Lecture 1 Agenda • Course introduc:on: what is an ontology? • Administra:on • RDF/RDFS
Literature • James Odell, Ontology White Paper, CSC Catalyst, 2011, V2011-‐07-‐15, hNp://www.jamesodell.com/ Ontology_White_Paper_2011-‐07-‐15.pdf. • For this lecture Sec.s 1-‐4 are relevant • Acknowledgement: some ﬁgures in this lecture come from the paper above.
What is an Ontology? • In philosophy: theory of what exists in the world • In IT: consensual & formal descrip:on of shared concepts in a domain • Aid to human communica:on and shared understanding, by specifying meaning • Machine-‐processable (e.g., agents use ontologies in communica:on) • Key technology in seman:c informa:on processing • Applica:ons: knowledge management, e-‐business, seman:c world-‐wide web.
What is an Ontology? II “explicit speciﬁca-on of a shared conceptualiza-on that holds in a par-cular context” (several authors)
Knowledge sharing and reuse • Knowledge engineering is costly and :me-‐ consuming • Distributed systems • Increasing need for deﬁni:on of a common frame of reference – Internet search, document indexing, ….
Domain standards and vocabularies as ontologies • Contain ontological informa:on • Ontology needs to be “extracted” – Not explicit • Lists of domain terms are some:mes also called “ontologies” – Implies a weaker no:on of ontology – Scope typically much broader than a speciﬁc applica:on domain – Contain some meta informa:on: hyponyms, synonyms, text • Structured knowledge is available (on the web) – use it! 14
Context and Domain Principle 1: “ The representa:on of real-‐world objects always depends on the context in which the object is used. This context can be seen as a “viewpoint” taken on the object. It is usually impossible to enumerate in advance all the possible useful viewpoints on (a class of ) objects.” Principle 2: “Reuse of some piece of informa:on requires an explicit descrip:on of the viewpoints that are inherently present in the informa:on. Otherwise, there is no way of knowing whether, and why this piece of informa:on is applicable in a new applica:on seing.”
Mul:ple views on a domain • typical viewpoints captured in ontologies: • func:on • behavior, • causality • shape, geometry • structure: part-‐of (mereology), aggrega:on • connectedness (topology) • viewpoints can have diﬀerent abstrac:on (generaliza:on) levels • viewpoints can overlap • applica:ons require combina:ons of viewpoints 19
The concept triad Source: http://www.jamesodell.com/Ontology_White_Paper_2011-07-15.pdf.
Concept speciﬁca:on • Symbol – Name used for the concept – Can be diﬀerent names, diﬀerent languages – E.g., “bike”, ﬁets” • Intension (deﬁni:on) – Intended meaning of the concept (seman:cs) – E.g. a bike has at least one wheel and a human-‐ powered movement mechanism • Extension – Set of examples of the concept – E.g. “my bike”, “your bike”
Incomplete concept speciﬁca:ons • Are common • Think of an example: – Concept with no instances – Concept with no symbol • Primi:ve vs. deﬁned concepts
Domain = area of interest • Can be any size – e.g., medicine • Concepts may have diﬀerent symbols in diﬀerent domains • The same symbol may be used for diﬀerent concepts in diﬀerent domains (some:mes also in the same domain)
Ontology Speciﬁca:on • Class (concept) • Aggrega:on • Subclass with inheritance • Rela:on-‐aNribute dis:nc:on • Trea:ng rela:ons as classes • Rela:on (slot) • Sloppy class/instance dis:nc:on – Class-‐level aNributes/ rela:ons – Meta classes • Constraints • Data types • Modularity – Import/export of an ontology • Ontology mapping
Ontology Languages – UML – RDF Schema, OWL – ….. • Common basis – Class (concept) – Subclass with inheritance – Rela:on (slot) 33
Ontology Tools Best known tool • Protégé (Stanford) • We will use this tool Decision points: – Expressivity – Graphical representa:on – DB backend – Modulariza:on support – Versioning
Administra:on • Course website: hNp://seman:cweb.cs.vu.nl/OE2012/ • Use blog posts for content ques:ons • Use oe-‐email@example.com for admin ques:ons
Engineering needs prac:ce! Lots of exercises throughout the course: • Two mee:ngs per week • Lectures on Monday • Work sessions on Thursday • You are encouraged to do assignments together with colleagues • Individual porsolio
RDF(S) Recap • Which RDF/RDF-‐Schema constructs do you remember?
URIs, URLs • URI: global iden:ﬁer for a web resource • hNp://www.w3.org/2006/03/wn/wn20/instances/synset-‐ anniversary-‐noun-‐1 • URL: dereferencable URI, used to locate a ﬁle on the web. • hNp://www.w3.org/2006/03/wn/wn20/instances/synset-‐ anniversary-‐noun-‐1 • URI abbrevia:ons: – Qnames • Namespace:iden:ﬁer • Wordnet:synset-‐anniversary-‐noun-‐1
Blank nodes How would you model “Sonnet78 was inspired by a woman who lives in England”? Lit:Sonnet78 lit:hasInspiration [ rdf:type bio:Woman; bio:livedIn geo:England ] .
subClassOf IFA rdfs:subClassOf Br rdf:type ATHENr rdf:type B
subPropertyOf IFP rdfs:subPropertyOf Ra P bTHENa R b
Domain and Range IF IFP rdfs:domain D P rdfs:range Rx P y x P yTHEN THENx rdf:type D y rdf:type R
More RDF(S) • rdfs:label • rdfs:comment • rdfs:seeAlso
RDF-‐Schema • Provides a way to talk about the vocabulary – Deﬁne classes, proper:es bb:author rdf:type rdfs:Property• Enables inferencing – Inferring new triples from asserted triples. • subClassOf, subPropertyOf, domain, range.
Guidelines for ontological engineering (1) • Do not develop from scratch • Use exis:ng data models and domain standards as star:ng point • Start with construc:ng an ontology of common concepts • If many data models, start with two typical ones • Make the purpose and context of the ontology explicit – E.g. data exchange between ship designers and assessors – Opera:onally purpose/context with use cases • Use mul:ple hierarchies to express diﬀerent viewpoints on classes • Consider trea:ng central rela:onships as classes 47
Guidelines for ontological engineering (2) • Do not confuse terms and concepts • Small ontologies are ﬁne, as long as they meet their goal • Don’t be overly ambi:ous: complete uniﬁed models are diﬃcult • Ontologies represent sta:c aspects of a domain – Do not include work ﬂow • Use a standard representa:on format, preferably with a possibility for graphical representa:on • Decide about the abstrac:on level of the ontology early on in the process. – E.g., ontology only as meta model 48