BIR 2016 - 3rd International Workshop on Bibliometric-enhanced Information Retrieval (BIR2016)

Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, although they offer value-added effects for users. In this workshop we will explore how statistical modelling of scholarship, such as Bradfordizing or network analysis of coauthorship network, or simple citation graphs, can improve retrieval services for specific communities, as well as for large, cross-domain collections like Mendeley. This workshop aims to raise awareness of the missing link between Information Retrieval (IR) and bibliometrics/scientometrics and to create a common ground for the incorporation of bibliometric-enhanced services into retrieval at the scholarly search engine interface.
The third BIR workshop (BIR2016) addresses scholarly and explicitly industrial researchers. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete, practical problems of information retrieval and browsing.
Aim of the Workshop
In this third workshop (see papers and presentations of the first workshop and second workshop) we aim to engage with the IR community about possible links to bibliometrics and complex network theory which also explores networks of scholarly communication. Bibliometric techniques are not yet widely used to enhance retrieval processes in digital libraries, yet they offer value-added effects for users. Our interests include information retrieval, information seeking, science modelling, network analysis, and digital libraries. The goal is to apply insights from bibliometrics, scientometrics, and informetrics to concrete practical problems of information retrieval and browsing.
Retrieval evaluations have shown that simple text-based retrieval methods scale up well but do not progress. Traditional retrieval has reached a high level in terms of measures like precision and recall, but scientists and scholars still face challenges present since the early days of digital libraries: mismatches between search terms and indexing terms, overload from result sets that are too large and complex, and the drawbacks of text-based relevance rankings. Therefore we will focus on statistical modelling and corresponding visualizations of the evolving science system. Such analyses have revealed not only the fundamental laws of Bradford and Lotka, but also network structures and dynamic mechanisms in scientific production. Statistical models of scholarly activities are increasingly used to evaluate specialties, to forecast and discover research trends, and to shape science policy. Their use as tools in navigating scientific information in public digital libraries is a promising but still relatively new development. We will explore how statistical modelling of scholarship can improve retrieval services for specific communities, as well as for large, cross-domain collections. Some of these techniques are already used in working systems but not well integrated in larger scholarly IR environments.
The availability of new IR test collections that contain citation and bibliographic information like the iSearch collection or the ACL collection could deliver enough ground to interest (again) the IR community in these kind of bibliographic systems. The long-term research goal is to develop and evaluate new approaches based on informetrics and bibliometrics.
The aim of this workshop is to bring together researchers from different domains, such as information retrieval, information seeking, science modelling, bibliometrics, scientometrics, network analysis, and digital libraries to move toward a deeper understanding of this research challenge.
Workshop Topics
To support the previously described goals the workshop topics include (but are not limited to) the following:
IR for digital libraries and scientific information portals
IR for scientific domains, e.g. social sciences, life sciences etc.
Information Seeking Behaviour
Bibliometrics, citation analysis and network analysis for IR
Query expansion and relevance feedback approaches
Science Modelling (both formal & empirical)
Task based user modelling, interaction, and personalisation
(Long-term) Evaluation methods and test collection design
Collaborative information handling and information sharing
Classification, categorisation and clustering approaches
Information extraction (including topic detection, entity and relation extraction)
Recommendations based on explicit and implicit user feedback