Probabilistic Claim Resolution for Author Entities in Scholarly Graphs
This work formalizes a claim workflow that separates platform users, researcher profiles and author entities, improving identity resolution without degrading product usability.
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Scholarly Graph Core Papers
Canonical foundation set for entity resolution, graph-aware discovery and scientific interaction systems.
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evidence mappublicScientific Knowledge Graphsby BZPEER
Use this page as the stable reading surface, then switch to graph when the relationships between items need to become visible.
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A Signal Layer for Science: Structured Reviews, Graph Context and Faster Research Triage
We present a graph-native product architecture for scholarly sensemaking where work pages, topic maps and researcher trust signals reduce time-to-understanding for new literature.
Graph Neighborhoods for Scholarly Search and Topic Expansion
We precompute neighborhoods over works, authors, communities and institutions to speed exploratory search.
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Probabilistic Claim Resolution for Author Entities in Scholarly GraphsConference Paper / 2025
workThis work formalizes a claim workflow that separates platform users, researcher profiles and author entities, improving identity resolution without degrading product usability.
Canonical identity-resolution paper for scholarly graphs.
A Signal Layer for Science: Structured Reviews, Graph Context and Faster Research TriagePreprint / 2026
workWe present a graph-native product architecture for scholarly sensemaking where work pages, topic maps and researcher trust signals reduce time-to-understanding for new literature.
Product architecture reference for graph-native research interaction.
Graph Signal Circletopic / 0 members
communityHigh-signal discussion for scholarly graphs, entity resolution and research discovery systems.
Public community connected to the same graph-native problem space.
Graph Neighborhoods for Scholarly Search and Topic ExpansionConference Paper / 2025
workWe precompute neighborhoods over works, authors, communities and institutions to speed exploratory search.
Precomputed neighborhoods make graph exploration and retrieval much faster.
Sparse Embeddings for Scholarly Discovery with Human-Readable SignalsConference Paper / 2026
workWe compare dense and sparse retrieval strategies for scholarly search when operators need interpretable reasons and controllable ranking.
Readable retrieval reasons pair well with graph-native exploration.