Search Results for author:
Found 10 papers, 9 papers with code
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
The joint model for the two tasks is becoming a tendency in SLU.
Ranked #6 on
Intent Detection
on SNIPS
EchoEA: Echo Information between Entities and Relations for Entity Alignment
Furthermore, we propose attribute-combined bi-directional global-filtered strategy (ABGS) to improve bootstrapping, reduce false samples and generate high-quality training data.
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD
With the introduction of a priori knowledge of 10 lesion signs of input images into the KFWC, we aim to accelerate the KFWC by means of multi-label classification pre-training, to locate the decisive image features in the fine-grained disease classification task and therefore achieve better classification.
FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoning
To address these challenges, we instead propose a novel KGR framework named Feature-Logic Embedding framework, FLEX, which is the first KGR framework that can not only TRULY handle all FOL operations including conjunction, disjunction, negation and so on, but also support various feature spaces.
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
In addition, our framework extends vector logic on timestamp set to cope with three extra temporal operators (After, Before and Between).
DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing
To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities.
Ranked #1 on
Link prediction on DH-KGs
on JW44K-6K
NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs).
Ranked #1 on
Complex Query Answering
on WD50K-QE
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level
The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers.
Ranked #1 on
Link Prediction
on Wikipeople
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction.
Event-based N-ary Relaiton Extraction
Hypergraph-based N-ary Relaiton Extraction
+3
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.
Ranked #1 on
Knowledge Base Question Answering
on WebQuestionsSP
