14 Social Systems 14.1 Generating ontologies 14.2 Wisdom of the - PDF document

07.07.2009 14 Social Systems 14.1 Generating ontologies 14.2 Wisdom of the crowds 14.3 Folksonomies Knowledge-Based Systems and Deductive Databases Wolf-Tilo Balke Christoph Lofi Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 2 14.0 Semantic Web Reasoning 14.0 Semantic Web Reasoning • Last week we saw ontologies as a powerful • OWL is the language (and semantics) of choice instrument for… for the ontology part – Representing knowledge – But OWL DL has a somewhat different semantics from RDF/S – And reason about it! – And OWL Full is compatible with RDF/S, but • Ontologies, rules and logics form computationally difficult… the middle layer of the proposed • Extensions to first order logic (FOL) or other Semantic Web stack extensions, such as simple common logic (SCL) – Formal syntax are even more difficult – Formal semantics 3 4 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 14.0 Semantic Web Reasoning 14.0 Semantic Web Reasoning • Thus, the stack does not really consists of a set of • While RDF/S (or at least the DLP bits) form a valid foundation for OWL, Datalog-style rule languages languages building directly and completely on the lower languages (RDF/S  OWL  logic) need other assumptions – Closed world semantics – Also a subsequent refinement to the – Leads to full negation as failure (NAF) „DL - program‟ bit of OWL and the Is there an overarching logic framework? split between OWL and rule – … languages did not help much • Whereas DLP is only a subset – RDF triples encode facts , but are of Horn rules also used to encode syntax … – And if it is interpreted with • Complex syntax is clumsy to write Herbrand models and CWA, • Syntax is a true fact..?! it is no longer suitable for OWL… Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 5 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 6 1

07.07.2009 14.0 Semantic Web Reasoning 14.0 Semantic Web Reasoning • Hmmmm … this leads to difficult questions … • In any case ontologies and logics are powerful once you have them, but how do we get the – If you want to join the debate: ontologies..?! • P . Patel-Schneider: A Revised Architecture for SemanticWeb Reasoning . In PPSWR„05, LNCS, Springer, 2005. – Expert create them like in our Datalog expert • I. Horrocks, B. Parsia, P . Patel-Schneider, J. Hendler: Semantic systems? Web Architecture: Stack orTwo Towers? In PPSWR„05, • Do all experts have the same world view? Can we simply LNCS, Springer, 2005. extract their knowledge? – Maybe rules on top of OWL..?! – Create a common backbone and let all individual users build their extensions „as they go‟? • How to keep the ontology consistent? Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 7 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 8 14.0 Semantic Web Reasoning 14.0 Semantic Web Reasoning • Ontologies are extremely powerful and based • So,… do we always need a full-fledged ontology on decidable logics, but… or are there other possibilities..?! – Let one little hobbit (read: – Depends on the area: a medical domain ontology inconsistency) in and the should be sound and consistent!!! entire thing comes crashing down… – But some ontology for document management or organizing your holiday photos..?! 9 10 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 14.1 The MeSH Ontology 14.1 The MeSH Ontology • Medical Subject Heading – Currently, MeSH contains around 25,000 subjects ( descriptors ) – Controlled vocabulary for indexing journal articles and books in life sciences • Accompanied by brief definition and a synonym list • Taxonomy • Descriptors are arranged in a hierarchy and may occur • Thesaurus multiple times in different branches – Maintained by the US National Library of – Entries in the tree hierarchies are uniquely identified Medicine (NLM) by an alpha-numerical ID system • Used to classify the MEDLINE/PubMed collections Top level concepts of caries • Free for use and download Caries types – Proprietary XML or text format – HTML web view – MeSH is hand-crafted by medical experts Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 11 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 12 2

07.07.2009 14.1 The MeSH Ontology 14.1 The MeSH Ontology Descriptor/heading (concept) Tree ID • Qualifiers encode commonly used tags – Can be added to all other headings – e.g. viral, microbiol, epidemic, etc Definition Synonyms Qualifier shortcut Qualifiers (Common Tags) Related concepts http://www.nlm.nih.gov/cgi/mesh/2009/MB_cgi?mode=&index=3573&field=all&HM=&II=&PA=&form=&input= http://www.nlm.nih.gov/cgi/mesh/2009/MB_cgi?mode=&term=MI&field=qual Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 13 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 14 14.1 The MeSH Ontology 14.1 The MeSH Ontology • By using MeSH, concept maps can be visualized – Help to quickly assess a given topic Visual dictionary uses co-occurrence of concepts In publications as weight indicator Typed links between concepts allow for “browsing” http://www.curehunter.com/public/dictionary.do 15 16 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 14.1 The MeSH Ontology 14.1 Hierarchical Expert Ontologies • Also, can be become easily very large and • MeSH is an example for enriched taxonomy complex manually modeled by domain experts – Expert taxonomies are widely used, however, they come with problems • Inflexible and rigid structure representing just the authors view and knowledge • Hard to change once established, expensive to maintain • Hierarchical classification often not very practical Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 17 Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke & Christoph Lofi – IfIS – TU Braunschweig 18 3