SCOPE

Papers on these and related subjects are particularly encouraged:

  • Artificial Immune Systems (AIS),
  • Search Engines (SE),
  • Computational Linguistics (CL),
  • Knowledge Discovery (KD).
The Conference's focus will also be on the following topics:
  • new computing paradigms, including, but not restricted to biologically motivated methods, quantum computing, DNA computing,
  • advanced data analysis,
  • new machine learning paradigms,
  • reasoning technologies,
  • natural language processing,
  • novelty detection,
  • new optimization technologies,
  • applied data mining using statistical and non-standard approaches,
  • technologies for very large text bases,
  • uncertainty management.


The four main streams:

Artificial Immune Systems (AIS)

Research on biologically motivated systems (genetic algorithms, neural networks, ant colony algorithms) has been going on for many years now. For a number of reasons the investigation of properties of immune system of humans and animals fertilized recently a broad range of research in creating artificial immune systems. The track on Artificial Immune Systems is intended for presentation of the progress achieved in the area.
Original contributions are welcome including but not restricted to the following topics and applications:

  • Simulation of natural immune systems,
  • Learning idiotype systems,
  • Exploratory data analysis,
  • Clustering techniques based on immunological principles,
  • Immunological data compression,
  • Immunological genetic algorithms,
  • Discrete and continuous optimization in static and dynamic environments,
  • Anomaly detection and detection of intruders, new approaches to combating software viruses.


Search Engines (SE)

Internet is a vast source of information. Unfortunately it is useless unless we know where to find the piece of information we need. Search Engines and related tools are intended to tell us where the information is. Here we have to do with a non-trivial challenge for an artificial intelligent system: it has to "understand" the relationship between the intention of a natural language query formulated by a truly intelligent human (the internaut) and the content of natural language documents prepared by some other intelligent humans.
We encourage papers devoted to Search Engines technology addressing the progress achieved in the area of

  • large scale search engines: design and implementation,
  • personal search tools,
  • intelligent spiders,
  • on-line and off-line document clustering,
  • static and dynamic document maps,
  • intelligent navigation through hypertext document collections,
  • translation of documents,
  • linguistic research on Web documents,
  • technologies of extraction of information from text and non-text documents,
  • text mining,
  • web mining,
  • question answering versus document retrieval,

and on related topics.



Computational Linguistics (CL)

Natural language is the most obvious way of man-to-man communication but it is still very difficult to use for computer programs. To overcome this difficulty, a lot of effort has been devoted to provide computational models of various aspects of natural language usage. These results are incorporated into many working systems today, including speech recognition systems, text-to-speech synthesizers, automated voice response systems, web search engines, text editors, or information extraction systems.
We invite contributors interested in Computational Linguistics to submit papers concerned especially with:

  • automatic text analysis,
  • corpus-based language processing,
  • statistical and machine learning methods in NLP,
  • information retrieval, information extraction and text summarization,
  • speech recognition and synthesis,
  • question answering systems,
  • dialogue systems,
  • machine and machine-aided translation tools,

and other areas of natural language processing.



Knowledge Discovery (KD)

The most intricating area of human activity is the discovery of relations within the physical and social environment. The complexity of the knowledge acquisition process and / or the amount of data urge for extended support of computing machines.
We encourage researchers in this and related areas to submit papers describing results and ongoing research on topics:

  • machine learning and statistical tools in knowledge discovery,
  • knowledge representation and its impact on discovery processes,
  • machine learning algorithms,
  • new knowledge discovery paradigms,
  • learning from data versus learning from knowledge sources,
  • autonomous knowledge discovery robots,
  • knowledge discovery tools in OLAP,
  • knowledge acquisition from the Web,
  • applications of knowledge acquisition methodologies and software,

and the like.