# Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases

arXiv: https://arxiv.org/abs/2108.08597 · HTML: https://mlsanity.com/paper/2108.08597/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 64 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering](https://mlsanity.com/paper/2012.14610/) (2020)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base](https://mlsanity.com/paper/1910.05069/) (2019)
- [Message Passing for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1908.06917/) (2019)
- [Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs](https://mlsanity.com/paper/1908.00469/) (2019)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking](https://mlsanity.com/paper/1609.08075/) (2016)
- [Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering](https://mlsanity.com/paper/1908.11053/) (2019)
- [Question Answering over Curated and Open Web Sources](https://mlsanity.com/paper/2004.11980/) (2020)
- [KOGNAC: Efficient Encoding of Large Knowledge Graphs](https://mlsanity.com/paper/1604.04795/) (2016)
- [Adaptive Low-level Storage of Very Large Knowledge Graphs](https://mlsanity.com/paper/2001.09078/) (2020)

## Built on by (most cited first, 11 of 11)

- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era](https://mlsanity.com/paper/2412.18702/) (2024)
- [Faithful Temporal Question Answering over Heterogeneous Sources](https://mlsanity.com/paper/2402.15400/) (2024)
- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [RAG-based Question Answering over Heterogeneous Data and Text](https://mlsanity.com/paper/2412.07420/) (2024)
- [UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers](https://mlsanity.com/paper/2410.20163/) (2024)
- [Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2511.20940/) (2025)
- [Musical Heritage Historical Entity Linking](https://mlsanity.com/paper/2502.09168/) (2025)
- [Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery](https://mlsanity.com/paper/2506.08771/) (2025)
- [A topic-aware graph neural network model for knowledge base updating](https://mlsanity.com/paper/2208.14601/) (2022)
- [Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors](https://mlsanity.com/paper/2402.00053/) (2024)

## Abstract (arXiv)

Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique for doing this is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.