🤖 AI Summary
Existing knowledge graphs modeling academic citation networks typically capture only topological structure, neglecting the stance and evaluative content embedded in citations. This work proposes a novel "claim network" representation framework that reformulates citations as typed claim units, each comprising source, target, claim text, and one of four stance labels, along with a general-purpose construction pipeline. By explicitly modeling citation intent for the first time, the approach enables fine-grained characterization of scholarly evaluative relationships. The authors construct a claim network from 127 papers on 3D point cloud semantic segmentation, yielding 8,260 typed claims, and demonstrate significant performance gains in downstream tasks including retrieval-augmented reasoning, stance-aware summarization, and topological analysis.
📝 Abstract
Knowledge graphs over corpora of inter-referencing documents - scholarly papers, legal opinions, policy briefs - encode the topology of reference but not its stance. The standard representation collapses a rich evaluative relation into an untyped edge, losing the very content that supports community-level queries about how one document is received by another. We propose the claim network: a representational pattern in which each cross-document reference is reified as a typed claim, carrying source, target, claim text, and a four-class stance label grounded in the citation-intent literature. We give a construction pipeline applicable to any corpus of scholarly inter-referencing documents and instantiate it on a corpus of 127 papers in 3D point cloud semantic segmentation, producing a network of 8,260 typed claims. Three downstream task families demonstrate what the network enables: retrieval signal augmentation, aggregated-stance summarisation, and topological analytics. Head-to-head evaluation against standard Retrieval-Augmented Generation (RAG) baselines shows that the gain over flat retrieval is the gain from the right intermediate representation rather than the wrong one.