Operational Research Literature as a Use Case for the Open Research Knowledge Graph

TL;DR

This paper explores how the Open Research Knowledge Graph can be used to semantically structure Operational Research literature, focusing on the Assembly Line Balancing Problem to automate knowledge extraction and improve accessibility.

Contribution

It demonstrates a method to semantically describe and ingest Operational Research papers into the ORKG, showcasing automation potential for structured scholarly data.

Findings

Semantic descriptions align with existing literature

Automated ingestion improves data accessibility

Refinement of semantic models enhances coverage

Abstract

The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.