Reducing semantic complexity in distributed Digital Libraries: treatment of term vagueness and document re-ranking

TL;DR

This paper proposes models to reduce semantic complexity in distributed digital libraries by addressing term vagueness and document re-ranking, enhancing search quality through controlled vocabularies and network analysis methods.

Contribution

It introduces a Search Term Recommender for natural language query reformulation and applies scientometric and network analysis techniques for improved document re-ranking.

Findings

User terms often mismatch controlled vocabulary terms

Rephrasing search terms improves retrieval relevance

Network analysis methods enhance document ranking

Abstract

The purpose of the paper is to propose models to reduce the semantic complexity in heterogeneous DLs. The aim is to introduce value-added services (treatment of term vagueness and document re-ranking) that gain a certain quality in DLs if they are combined with heterogeneity components established in the project "Competence Center Modeling and Treatment of Semantic Heterogeneity". Empirical observations show that freely formulated user terms and terms from controlled vocabularies are often not the same or match just by coincidence. Therefore, a value-added service will be developed which rephrases the natural language searcher terms into suggestions from the controlled vocabulary, the Search Term Recommender (STR). Two methods, which are derived from scientometrics and network analysis, will be implemented with the objective to re-rank result sets by the following structural properties: the ranking of the results by core journals (so-called Bradfordizing) and ranking by centrality of authors in co-authorship networks.