# Reasoning Over Virtual Knowledge Bases With Open Predicate Relations

arXiv: https://arxiv.org/abs/2102.07043 · HTML: https://mlsanity.com/paper/2102.07043/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 28 of 28)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [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)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [Entities as Experts: Sparse Memory Access with Entity Supervision](https://mlsanity.com/paper/2004.07202/) (2020)
- [Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling](https://mlsanity.com/paper/1906.07241/) (2019)
- [Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index](https://mlsanity.com/paper/1906.05807/) (2019)
- [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016)
- [Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge](https://mlsanity.com/paper/1805.07858/) (2018)
- [Neural Natural Language Inference Models Enhanced with External Knowledge](https://mlsanity.com/paper/1711.04289/) (2017)
- [Differentiable Reasoning over a Virtual Knowledge Base](https://mlsanity.com/paper/2002.10640/) (2020)
- [Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](https://mlsanity.com/paper/2007.00849/) (2020)
- [Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base](https://mlsanity.com/paper/2002.06115/) (2020)
- [Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension](https://mlsanity.com/paper/1804.07726/) (2018)
- [A Neural Knowledge Language Model](https://mlsanity.com/paper/1608.00318/) (2016)
- [Learning Cross-Context Entity Representations from Text](https://mlsanity.com/paper/2001.03765/) (2020)
- [Dynamic Integration of Background Knowledge in Neural NLU Systems](https://mlsanity.com/paper/1706.02596/) (2017)

## Built on by (most cited first, 18 of 18)

- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [Mention Memory: incorporating textual knowledge into Transformers through entity mention attention](https://mlsanity.com/paper/2110.06176/) (2021)
- [QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs](https://mlsanity.com/paper/2205.12665/) (2022)
- [Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization](https://mlsanity.com/paper/2204.13761/) (2022)
- [Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering](https://mlsanity.com/paper/2204.04581/) (2022)
- [Iterative Hierarchical Attention for Answering Complex Questions over Long Documents](https://mlsanity.com/paper/2106.00200/) (2021)
- [Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks](https://mlsanity.com/paper/2210.00185/) (2022)
- [Relation-Guided Pre-Training for Open-Domain Question Answering](https://mlsanity.com/paper/2109.10346/) (2021)
- [A Unified Encoder-Decoder Framework with Entity Memory](https://mlsanity.com/paper/2210.03273/) (2022)
- [QA Is the New KR: Question-Answer Pairs as Knowledge Bases](https://mlsanity.com/paper/2207.00630/) (2022)
- [Single Sequence Prediction over Reasoning Graphs for Multi-hop QA](https://mlsanity.com/paper/2307.00335/) (2023)
- [Automating IRAC Analysis in Malaysian Contract Law using a Semi-Structured Knowledge Base](https://mlsanity.com/paper/2406.13217/) (2024)
- [Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions](https://mlsanity.com/paper/2308.08661/) (2023)
- [Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start](https://mlsanity.com/paper/2304.03414/) (2023)
- [Semi-supervised Relation Extraction via Data Augmentation and Consistency-training](https://mlsanity.com/paper/2306.10153/) (2023)
- [READ: Improving Relation Extraction from an ADversarial Perspective](https://mlsanity.com/paper/2404.02931/) (2024)

## Abstract (arXiv)

We present the Open Predicate Query Language (OPQL); a method for constructing a virtual KB (VKB) trained entirely from text. Large Knowledge Bases (KBs) are indispensable for a wide-range of industry applications such as question answering and recommendation. Typically, KBs encode world knowledge in a structured, readily accessible form derived from laborious human annotation efforts. Unfortunately, while they are extremely high precision, KBs are inevitably highly incomplete and automated methods for enriching them are far too inaccurate. Instead, OPQL constructs a VKB by encoding and indexing a set of relation mentions in a way that naturally enables reasoning and can be trained without any structured supervision. We demonstrate that OPQL outperforms prior VKB methods on two different KB reasoning tasks and, additionally, can be used as an external memory integrated into a language model (OPQL-LM) leading to improvements on two open-domain question answering tasks.