Semantic querying of data guided by Formal Concept Analysis

What can FCA do for Artificial Intelligence?

Víctor Codocedo1 ; Ioanna Lykourentzou1,2 ; Amedeo Napoli1

Others  :  http://ceur-ws.org/Vol-939/paper4.pdf
PID  :  27004

来源: CEUR

【 摘 要 】

In this paper we present a novel approach to handle querying over a concept lattice of documents and annotations. We focus on the problem of "non- matching documents", which are those that, despite being semantically relevant to the user query, do not contain the query's elements and hence cannot be retrieved by typical string matching approaches. In order to find these documents, we modify the initial user query using the concept lattice as a guide. We achieve this by identifying in the lattice a formal concept that represents the user query and then by finding potentially relevant concepts, identified as such through the proposed notion of cousin concepts. Finally, we use a concept semantic similarity metric to order and present retrieved documents. The main contribution of this paper is the introduction of the notion of cousin concepts of a given formal concept followed by a discussion on how this notion is useful for lattice-based information indexing and retrieval.