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

1. Demo of an automatic semantic interpretation of
unstructured data for knowledge management
Topic Maps in the Industry
TMRA 2010

2. Inverted approach of semantic it
Agenda
1. Demo
2. Knowledge Discovery
3. Technical Solution

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. 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. 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

7. 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.

8. 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.

9. 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.

10. 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.

11. 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

12. 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)

13. 3.
Bottom-up semantic data analysis
Example of a graph fragment

14. 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

15. 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

16. Dr. Jörg Wurzer
Member of the board
joerg.wurzer@iqser.net
www.iqser.com
+49 172 66 800 73