Hugging Face Trending Papers
Graph-to-Table Interfaces Control Relational Generalisation
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Hugging Face Trending Papers
Oct 16
Path to Prior-Data Fitted Relational Models via Learned Latent Multi-table Representations
Prior-data fitted networks (PFNs) show that pretraining on large populations of synthetic tasks can enable generalization to unseen datasets with little or no downstream fine-tuning. This paradigm is...
arXiv Machine Learning
Aug 13
AutoGrable: What Is a Good Graph for a Table?
arXiv:2608. 11431v1 Announce Type: new Abstract: Graph learning presupposes a graph, and tables and relational databases do not come with one.
arXiv AI
Oct 7
Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing
The paper introduces QRAKEN, a training‑free, ontology‑agnostic neurosymbolic pipeline that grounds natural‑language queries to RDF knowledge graphs using empirical graph evidence instead of schema expectations. QRAKEN distills a compact TTQL representation offline, then uses it online to guide large language models, providing deterministic checks and an iterative refinement loop. On the CK25 benchmark, QRAKEN achieves a strict F1 of 0.643–0.652 with GPT‑4.1 mini and GPT‑5.4, outperforming state‑of‑the‑art systems and demonstrating the effectiveness of empirical pattern distillation over schema‑only approaches.
arXiv AI
Sep 3
PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion
PEARL is a new framework for inductive knowledge graph completion that treats relational paths as context-conditioned reasoning signals. It builds a query‑specific contextual subgraph from the query entities’ neighborhoods and uses a large language model‑guided retriever to select semantically relevant paths. By constructing a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, and applying a dual‑view contrastive objective, PEARL adapts path embeddings to local and global structural evidence, achieving the best average Hits@10 on WN18RR, FB15k‑237, and NELL‑995.
arXiv AI
Aug 28
Beyond Linearization: Attributed Table Graphs for Table Reasoning
The paper introduces Table Graph Reasoner (TabGR), a model that represents tables as an Attributed Table Graph (ATG) to preserve row-column-cell structure and enable graph-based reasoning without task-specific training. It also proposes a Question-Guided Personalized PageRank (QG-PPR) mechanism to rerank tabular data and address the lost-in-the-middle issue. Experiments on multiple table reasoning benchmarks show that TabGR outperforms state-of-the-art models by up to 9.7% in accuracy.
arXiv Machine Learning
Sep 11
Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs
The paper introduces a method that transforms knowledge graph facts into a fixed vocabulary representation, where each fact becomes a node linked to its subject, object, and relation type via six meta-relations. Using this representation, standard GNNs (e.g., GAT, GINE, GraphSAGE, R-GCN) trained on a single small graph can achieve zero‑shot link prediction on 40 inductive benchmarks, matching the performance of specialized foundation models like ULTRA. The approach also generalizes to relational databases, enabling foreign‑key prediction without cell values or schema text, and the authors provide code, checkpoints, and evaluation tools for all benchmarks.