Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
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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.