Topic Maps in the Industry

TMRA 2010

Demo of an automatic semantic interpretation of unstructured data for knowledge management

Agenda

1. Demo

2. Knowledge Discovery

3. Technical Solution

Inverted approach of semantic it

1.

The demo shows twofold results of an automatic semantic analysis of Wikipedia articles to demonstrate a new approach for knowledge discovery.

Demo

1.

Analysis of Wikipedia articles about astronomy

Demo

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

1.

What the demo shows

Demo

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

2.

Isolated data becomes meaning by links to related data. Even unstructured information can be evaluated systematically by linked data and a derived taxonomy.

Knowledge Discovery

2.

Use cases for an object graph

Knowledge Discovery

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.

2.

Use cases for a concept graph

Knowledge discovery

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.

3.

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.

Technical Solution

3.

Implementing Content Provider

Bottom-up semantic data integration

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

3.

Object graph (linked data graph)

Bottom-up semantic data analysis

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)

3.

Example of a graph fragment

Bottom-up semantic data analysis

3.

Concept graph

Bottom-up semantic data integration

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

3.

iQser GIN Platform

ERPCRM WWW

Collaboration

Fila System

Custom Applications

Client Connector API

Content Provider API

Ana

lyze

r Tas

k A

PI

Even

t Li

sten

er A

PI

Security Layer

iQser Core

Custom Analytics / Ontologies

Custom Event Actions / Business

Logic

ESB / SOAWeb Rich-/Fat Client

Mobile

Analyzer Chain Event Processor

Objektgraph Konzeptgraph

Index

joerg.wurzer@iqser.net

www.iqser.com

+49 172 66 800 73

Dr. Jörg Wurzer Member of the board