AIS and Semantic Query

  • Rana Kashif Ali
  • Steve Cayzer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3532)


The semantic web has created various exciting opportunities to explore. Here we present a nature inspired solution to one such opportunity; that of semantic queries for information retrieval. We take our inspiration from the human immune system and develop an analogy between antibodies and queries. Successful antibodies are those that are activated by an infection. These antibodies are stimulated to clone, but imperfectly, giving rise to a multitude of similar antibodies that are better suited to tackle the infection. Analogously, queries producing relevant results can be cloned to give rise to various similar queries, each of which may be an improvement on the original query. The semantic web, being concept based, has a set of rules for creating expressive yet standardised queries with clear semantics guiding their modification. This paper discusses the implementation and evaluation of such an immune based information retrieval technique for the semantic web. Two query mutation operators; RandomMutationOperator and ConstrainedMutationOperator are proposed and compared in terms of their precision, recall and convergence. We have found the presented approach to be viable, and we discuss the potential for further improvements.


Resource Description Framework Artificial Immune System Query Expansion Semantic Query Biological Immune System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rana Kashif Ali
    • 1
  • Steve Cayzer
    • 2
  1. 1.The University of BirminghamBirminghamUK
  2. 2.HP LaboratoriesBristolUK

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