontology ABC • Ontology attracts attentions across many fields in computer science recently. • • The term ontology originates from philosophy and its current usage in The term ontology originates from philosophy and its current usage in computer science (first introduced in AI) is far from its philosophical origin. • • There exists no consensus definition about ontology (some definitions There exists no consensus definition about ontology (some definitions next). • In many cases, the term ontology is another name denoting the result of familiar activities like conceptual analysis and domain modeling of familiar activities like conceptual analysis and domain modeling. • The roles of ontology vary from knowledge management to semantic interoperability. • One important reason for that ontology attracts so much attention recently is the Semantic Web, since ontology is considered the key enabler of Semantic Web. TDT4215 - Ontologies 6 Some definitions Some definitions • “That department of the science of metaphysics which investigates and explains ontology as the nature and essential properties and relations of all beings, as such, or the principles and causes of being” • “Ontology is an abstract model which represents a “O t l i b t t d l hi h t common and shared understanding of a domain” • “ An ontology is a [formal explicit] specification of a “ An ontology is a [formal explicit] specification of a [shared] conceptualization ” (one of the most cited) • “Computer ontologies are formally specified models of • Computer ontologies are formally specified models of known knowledge in a given domain” Ref: (Akerkar, 2011) ( ) TDT4215 - Ontologies
7 What are ontologies? What are ontologies? Studer(98): Formal, explicit specification of a shared conceptualization F l li i ifi i f h d li i Machine Consensual readable readable knowledge knowledge Concepts, properties, i Abstract model of b d l f functions, axioms some phenomena are explicitly defined are explicitly defined in the world in the world TDT4215 - Ontologies 8 More terminology More terminology • Ontology: (engineering artefact) – Constituted by a vocabulary (concepts, relations) – Assumptions about intended meaning • Formalization: – Logical theory accounting for the intended meaning of a formal Logical theory accounting for the intended meaning of a formal vocabulary – Committed to a particular conceptualization of the world • Ontology vs. conceptualization – Conceptualization is language-independent – Ontology is language-dependent Ontology is language dependent TDT4215 - Ontologies
9 Ex#1: SUMO Ex#1: SUMO • Suggested Upper Merged Ontology (SUMO) Ref: http://www.ontologyportal.org TDT4215 - Ontologies 10 Ex#2: OpenCyc Ex#2: OpenCyc Know ledge g Upper Ontology: Abstract Concepts Upper Ontology: Abstract Concepts Base Upper Layers Ontology Core Theories: Space, Time, Causality, … Core Theories: Space, Time, Causality, … Core Core Theories Domain-Specific Theories Domain-Specific Theories Theories Facts Facts: Instances (Database) Ref: http://www.opencyc.org TDT4215 - Ontologies
11 Ex#3: Hierarchical Categories? Ex#3: Hierarchical Categories? • Can hierarchical categories be ontologies? Conceptualization of medical domain? domain? TDT4215 - Ontologies 12 More confusion? More confusion? Differences and similarities Thesaurus O Ontology l Meta- Taxonomy y model d l TDT4215 - Ontologies
13 Ontology spectrum Ontology spectrum Strong semantics First order logic Local Domain Theory Is disjoint subclass Description logic of with transitive OWL property UML UML Conceptual Model RDF/S Is subclass of Extended ER Thesaurus Has narrower meaning than ER Schema Taxonomy Is subclassification of W Weak semantics k ti Ref: ”Ontologies Come of Age”, McGuinness, 2003 TDT4215 - Ontologies 14 Ontological levels Ontological levels E.g. units, everyday concepts Most central (a few hundred) concepts in domain. Known to most domain experts Detailed specific concepts not fully understood by all domain experts Low level concepts are more difficult and expensive to formalize TDT4215 - Ontologies
15 Ontology languages Ontology languages OWL & OWL 2 OWL & OWL 2 TDT4215 - Ontologies 16 Ontology Languages • Wide variety of languages for “explicit specification” – Graphical notations • Semantic networks TDT4215 - Ontologies
17 Ontology Languages • Wide variety of languages for “explicit specification” – Graphical notations • Topic Maps TDT4215 - Ontologies 18 Ontology Languages • Wide variety of languages for “explicit specification” – Graphical notations • UML TDT4215 - Ontologies
19 Ontology Languages • Wide variety of languages for “explicit specification” – Graphical notations • RDF TDT4215 - Ontologies 20 Ontology Languages • Wide variety of languages for “explicit specification” – Logic based • Description Logics (e.g., OIL, DAML+OIL, OWL) • Rule (e.g. SWRL, RuleML, Prolog) • First Oder Logic (e.g., KIF) TDT4215 - Ontologies
21 Ontology Languages • Wide variety of languages for “explicit specification” – Logics based • Conceptual graphs TDT4215 - Ontologies 22 Ontology Languages Ontology Languages • Degree of formality varies widely – Increased formality makes languages more amenable to machine processing (e.g., automated reasoning) i ( t t d i ) TDT4215 - Ontologies
23 The Semantic Web The Semantic Web • Goal: evolve the Web – From sites designed for human consumption – To sites also understandable and usable by computer programs. “The Semantic Web is an extension of “The Semantic Web is an extension of the current web in which information is the current web in which information is the current web in which information is the current web in which information is given well-defined meaning, better given well-defined meaning, better enabling computers and people to enabling computers and people to work in cooperation.” work in cooperation.” • What would that do for us? – Query answering rather than document retrieval – Services findable, usable, and composable by automated agents – Information exchange among independently designed programs TDT4215 - Ontologies 24 The Semantic Web The Semantic Web • How do we get there from here? – For services • Service description • Ontologies to provide intended meaning of service item. – For documents • Structure, ala XML • Ontologies to provide intended meaning of terms TDT4215 - Ontologies
25 Semantic Web “Layered Cake” Semantic Web Layered Cake Ref: http://www.w3.org/2007/Talks/0130-sb-W3CTechSemWeb/#%2824%29 TDT4215 - Ontologies 26 Data integration Data integration Ref: http://www.w3.org/People/Ivan/CorePresentations/SW_QA/Slides.html#%2810%29 TDT4215 - Ontologies
27 XML XML • XML describes document structure • “XML is like HTML, where you make up your own tags.” • HTML – Language for describing how to display document content • E.g., tag a word to be displayed in bold or italic • XML – Language for describing the structure of document content • E.g., declare data to be a retail price, a sales tax, a book title, ... g , p , , , – XML allows authors to create their own markup (e.g. <AUTHOR>), which seems to carry some semantics. However, from a computational perspective tags like <AUTHOR> carries as much semantics as a tag like <H1>. A computer simply does not know, what an author is and how the H1 A t i l d t k h t th i d h th concept author is related to e.g. a concept person. TDT4215 - Ontologies 28 Bibliographic entry in XML Bibliographic entry in XML <Publication URL = "ftp://db.stanford … xml.ps”> <Title> From Semi-structured Data ... Language </Title> <Author> R Goldman </Author> <Author> R. Goldman </Author> <Published> Proceedings of ... Databases </Published> Location of what? Location of what? f <Location> <City> Philadelphia </City> <State> Pennsylvania </State> </Location> </Location> <Date> When in June? When in June? <Month> June </Month> <Year> 1999 </Year> </Date> </Publication> TDT4215 - Ontologies
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