# Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval

arXiv: https://arxiv.org/abs/2402.02175 · HTML: https://mlsanity.com/paper/2402.02175/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph](https://mlsanity.com/paper/2212.00959/) (2022)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering](https://mlsanity.com/paper/2308.13259/) (2023)
- [Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering](https://mlsanity.com/paper/2202.13296/) (2022)
- [RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering](https://mlsanity.com/paper/2109.08678/) (2021)
- [DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2210.00063/) (2022)
- [TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases](https://mlsanity.com/paper/2210.12925/) (2022)
- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering](https://mlsanity.com/paper/2308.12060/) (2023)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Variational Knowledge Graph Reasoning](https://mlsanity.com/paper/1803.06581/) (2018)

## Built on by (most cited first, 16 of 16)

- [Graph Neural Network Enhanced Retrieval for Question Answering of LLMs](https://mlsanity.com/paper/2406.06572/) (2024)
- [Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs](https://mlsanity.com/paper/2410.20321/) (2024)
- [Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation](https://mlsanity.com/paper/2502.12737/) (2025)
- [ReMindRAG: Low-Cost LLM-Guided Knowledge Graph Traversal for Efficient RAG](https://mlsanity.com/paper/2510.13193/) (2025)
- [Dynamic-KGQA: A Scalable Framework for Generating Adaptive Question Answering Datasets](https://mlsanity.com/paper/2503.05049/) (2025)
- [Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2510.08825/) (2025)
- [Dynamic Few-Shot Learning for Knowledge Graph Question Answering](https://mlsanity.com/paper/2407.01409/) (2024)
- [RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs](https://mlsanity.com/paper/2510.01257/) (2025)
- [Enhancing Large Language Models with Reward-guided Tree Search for Knowledge Graph Question and Answering](https://mlsanity.com/paper/2505.12476/) (2025)
- [MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation](https://mlsanity.com/paper/2508.03553/) (2025)
- [CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineering](https://mlsanity.com/paper/2509.21035/) (2025)
- [KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval](https://mlsanity.com/paper/2508.20417/) (2025)
- [Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning](https://mlsanity.com/paper/2510.13909/) (2025)
- [RPO-RAG: Aligning Small LLMs with Relation-aware Preference Optimization for Knowledge Graph Question Answering](https://mlsanity.com/paper/2601.19225/) (2026)
- [CHE-TKG: Collaborative Historical Evidence and Evolutionary Dynamics Learning for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2605.04652/) (2026)
- [Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation](https://mlsanity.com/paper/2608.25922/) (2026)

## Abstract (arXiv)

Information retrieval (IR) methods for KGQA consist of two stages: subgraph extraction and answer reasoning. We argue current subgraph extraction methods underestimate the importance of structural dependencies among evidence facts. We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. We implement EPR by indexing the atomic adjacency pattern of resource pairs. Given a question, we perform dense retrieval to obtain atomic patterns formed by resource pairs. We then enumerate their combinations to construct candidate evidence patterns. These evidence patterns are scored using a neural model, and the best one is selected to extract a subgraph for downstream answer reasoning. Experimental results demonstrate that the EPR-based approach has significantly improved the F1 scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves competitive performance on WebQuestionsSP.