Automatic semantic interpretation of unstructured data for knowledge management

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The demo shows an automatic semantic analysis of Wikipedia articles about astronomy.

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Automatic semantic interpretation of unstructured data for knowledge management

  1. 1. Demo of an automatic semantic interpretation of unstructured data for knowledge management Topic Maps in the Industry TMRA 2010
  2. 2. Inverted approach of semantic it Agenda 1. Demo 2. Knowledge Discovery 3. Technical Solution
  3. 3. 1. Demo The demo shows twofold results of an automatic semantic analysis of Wikipedia articles to demonstrate a new approach for knowledge discovery.
  4. 4. 1. Demo Analysis of Wikipedia articles about astronomy Crawling all articles of a knowledge domain Extracting the relevant text parts of Wikipedia pages Extracting meta data of each Wikipedia article Automatic semantic analysis of integrated data on a term level to create a linked concept graph on an object level linked data (object) graph
  5. 5. 1. Demo What the demo shows Visualization of the linked concept graph (left) Visualization of the linked data graph (right) Knowledge discovery by a taxonomy and linked data Accessing information by linked data Accessing information by derived taxonomy
  6. 6. 2. Knowledge Discovery Isolated data becomes meaning by links to related data. Even unstructured information can be evaluated systematically by linked data and a derived taxonomy.
  7. 7. 2. Knowledge Discovery Use cases for an object graph Information Logistics: Relevant information will be provided automatically in the process or activity context of a user. Portal navigation: Users can navigate according to their personal focus of interest along the dynamic links to each selected context. Knowledge discovery: Awareness of hidden knowledge such as project synergies, sales opportunities, relevant news. Question answering: The identification of appropriate responses, related problems, or experts on the issue. Business intelligence: Complex queries of the object graph for reports on customer behavior, staff profiles and project analysis.
  8. 8. 2. Knowledge discovery Use cases for a concept graph Knowledge Representation: The concept graph gives an overview of key entities and facts in an unstructured data set. Document and e-mail-clustering: Unstructured data will be grouped thematically or associated with each path in a taxonomy. Moderated search: searches for the automatic extension of a keyword search for increased precision of the results. Topic monitoring: Identifying new facts and new issues or topics in the news, or constellations of other publications Taxonomy or ontology modeling and maintenance: Initial knowledge representation and identification of adaptation needs.
  9. 9. 3. Technical Solution Knowledge discovery needs a real bottom-up-approach with no initial effort on modeling a knowledge domain. The result can be exported as topic maps or combined with formalized domain knowledge of existing topic maps.
  10. 10. 3. Bottom-up semantic data integration Implementing Content Provider Lean interfaces to connect any data format and source Push and pull principle to monitor data sources Optional bi-directional integration of data sources Optional definition of actions for data objects in each source Implicit data harmonization and derivation of a common model
  11. 11. 3. Bottom-up semantic data analysis Object graph (linked data graph) All relations (quadruples) are dynamically created and updated in real-time described by the semantic reason weighted regarding the relevance All relations are created by Key attributes (syntax analysis) Text mining (pattern analysis) User behavior (usage analysis)
  12. 12. 3. Bottom-up semantic data analysis Example of a graph fragment
  13. 13. 3. Bottom-up semantic data integration Concept graph Extraction of concepts such as names and terms in texts Calculation of significance of extracted concepts Identification of the co-occurrences of significant concepts Creating a graph with significance value for nodes and edges Dynamically updated graph caused by new data Calculation of a hierarchical structure for a taxonomy
  14. 14. 3. iQser GIN Platform Web Rich-/Fat Client ESB / SOA Mobile Client Connector API Security Layer iQser Core Event Listener API Analyzer Task API Analyzer Chain Event Processor Custom Event Custom Analytics / Actions / Business Ontologies Logic Objektgraph Konzeptgraph Index Content Provider API Fila System Custom ERP Collaboration Applications CRM WWW
  15. 15. Dr. Jörg Wurzer Member of the board joerg.wurzer@iqser.net www.iqser.com +49 172 66 800 73

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