A Science Model Driven Retrieval Prototype

TL;DR

This paper introduces science model driven retrieval services for digital libraries, improving retrieval precision and offering new ways to explore scientific knowledge structures.

Contribution

It presents three novel science model based retrieval services and evaluates their effectiveness compared to traditional tf-idf methods.

Findings

Precision is comparable or better than tf-idf baseline.

Services retrieve disjoint sets of relevant documents.

Different models highlight different relevant documents.

Abstract

This paper is about a better understanding on the structure and dynamics of science and the usage of these insights for compensating the typical problems that arises in metadata-driven Digital Libraries. Three science model driven retrieval services are presented: co-word analysis based query expansion, re-ranking via Bradfordizing and author centrality. The services are evaluated with relevance assessments from which two important implications emerge: (1) precision values of the retrieval service are the same or better than the tf-idf retrieval baseline and (2) each service retrieved a disjoint set of documents. The different services each favor quite other - but still relevant - documents than pure term-frequency based rankings. The proposed models and derived retrieval services therefore open up new viewpoints on the scientific knowledge space and provide an alternative framework to structure scholarly information systems.