Knowledge Representation and Semantic Web

Technologies for extended minds

Knowledge Representation Aspects

• How do we represent what we know?– Expressiveness can conflict with computability

• What aspects of what we know and their relationships are important?– Every KR is an explicit answer to this question– Every KR is a fragmented of full reasoning

• The subset useful to the problem at hand in tractable limits

– The choice of KR limits• What can be captured/expressed• What sorts of questions may be tractably answered• Usefulness for human exploration and learning• Usefulness for computational exploration and learning

KR Desired Properties

• Coverage– Sufficient breath and depth

• Understandable by humans– If for human use anyway. Useful for debugging in any case

• Consistency• Efficient• Easy of modification• Supports the applications / functions the KR was

desired for

Historical Attempts

• 70s and early 80s• Heuristic question-answering, neural networks, theorem

proving, expert systems. (Mycin)• Cyc starting is late 80s.

– Naïve physics, time notions, causality, motivation, common objects and classes of objects

• 90s to now• Computational linquistics• KR Programming languages• SGML -> HTML -> XML• Semantic Web

Uniting Information Sources

Semantic Web

• KR of web content– Machine readable web content or description of content– Integration across different content, applications,

systems• Enterprise Information Systems

– Semantic publishing• Documents with semantic markup

– RDF is most used currently

– Two Approaches• Information as data objects using semantic language (RDF,

OWL)• Embed formal metadata within documents with new markup

– RDFa, Microformats

Some ontologies and vocabularies• Dublin Core

– Resources, materials, media, text, web pages• SKOS

– Thesauri, taxonomies, classification schemes• FOAF

– Friend of a friend. Social network ontology• SIOC

– Interconnection of discussions, blogs, forums, mailing lists• RSS

– Syndication. Updates of blogs, news headlines, audio, video• DOAP

– Description of a project. 43000 OS projects in Freshmeat• SPE

– Scientific publishing experiment

Open Source Tools and Services• Ambra Project

– Publish open access journal with RDF. • Semantic MediaWiki

– Mediawiki extension for semantic annotation and RDF publishing• Swoogle

– Search engine for ontologies and instance data a• Ufeed

– Publishes RDF resources and feeds• D2R Server

– Publishes relational database on the web als Linked Data and SPARQL endpoints• BigBlogZoo

– Crawls and reaggregates 60000 XML sources under semantic URLs• Utopia

– Interactive documents

Resource Description Framework• RDF basics

– Subject predicate object• Typically all three are URIs to keep identity clear• Graphed as subject node, object node, predicate as labeled directed edge

– Basically a lightweight binary relationship– Note similarity to Prolog entries

– Structured information broken in two set of RDF triplets– Nodes, at least objects, can be containers of URIs

• Containers are unbound bags• Collections are closed / complete

• RDF Schema (RDFS)– Defines types and classes of URIs and expected associations or information

about types.• IS-A and HAS-A relationships• Meaning details for types• Properties of classes

Web Ontology Language (OWL)

• Components• Classes• Instances• Properties• Datatype properties• Object properties• operators

Topic Maps• Components

– Topics– Associations– Occurrences

• Similar to concept maps and mind maps• Higher level of semantic abstraction than OWL and RDFS• Fully supports merging of topic maps• APIs

– TMAPI • Query

– TMQL• Constraint specification (unfinished)

– TMCL