Merging Structural and Taxonomic Similarity for Text Retrieval Using Relational Descriptions

Information retrieval effectiveness has become a crucial issue with the enormous growth of available digital documents and the spread of Digital Libraries. Search and retrieval are mostly carried out on the textual content of documents, and traditionally only at the lexical level. However, pure term-based queries are very limited because most of the information in natural language is carried by the syntactic and logic structure of sentences. To take into account such a structure, powerful relational languages, such as first-order logic, must be exploited. However, logic formulæ constituents are typically uninterpreted (they are considered as purely syntactic entities), whereas words in natural language express underlying concepts that involve several implicit relationships, as those expressed in a taxonomy. This problem can be tackled by providing the logic interpreter with suitable taxonomic knowledge. This work proposes the exploitation of a similarity framework that includes both structural and taxonomic features to assess the similarity between First-Order Logic (Horn clause) descriptions of texts in natural language, in order to support more sophisticated information retrieval approaches than simple term-based queries. Evaluation on a sample case shows the viability of the solution, although further work is still needed to study the framework more deeply and to further refine it.