A semantic-grained perspective of latent knowledge modeling

In the era of Web 2.0, the knowledge is the de-facto social currency in the global network environment. Knowledge is not an accumulation of data, but a relation-based representation of the information content, which needs to be distilled and arranged in a semantic infrastructure to guarantee interoperability and sharable understanding. In the light of this scenario, the paper introduces a semantically enhanced document retrieval system that describes each retrieved document with an ontological multi-grained network of the extracted conceptualization. The system is based on two well-known latent models: Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA): LSA provides a spatial distribution of the input documents, facilitating their retrieval, thanks to an ontological representation of their relationship network. LDA works instead at deeper level: it drives the ontological structuring of the knowledge inside the individual retrieved documents in terms of words, concepts and topics. The novelty of this approach is a multi-level granulation of the knowledge: from a document matching the query (coarse granularity), to the topics that join documents, until to the words describing a concept into a topic (fine granularity). The final result is a SKOS-based ontology, ad-hoc created for a document corpus; graphically supported for the navigation, it enables the exploration of the concepts at different granularity levels.