1 Building Semantic Applications Paul Warren

2 the need for semantics the SEKT solution SEKT partners architecture applications semantic knowledge management

3 The need for semantics Knowledge workers overwhelmed by info from intranets, s, newslines … but still lack vital information 80% of corporate data is unstructured including key business decisions subject to regulation, e.g. SOX Companies suffer from decisions made under incomplete knowledge threat of compliance failure

4 We need information … Identified by semantics, not just keywords precise and complete Selected by their interests & task context defined semantically From heterogeneous sources, accessed uniformally Presented meaningfully and appropriately for the user

5 In the right form Integrated into the desktop applications Seamless proactive, not reactive Depending on context interests and current activities mobile phone, PDA, blackberry With appropriate visualisation relation between documents & concepts And expressed in natural language where this aids understanding multilingual the need for semantics

6 Semantic knowledge management Semantic knowledge management classifies, finds, distributes, shares and uses knowledge based on meaning not the particular words used to represent meaning.

7 In three words Semantic knowledge management classifies, finds, distributes, shares and uses knowledge based on meaning not the particular words used to represent meaning. semantic knowledge management

8 Words and meanings same word, different meanings different words, same meaning disability legislation equal opportunity laws different words, related meaning trade unioncharitycompany organisation Jaguar semantic knowledge management

9 The SEKT solution Finding and sharing knowledge through its semantics for improved precision and recall for the user’s interests and current context Extracting knowledge in a meaningful way, without duplication to create a knowledgebase Reasoning about knowledge Displaying all relevant knowledge information-centric, not document-centric

10 Extracting the semantics Information extraction using human language technology Knowledge discovery machine learning and statistical methods Existing metadata, e.g. database schemas mapping and merging the SEKT solution

11 The knowledge base the SEKT solution

12 Accessing the knowledge base the SEKT solution

13 Number sells to operates in employee size Ontology modelling the SEKT solution

14 SEKT applications applications intelligent decision support in the legal sector – helping newly appointed judges knowledge management for IT consultants intelligent content management

15 Intelligent content management applications based on semantics … not text providing one view to heterogeneous knowledge classifying semantically extracting and presenting visually and in natural language Searching, alerting, sharing …

16 Knowledge management applications sharing and reusing knowledge across a global team … … building on Siemens knowledgemotion®

17 Intelligent decision support applications a database of frequently asked questions – using semantic distance to identify questions and answers with justification drawn from comprehensive legal databases combining formal and informal knowledge

18 More applications … portals intelligent customer contact business intelligence supply chain management semantic desktop enhancing collaboration …. applications

19 SEKT architecture architecture knowledge engineering timeapplication run-time persistent data Device-Independent Web Application Framework Applications, e.g. search & browse, legal decision support SEKT Integrated Platform (SIP) Knowledge engineering environment (OntoStudio) Ontology, annotation and mapping editors Ontology management and reasoning (KAON2)

20 Run-time Supporting users in their knowledge work generic applications: search & browse knowledge sharing knowledge alerting specific applications legal decision-making business intelligence, CRM, collaboration …

21 Knowledge engineering Enriching information with meaning Creating and editing ontologies with automatic assistance Annotating documents with respect to an ontology editing automatically-created annotations Creating ontology mappings editing automatically-created mappings

22 Applications and training Intelligent content management Knowledge management Legal decision support Training SEKT partners

23 Tools End-user tools visualisation ~ iSOCO profiling ~ Jozef Stefan Institute language generation ~ University of Sheffield Editor & annotation tools SEKT partners

24 Enabling components evOWLution - managing & evolving knowledge models Text2Onto – ontology learning from text GATE information extraction TextGarden knowledge discovery PROTON – knowledgebase KIM – semantic annotation & retrieval SEKT partners

25 Applications & systems integration Knowledge management Empolis GmbH - Ralph Traphöner Siemens Business Service – Dirk Ramhorst Legal decision support iSOCO – Richard Benjamins UAB Institute of Law and Technology – Pompeu Casanovas Intelligent content management British Telecommunications Plc - John Davies SEKT partners

26 Tools & services knowledge models & knowledge software Ontoprise GmbH Ontotext Lab, Sirma training kea-pro GmbH SEKT partners

27 Research partners AIFB, University of Karlsruhe Rudi Studer - Department of Computer Science, University of Sheffield Hamish Cunningham - Dept. of Intelligent Systems, Jozef Stefan Institute Marko Grobelnik - AI Department, Vrije Universiteit Amsterdam Frank van Harmelen - Institute of Computer Science, University of Innsbruck Dieter Fensel – SEKT partners

28 The SEKT partners kea-pro University of Sheffield