training06 sure stsw 01

A Short Semantic Web Tutorial: 

A Short Semantic Web Tutorial June 17th, 2003 Andreas Hotho & York Sure Knowledge Management Group Institute AIFB University of Karlsruhe © York Sure and Andreas Hotho, 2003

Karlsruhe: Location for Semantic Technologies: 

Karlsruhe: Location for Semantic Technologies

KAON: 

KAON KAON stands for Karlsruhe Ontology and Semantic Web Framework. Open Source platform for ontology-related tools, including Ontology Modeling tools, including ontology learning Scalable Ontology Server, including API, inference engine and query language. Open source under LGPL, available at: http://kaon.semanticweb.org

Slide4: 

Agenda Semantic Web & Ontology Ontology Engineering supported by Learning Mining the Semantic Web Our Vision

Semantic Web: 

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 co-operation.“ [Berners-Lee et al., 2001]

Machine accessible meaning (What it’s like to be a machine) : 

Machine accessible meaning (What it’s like to be a machine)

Semantic Web Layers(T. Berners-Lee et al.): 

Semantic Web Layers (T. Berners-Lee et al.)

XML:: 

XML: User definable and domain specific markup <H1>Knowledge Management</H1> <UL> <LI>Teacher: Rudi Studer <LI>Students: Master </UL> HTML: <course> <title>Knowledge Management</title> <teacher>Rudi Studer</teacher> <students>Master</students> </course> XML:

XML: Document = labelled tree: 

XML: Document = labelled tree DTD: simple grammars to describe legal trees So: why not use XML to represent ontologies? node = label + attr/values + contents

XML: limitations for semantic markup: 

XML: limitations for semantic markup XML makes no commitment on: Domain specific ontological vocabulary Ontological modelling primitives  requires pre-arranged agreement on  &  Only feasible for closed collaboration agents in a small & stable community pages on a small & stable intranet .. not for sharable Web-resources

XML  machine accessible meaning: 

XML  machine accessible meaning

The semantic pyramid again: 

The semantic pyramid again

RDF for semantic annotation: 

RDF for semantic annotation RDF provides metadata about Web resources Object -> Attribute-> Value triples It has an XML syntax Chained triples form a graph

What does RDF Schema add?: 

What does RDF Schema add? Defines vocabulary for RDF Organizes this vocabulary in a typed hierarchy Class, subClassOf, type Property, subPropertyOf domain, range Rudi York hasSuperVisor

RDF Schema syntax in XML: 

<rdf:Description ID="MotorVehicle"> <rdf:type resource="http://www.w3.org/...#Class"/> <rdfs:subClassOf rdf:resource="http://www.w3.org/...#Resource"/> </rdf:Description> <rdf:Description ID="Truck"> <rdf:type resource="http://www.w3.org/...#Class"/> <rdfs:subClassOf rdf:resource="#MotorVehicle"/> </rdf:Description> <rdf:Description ID="registeredTo"> <rdf:type resource="http://www.w3.org/...#Property"/> <rdfs:domain rdf:resource="#MotorVehicle"/> <rdfs:range rdf:resource="#Person"/> </rdf:Description> <rdf:Description ID=”ownedBy"> <rdf:type resource="http://www.w3.org/...#Property"/> <rdfs:subPropertyOf rdf:resource="#registeredTo"/> </rdf:Description> RDF Schema syntax in XML

Conclusions about RDF(S): 

Conclusions about RDF(S) Next step up from plain XML: (small) ontological commitment to modeling primitives possible to define vocabulary However: no precisely described meaning no inference model

Last but not least ...: 

Last but not least ... RFC Work in Progress Further Activities: Semantic Web Service Committee Query Language

Ontology: 

Ontology Ontologies enable a better communication between Humans/Machines Ontologies standardize and formalize the meaning of words through concepts „An ontology is an explicit specification of a conceptualization.“ [Gruber, 1993] „People can‘t share knowledge if they do not speak a common language.“ [Davenport & Prusak, 1998]

Communication Principle: 

^ Communication Principle Referent Form Stands for refers to evokes Concept “Jaguar“ [Odwen, Richards, 1923]

Views on Ontologies: 

Views on Ontologies Front-End Back-End Ontologies Navigation Queries Sharing of Knowledge Information Retrieval Query Expansion Mediation Reasoning Consistency Checking EAI

Slide21: 

Taxonomy Object Person Topic Document Researcher Student Semantics Ontology Doctoral Student Taxonomy := Segementation, classification and ordering of elements into a classification system according to their relationships between each other PhD Student F-Logic Menu

Slide22: 

Thesaurus Object Person Topic Document Researcher Student Semantics PhD Student Doktoral Student Terminology for specific domain Graph with primitives, 2 fixed relationships (similar, synonym) originate from bibliography similar synonym Ontology F-Logic Menu

Slide23: 

Topic Map Object Person Topic Document Researcher Student Semantics Topics (nodes), relationships and occurences (to documents) ISO-Standard typically for navigation- and visualisation synonym Menu

Slide24: 

Ontology (in our sense) Object Person Topic Document Tel described_in writes Representation Language: Predicate Logic (F-Logic) Standards: RDF(S); coming up standard: OWL Researcher Student instance_of

Ontology & Metadata: 

PhD Student AssProf AcademicStaff rdfs:subClassOf rdfs:subClassOf cooperate_with rdfs:range rdfs:domain Ontology Ontology & Metadata Links have explicit meanings!

Example: OntoWeb.org: 

Example: OntoWeb.org

Slide27: 

Agenda Semantic Web & Ontology Ontology Engineering supported by Learning Mining the Semantic Web Our Vision

OTK Methodology:Knowledge Meta Process: 

Task: Build ontology based KM applications Problems: Collaboration between domain experts and knowledge engineers Evaluation of ontologies OTK Methodology: Knowledge Meta Process Process-oriented, cyclic Pre-defined decisions and outcomes for each step Links to further existing methodologies for substeps [Y. Sure and R. Studer, 2002]

But ...: 

But ... Manual engineering of ontologies is a very time consuming task! (Semi-)automatic support needed to reduce the burden of engineering! ... e.g. with Ontology and Instance Learning!

Slide30: 

Why only semi-automatically? A lot of tacit background knowledge, experiences, social conventions, etc is involved in the modeling process. In order to obtain high quality results, a human has to be in the loop. If this were not the case, the Semantic Web would be superfluous!

Where to start?: 

Where to start? Web Mining Areas Web content mining

Slide32: 

Extracting Semantics from the Web Web Mining can help to learn structures for knowledge organization (e.g. ontologies) and to populate them. Ontology Learning Instance Learning

Ontology Learning: 

Ontology Learning Typically, a domain-specific document corpus contains much information about a specific domain. One possible approach is to take this given corpus and extract linguistic and ontological resources from it.  Concentration to Web Content ONTOLOGY LEARNING Knowledge Discovery Ontology Engineering

Ontology Learning Steps: 

Ontology Learning Steps 1. Concept Extraction Multi-Word-Term Extraction Word Meaning Recognition 2. Concept Relation Extraction: Taxonomy Learning Non-taxonomic relation extraction Labeling of non-taxonomic relations Beside these two steps ontology reuse via pruning is applicable.

Slide35: 

Ontology Learning from the Web [Mädche, Staab: ECAI 2000] is-a hierarchy Example

Slide36: 

Extracting Semantics from the Web Web Mining can help to learn structures for knowledge organization (e.g. ontologies) and to populate them. Ontology Learning Instance Learning

Slide37: 

Instance Learning from the Web Example

Slide38: 

Example Information Highlighting for supporting annotation based on IE techniques.

Crawling the (semantic) web for filling the ontology: 

Crawling: load a document extract links load next document Focused Crawling intelligent focused decision on the next step Crawling the (semantic) web for filling the ontology Example [Ehrig et al, 2002]

Slide40: 

Agenda Semantic Web & Ontology Ontology Engineering supported by Learning Mining the Semantic Web Our Vision

Slide41: 

Mining the Semantic Web Knowledge base Hotel: Wellnesshotel GolfCourse: Seaview belongsTo(Seaview, Wellnesshotel) ... Example

Semantic Web Usage Mining : 

Semantic Web Usage Mining p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:03:51 +0100] "GET /search.html?l=ostsee%20strand&syn=023785&ord=asc HTTP/1.0" 200 1759 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:05:06 +0100] "GET /search.html?l=ostsee%20strand&p=low&syn=023785&ord=desc HTTP/1.0" 200 8450 p3ee24304.dip.t-dialin.net - - [19/Mar/2002:12:06:41 +0100] "GET /mlesen.html?Item=3456&syn=023785 HTTP/1.0" 200 3478 Search by Location Search by Location and Price Refine search Choose item Look at individual Hotel. From logfile analysis ... ... to semantic logfile analysis: Basic idea: associate each requested page with one or more ontological entities, to better understand the process of navigation [Berendt & Spiliopoulou 2000; Berendt 2002; Oberle 2003] Use the gained knowledge to understand search strategies improve navigation design personalization Example

Text Document Clustering of Crawled Documents: 

Text Document Clustering of Crawled Documents WWW Clustering Focused Crawling Example

Slide44: 

Agenda Semantic Web & Ontology Ontology Engineering supported by Learning Mining the Semantic Web Our Vision

Our Vision: 

Our Vision Combination of three core technologies: Ontology and Metadata Technology (OMT) Human Language Technology (HLT) Knowledge Discovery (KD) Transfer into other application areas: Web Services Peer-to-Peer Multimedia No boundaries between: Document Management Content Management Knowledge Retrieval Knowledge Management tasks become an almost effortless part of day to day activities 18 month 7-10 years 3 years

Acknowledgements: 

Acknowledgements We would like to thank all our colleagues which contributed to this tutorial by numerous fruitful discussions. In particular we would like to mention the following ones, who contributed some of the slides: Prof. Dr. Frank van Harmelen, VU Amsterdam (RDF/S Introduction) Mike Ullrich, ontoprise GmbH (Views on Ontologies)

Selected Literature: 

Selected Literature Semantic Web & Ontology Y. Sure and R. Studer. Vision for Semantically-Enabled Knowledge Technologies. Online at: KTweb -- Connecting Knowledge Technologies Communities, 2003. Y. Sure and R. Studer: A Methodology for Ontology-based Knowledge Management. In: On-To-Knowledge: Semantic Web enabled Knowledge Management. J. Davies, D. Fensel, F. van Harmelen (eds.), ISBN: 0-470-84867-7, Wiley, 2002, pages 33-46. Y. Sure, S. Staab and R. Studer. Methodology for Development and Employment of Ontology Based Knowledge Management Applications. In: SIGMOD Record, Vol. 31, No. 4, pp. 18-23, December 2002. S. Staab, H.-P. Schnurr, R. Studer, and Y. Sure: Knowledge Processes and Ontologies. In: IEEE Intelligent Systems 16(1), January/Febrary 2001, Special Issue on Knowledge Management.

Selected Literature: 

Selected Literature Semantic Web & Ontology Y. Sure, S. Staab, J. Angele. OntoEdit: Guiding Ontology Development by Methodology and Inferencing. In: R. Meersman, Z. Tari et al. (eds.). Proceedings of the Confederated International Conferences CoopIS, DOA and ODBASE 2002, October 28th - November 1st, 2002, University of California, Irvine, USA, Springer, LNCS 2519, pages 1205-1222. Y. Sure, M. Erdmann, J. Angele, S. Staab, R. Studer and D. Wenke. OntoEdit: Collaborative Ontology Engineering for the Semantic Web. In: Proceedings of the first International Semantic Web Conference 2002 (ISWC 2002), June 9-12 2002, Sardinia, Italia, Springer, LNCS 2342, pages 221-235. E. Bozsak, M. Ehrig, S. Handschuh, A. Hotho, A. Mädche, B. Motik, D. Oberle, C. Schmitz, S. Staab, L. Stojanovic, N. Stojanovic, R. Studer, G. Stumme, Y. Sure, J. Tane, R. Volz, V. Zacharias. KAON - Towards a large scale Semantic Web. In: Proceedings of EC-Web 2002 (in combination with DEXA2002). Aix-en-Provence, France, September 2-6, 2002. LNCS, Springer, 2002, pages 304-313.

Selected Literature: 

Selected Literature Text Clustering A. Hotho, S. Staab, and G. Stumme. Explaining text clustering results using semantic structures. In Proc. of the 7th PKDD, 2003. B. Lauser and A. Hotho. Automatic multi-label subject indexing in a multilingual environment. In Proc. of the 7th European Conference in Research and Advanced Technology for Digital Libraries, ECDL 2003, 2003. A. Hotho, S. Staab, and G. Stumme. Text clustering based on background knowledge. Technical Report 425, University of Karlsruhe, Institute AIFB, 2003. Hotho, A., Mädche, A., Staab, S.: Ontology-based Text Clustering, Workshop "Text Learning: Beyond Supervision",IJCAI 2001. A. Hotho, A. Maedche, S. Staab, V. Zacharias : On Knowledgeable Supervised Text Mining . to appear in: "Text Mining" Workshop Proceedings, Springer, 2002.

Selected Literature: 

Selected Literature Semantic Web Mining B. Berendt, A. Hotho, and G. Stumme. Towards semantic web mining. In I. Horrocks and J. A. Hendler, editors, The Semantic Web - ISWC 2002, First International Semantic Web Conference, Sardinia, Italy, June 9-12, 2002, Proceedings, volume 2342 of Lecture Notes in Computer Science, pages 264–278. Springer, 2002.