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Found 27 papers, 12 papers with code
RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction
However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.
AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing
With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality.
Good Idea or Not, Representation of LLM Could Tell
Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs, demonstrating a promising avenue for automating the idea assessment process.
Unlock the Power of Frozen LLMs in Knowledge Graph Completion
Traditional knowledge graph completion (KGC) methods rely solely on structural information, struggling with the inherent sparsity of knowledge graphs (KGs).
AutoFAIR : Automatic Data FAIRification via Machine Reading
Subsequently, FAIR Alignment is employed to make metadata comply with FAIR principles by ontology guidance and semantic matching.
Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints
The penalty function method has recently been studied as an effective approach for handling constraints, which imposes constraints penalties on the objective to transform the constrained problem into an unconstrained one.
OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning
Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem.
RepEval: Effective Text Evaluation with LLM Representation
Besides, previous LLM-based metrics ignore the fact that, within the space of LLM representations, there exist direction vectors that indicate the estimation of text quality.
Characterizing the Influence of Topology on Graph Learning Tasks
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.
Is Reference Necessary in the Evaluation of NLG Systems? When and Where?
The majority of automatic metrics for evaluating NLG systems are reference-based.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
GeoGalactica: A Scientific Large Language Model in Geoscience
To our best knowledge, it is the largest language model for the geoscience domain.
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields.
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation
To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning.
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets.
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Large language models (LLMs) have achieved great success in general domains of natural language processing.
Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
FMGNN: Fused Manifold Graph Neural Network
Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks.
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.
Temporal Knowledge Graph Reasoning with Historical Contrastive Learning
Simultaneously, it trains representations of queries to investigate whether the current moment depends more on historical or non-historical events by launching contrastive learning.
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications
Understanding the origin and influence of the publication's idea is critical to conducting scientific research.
INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion
As a fully unsupervised framework, INFINITY is empirically verified to outperform state-of-the-art baselines for G2T and T2G tasks.
Disentangled Graph Contrastive Learning for Review-based Recommendation
In review-based recommendation methods, review data is considered as auxiliary information that can improve the quality of learned user/item or interaction representations for the user rating prediction task.
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities.
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation
Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype.
class-incremental learning
Few-Shot Class-Incremental Learning
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High-Order Relation Construction and Mining for Graph Matching
Iterated line graphs are introduced for the first time to describe such high-order information, based on which we present a new graph matching method, called High-order Graph Matching Network (HGMN), to learn not only the local structural correspondence, but also the hyperedge relations across graphs.
