Discovering Latent Information By Spreading Activation Algorithm For Document Retrieval
This addresses the limitation of keyword-based retrieval for users needing semantically relevant documents, though it is incremental as it builds on existing spreading activation methods.
The paper tackles the problem of retrieving documents that are semantically related but lack exact query keywords by proposing a query-oriented constrained spreading activation algorithm, which improved MAP by 18.9% over syntactic search and 43.8% over classical constrained spreading activation on a benchmark dataset.
Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation. KEYWORDS: Information Retrieval, Ontology, Semantic Search, Spreading Activation