Good Papers

Showing Knowledge graphs Show all papers

45%Niche pick
?Niche pickVote to see the score

Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering

Yang Hong, Yang Yajun, Xin Wang, Liping Jing and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
78%Highly rated
?Highly ratedVote to see the score

Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

Intern-Atlas builds a methodological evolution graph from over one million AI papers to model how methods emerge and adapt, enabling automated idea evaluation and generation.

Yujun Wu, Dongxu Zhang, Xinchen Li, Jinhang Xu and 10 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 47 on Hugging Face

– ReadersNo votes yet
11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
83%Must read
?Must readVote to see the score

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning

A unified framework combines unsupervised pretraining and supervised neural knowledge graph learning, with a nonasymptotic risk bound showing unlabeled data reduces downstream prediction error.

Suqi Liu, Miklos Z. Racz, Jifan Zhang

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 2/5
76%Highly rated
?Highly ratedVote to see the score

EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge

EMERGE benchmarks knowledge graph updates via 233K Wikipedia passages mapped to 1.45M Wikidata edits across 2019, 2025. Experiments highlight challenges integrating emerging textual knowledge with existing graph structures.

Klim Zaporojets, Daniel Daza, Edoardo Barba, Ira Assent and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 2/5
76%Highly rated
?Highly ratedVote to see the score

Neural Structural Reasoner: A Brain-inspired Architecture for Reasoning over Structured Knowledge

Neural Structural Reasoner is a brain-inspired network preserving relational structure in neuronal dynamics to achieve interpretable, efficient structural reasoning over knowledge graphs.

Zixing Jia, Yuhang Pan, Ni Ji

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5
74%Highly rated
?Highly ratedVote to see the score

NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training

NGDB-Zoo improves neural graph database training via operator-level scheduling and semantic augmentation, achieving 1.8, 6.8x throughput without I/O stalls.

zhongwei xie, Jiaxin Bai, Shujie LIU, Haoyu Huang and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 2/5
medium 6/10
strict 1/5