Construction of a Battery Research Knowledge Graph using a Global Open Catalog

TL;DR

This paper presents a pipeline to construct a comprehensive, author-centric knowledge graph for battery research using open bibliographic data, semantic keyphrase extraction, and linked data integration.

Contribution

It introduces a novel method combining semantic keyphrases and bibliographic data to build an interoperable, cross-institutional battery research knowledge graph.

Findings

Supports author similarity and community detection.

Enables exploratory search via a web interface.

Links to external data sources like Wikidata.

Abstract

Battery research is a rapidly growing and highly interdisciplinary field, making it increasingly difficult to track relevant expertise and identify potential collaborators across institutional boundaries. In this work, we present a pipeline for constructing an author-centric knowledge graph of battery research built on OpenAlex, a large-scale open bibliographic catalogue. For each author, we derive a weighted research descriptors vector that combines coarse-grained OpenAlex concepts with fine-grained keyphrases extracted from titles and abstracts using KeyBERT with ChatGPT (gpt-3.5-turbo) as the backend model, selected after evaluating multiple alternatives. Vector components are weighted by research descriptor origin, authorship position, and temporal recency. The framework is applied to a corpus of 189,581 battery-related works. The resulting vectors support author-author similarity computation, community detection, and exploratory search through a browser-based interface. The knowledge graph is then serialized in RDF and linked to Wikidata identifiers, making it interoperable with external linked open data sources and extensible beyond the battery domain. Unlike prior author-centric analyses confined to institutional repositories, our approach operates at cross-institutional scale and grounds similarity in domain semantics rather than citation or co-authorship structure alone.