Challenge: Existing retrieval-based approaches to solve multihop Knowledge Base Question Answering (KBQA) fail to utilize information from head-tail entities and the semantic connection between relations to enhance the information capturing of relations in KGs.
Approach: They propose to use a dual relation graph to find the answer entity in a knowledge graph . they use primal entity graph reasoning, dual relation grafitment and interaction .
Outcome: The proposed approach achieves significant performance gain over the prior state-of-the-art on two public datasets, WebQSP and CWQ.

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Challenge: Existing approaches to answer natural language questions on knowledge graphs (KGQA) use large-scale entity-related text corpus or knowledge graph embeddings as auxiliary information to facilitate answer selection.
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Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)

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Challenge: Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale.
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Constraint-based Multi-hop Question Answering with Knowledge Graph (2022.naacl-industry)

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Challenge: Recent work addresses multi-hop KGQA, which requires reasoning across numerous edges of the KG.
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Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)

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Challenge: Existing Knowledge-based Question Answering methods use a query graph to find the answer to a question.
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Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings (2020.acl-main)

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Challenge: Existing multi-hop KGQA methods impose heuristic neighborhood limits, which often make it much harder to answer the input NL question.
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Knowledge Base Question Answering via Encoding of Complex Query Graphs (D18-1)

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Challenge: Existing KBQA methods focus on simpler questions and do not work well on complex questions . a knowledge-based question answering approach is able to answer complex questions using a standard knowledge base .
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Chain-of-Relations: Faithful and Efficient LLM Reasoning over Knowledge Graphs via Relation-Centric Exploration (2026.findings-acl)

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Challenge: Existing methods adopt entity-centric exploration that incrementally constructs reasoning paths by selecting and connecting intermediate entities.
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A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation (2021.findings-emnlp)

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Challenge: Existing methods treat multi-label learning problem as a single label . Existing approaches focus on measuring semantic similarity of questions and candidate relations .
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Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)

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Challenge: Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them.
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CORN: Co-Reasoning Network for Commonsense Question Answering (2022.coling-1)

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Challenge: Existing work uses two independent modules to model QA content and external commonsense knowledge graph (KG) Existing research uses two separate modules to create QA contextual text representations and relationships between QA entities.
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