arXiv AI By Runyu Yu, Zhe Xue, Ang Li

Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences

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arXiv:2204. 04888v2 Announce Type: replace-cross Abstract: In recent years, with the continuous progress of science and technology, the number of scientific research achievements has increased rapidly.

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arXiv AI
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COCI: Conference Organisers and Content Identifier

arXiv:2608.24559v1 Announce Type: cross Abstract: Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from moder...

By Angelo Salatino, Francesco Osborne, Alexis Vizcaino, Aliaksandr Birukou, Enrico Motta
arXiv Computation and Language
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The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

The paper introduces the Scientific Contribution Graph, a large-scale resource that extracts 6 million scientific contributions from 655 k open-access papers across multiple disciplines and links them with 36 million prerequisite edges. It frames automated technological roadmapping as the task of identifying contributions and their prerequisites, and presents a new scientific prerequisite prediction task where models forecast which existing technologies enable future discoveries. The authors report that current models achieve a 0.48 MAP score on temporally-filtered backtesting, indicating rapid progress in this area.

By Peter A. Jansen
arXiv AI
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Time-Aligned Evolving Concept Graphs for Scientific Relation Forecasting

The paper introduces a time‑aligned evolving concept graph framework that jointly models semantic and structural changes in scientific literature. By treating dated papers as shared update events, it reconstructs both semantic and structural states from the same publication history for each prediction time, and fuses these states at the pair level to forecast co‑occurrence, relation formation, and conditional relation type. Experiments on a large graph of 187,848 papers and 270,687 concepts show that refreshing context with graph updates boosts mean relation AUPRC by 16.6% and raises mean relation AUROC from 0.9290 to 0.9722.

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