Recognizing Topic Change in Search Sessions of Digital Libraries based on Thesaurus and Classification System

TL;DR

This paper introduces a novel method for segmenting search sessions in digital libraries based on thesaurus and classification systems, improving topic detection with expert validation.

Contribution

It proposes a new approach leveraging domain-specific knowledge organization systems for session segmentation, validated by expert ratings.

Findings

Experts rated the method as good for topic segmentation.

The approach effectively uses thesaurus and classification data.

It enhances user support by accurately identifying topic boundaries.

Abstract

Log analysis in Web search showed that user sessions often contain several different topics. This means sessions need to be segmented into parts which handle the same topic in order to give appropriate user support based on the topic, and not on a mixture of topics. Different methods have been proposed to segment a user session to different topics based on timeouts, lexical analysis, query similarity or external knowledge sources. In this paper, we study the problem in a digital library for the social sciences. We present a method based on a thesaurus and a classification system which are typical knowledge organization systems in digital libraries. Five experts evaluated our approach and rated it as good for the segmentation of search sessions into parts that treat the same topic.