Beamqa: Multi-hop knowledge graph question answering with sequence-to-sequence prediction and beam search
Knowledge Graph Question Answering (KGQA) is a task that aims to answer natural
language queries by extracting facts from a knowledge graph. Current state-of-the-art …
language queries by extracting facts from a knowledge graph. Current state-of-the-art …
Semantic structure based query graph prediction for question answering over knowledge graph
Building query graphs from natural language questions is an important step in complex
question answering over knowledge graph (Complex KGQA). In general, a question can be …
question answering over knowledge graph (Complex KGQA). In general, a question can be …
PerKGQA: Question answering over personalized knowledge graphs
Previous studies on question answering over knowledge graphs have typically operated
over a single knowledge graph (KG). This KG is assumed to be known a priori and is lever …
over a single knowledge graph (KG). This KG is assumed to be known a priori and is lever …
Improving question answering over incomplete knowledge graphs with relation prediction
F Zhao, Y Li, J Hou, L Bai - Neural Computing and Applications, 2022 - Springer
Large-scale knowledge graphs (KGs) play a critical role in question answering over KGs
(KGs-QA). Despite of large scale, KGs suffer from incompleteness, which has fueled a lot of …
(KGs-QA). Despite of large scale, KGs suffer from incompleteness, which has fueled a lot of …
Entity neighborhood awareness and hierarchical message aggregation for inductive relation prediction
Inductive relation prediction aims to apply the reasoning ability learned from existing
knowledge graphs to predict the relation between invisible entities. Recently proposed …
knowledge graphs to predict the relation between invisible entities. Recently proposed …
A survey: complex knowledge base question answering
Y Luo, B Yang, D Xu, L Tian - 2022 IEEE 2nd International …, 2022 - ieeexplore.ieee.org
Knowledge base question answering (KBQA) is a technique that utilizes the rich semantic
information in the knowledge base and fully understands the question to obtain the answer …
information in the knowledge base and fully understands the question to obtain the answer …
Fact Finder--Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs
D Steinigen, R Teucher, TH Ruland, M Rudat… - arxiv preprint arxiv …, 2024 - arxiv.org
Recent advancements in Large Language Models (LLMs) have showcased their proficiency
in answering natural language queries. However, their effectiveness is hindered by limited …
in answering natural language queries. However, their effectiveness is hindered by limited …
Multi-hop question answering with knowledge graph embedding in a similar semantic space
F Li, M Chen, R Dong - 2022 International Joint Conference on …, 2022 - ieeexplore.ieee.org
Multi-hop Question Answering using the knowledge graph (KG) as a data source requires
subject entities and relations that are obtained from natural language questions; the …
subject entities and relations that are obtained from natural language questions; the …
Massively multilingual language models for cross lingual fact extraction from low resource indian languages
Massive knowledge graphs like Wikidata attempt to capture world knowledge about multiple
entities. Recent approaches concentrate on automatically enriching these KGs from text …
entities. Recent approaches concentrate on automatically enriching these KGs from text …
A Human-Centric Evaluation Platform for Explainable Knowledge Graph Completion
Explanations for AI are expected to help human users understand AI-driven predictions.
Evaluating plausibility, the helpfulness of the explanations, is therefore essential for …
Evaluating plausibility, the helpfulness of the explanations, is therefore essential for …