Metadata about: New research is being published at a rate, at which it is infeasible for many scholars to read and assess everything possibly relevant to their work. In pursuit of a remedy, efforts towards automated processing of publications, like semantic modelling of papers to facilitate their digital handling, and the development of information filtering systems, are an active area of research. In this paper, we investigate the benefits of semantically modelling citation contexts for the purpose of citation recommendation. For this, we develop semantic models of citation contexts based on entities and claim structures. To assess the effectiveness and conceptual soundness of our models, we perform a large offline evaluation on several data sets and furthermore conduct a user study. Our findings show that the models can outperform a non-semantic baseline model and do, indeed, capture the kind of information they’re conceptualized for.

About: New research is being published at a rate, at which it is infeasible for many scholars to read and assess everything possibly relevant to their work. In pursuit of a remedy, efforts towards automated processing of publications, like semantic modelling of papers to facilitate their digital handling, and the development of information filtering systems, are an active area of research. In this paper, we investigate the benefits of semantically modelling citation contexts for the purpose of citation recommendation. For this, we develop semantic models of citation contexts based on entities and claim structures. To assess the effectiveness and conceptual soundness of our models, we perform a large offline evaluation on several data sets and furthermore conduct a user study. Our findings show that the models can outperform a non-semantic baseline model and do, indeed, capture the kind of information they’re conceptualized for.

an Entity references as follows:

Graph IRI	Count
http://ns.inria.fr/covid19/graph/entityfishing	6
http://ns.inria.fr/covid19/graph/articles	3

Faceted Search & Find service v1.13.91

This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Copyright © 2009-2024 OpenLink Software