Workshop Description · Areas of Intererst  · Accepted Papers ·Program · Target Audience · Submission Details · Important Dates  · Organizing Committee 

**** new ! List of accepted Papers ****

Workshop Description
Vast quantities of valuable knowledge are embedded in unstructured textual formats. Petabytes of text are currently available on the public Web, in intranets and other private repositories, and on our personal desktop machines. In many cases, the only way to access such documents is through blunt instruments such as keyword-based document retrieval. In recent years, there has been significant research (and  considerable commercial interest) in technologies for automatically
extracting and mining useful structured knowledge from unstructured text. Current trends suggest a movement away from pure natural language processing approaches requiring the manual development of  rules, to a shallower, less knowledge intensive techniques based on techniques from machine learning, information retrieval and data mining.

Adaptive text extraction and mining is an enabling technology with a wide variety of applications. On the Web, automated knowledge capture from text would open the way for both better retrieval, and advanced business applications (e.g. B2B/B2C applications mediated by knowledge-aware agents). For knowledge management, capturing the knowledge contained in a company’s repositories would encourage knowledge to be shares and reused among employees, improving
efficiency and competitiveness. Extracting information from texts is an important step in capturing knowledge, e.g. for populating databases or ontologies, supporting document annotation (e.g. for the Semantic Web), for learning ontologies, etc.

The workshop will bring together researchers and practitioners from different communities (e.g. machine learning, text mining, natural language processing, information extraction, information retrieval, ontology learning) to discuss recent results and trends in mining texts for knowledge capture. Members of other communities (e.g.  information integration and data mining) could find the workshop very interesting as well. Previous workshops on the use of machine learning for information extraction were held at AAAI-1998, ECAI-2000, and IJCAI-2001.

• machine learning and natural language
• learning to annotate documents
• ontology learning 
• information retrieval and learning 
• information integration from textual and multimedia resources 
• relevant aspects of human-computer interaction and semi-supervised  learning
  

Organizing  Committee:
 

Fabio Ciravegna ·(Primary contact)  p: Computer Science Department, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP, UK · t: +44-114-22-21800 · f: +44-114-22-21810 · e: f.ciravegna at dcs . shef . ac . uk · w: www.dcs.shef.ac.uk/~fabio.

Nicholas Kushmerick  · p: Smart Media Institute, Computer Science Department, University College Dublin, Dublin 4, Ireland · t: +353-1-706-2479 · f: +353-1-269-7262 · e: nick at ucd . ie · w: www.cs.ucd.ie/staff/nick.