Automated Query Learning with Wikipedia and Genetic Programming
Pekka Malo, Pyry Siitari, Ankur Sinha

TL;DR
This paper introduces Wiki-ES, a framework that uses Wikipedia semantics and genetic programming to automatically learn concept-based search queries, significantly improving information retrieval efficiency over traditional token-based methods.
Contribution
It presents the novel Wiki-ES framework that incorporates Wikipedia semantics into query learning using evolutionary algorithms, shifting from token-based to concept-based queries.
Findings
Significant performance improvement in information retrieval using Wiki-ES.
Effective transition from token-based to concept-based queries.
Validation on Reuters newswire documents shows enhanced retrieval efficiency.
Abstract
Most of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents an essential shift from conventional token based queries to concept based queries, leading to an enhanced efficiency of information retrieval systems. To efficiently handle the automated query learning problem, we propose Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based queries are learnt using a co-evolving evolutionary procedure.…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Metaheuristic Optimization Algorithms Research · Advanced Text Analysis Techniques
