Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
- URL: http://arxiv.org/abs/2102.07043v1
- Date: Sun, 14 Feb 2021 01:29:54 GMT
- Title: Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
- Authors: Haitian Sun, Pat Verga, Bhuwan Dhingra, Ruslan Salakhutdinov, William
W. Cohen
- Abstract summary: We present the Open Predicate Query Language (OPQL)
OPQL is a method for constructing a virtual Knowledge Base (VKB) trained entirely from text.
We demonstrate that OPQL outperforms prior VKB methods on two different KB reasoning tasks.
- Score: 85.19305347984515
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering [17.281005999581865]
Large-scale knowledge bases (KBs) like Freebase and Wikidata house millions of structured knowledge.
Knowledge Base Question Answering (KBQA) provides a user-friendly way to access these valuable KBs via asking natural language questions.
This paper develops KBLLaMA, which follows a learn-then-reason framework to inject new KB knowledge into a large language model for flexible end-to-end KBQA.
arXiv Detail & Related papers (2024-06-20T22:22:41Z) - ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models [19.85526116658481]
We introduce ChatKBQA, a novel and simple generate-then-retrieve KBQA framework.
Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets.
This work can also be regarded as a new paradigm for combining LLMs with knowledge graphs for interpretable and knowledge-required question answering.
arXiv Detail & Related papers (2023-10-13T09:45:14Z) - KnowledGPT: Enhancing Large Language Models with Retrieval and Storage
Access on Knowledge Bases [55.942342665806656]
KnowledGPT is a comprehensive framework to bridge large language models with various knowledge bases.
The retrieval process employs the program of thought prompting, which generates search language for KBs in code format.
KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands.
arXiv Detail & Related papers (2023-08-17T13:07:00Z) - Mapping and Cleaning Open Commonsense Knowledge Bases with Generative
Translation [14.678465723838599]
In particular, open information extraction (OpenIE) is often used to induce structure from a text.
OpenIEs contain an open-ended, non-canonicalized set of relations, making the extracted knowledge's downstream exploitation harder.
We propose approaching the problem by generative translation, i.e., by training a language model to generate fixed- assertions from open ones.
arXiv Detail & Related papers (2023-06-22T09:42:54Z) - Few-shot In-context Learning for Knowledge Base Question Answering [31.73274700847965]
We propose KB-BINDER, which for the first time enables few-shot in-context learning over KBQA tasks.
The experimental results on four public heterogeneous KBQA datasets show that KB-BINDER can achieve a strong performance with only a few in-context demonstrations.
arXiv Detail & Related papers (2023-05-02T19:31:55Z) - Cross-Lingual Question Answering over Knowledge Base as Reading
Comprehension [61.079852289005025]
Cross-lingual question answering over knowledge base (xKBQA) aims to answer questions in languages different from that of the provided knowledge base.
One of the major challenges facing xKBQA is the high cost of data annotation.
We propose a novel approach for xKBQA in a reading comprehension paradigm.
arXiv Detail & Related papers (2023-02-26T05:52:52Z) - Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge
Base and Database [86.03294330305097]
We propose a unified semantic element for question answering (QA) on both knowledge bases (KB) and databases (DB)
We introduce the primitive (relation and entity in KB, table name, column name and cell value in DB) as an essential element in our framework.
We leverage the generator to predict final logical forms by altering and composing topranked primitives with different operations.
arXiv Detail & Related papers (2022-11-09T19:33:27Z) - TIARA: Multi-grained Retrieval for Robust Question Answering over Large
Knowledge Bases [20.751369684593985]
TIARA outperforms previous SOTA, including those using PLMs or oracle entity annotations, by at least 4.1 and 1.1 F1 points on GrailQA and WebQuestionsSP.
arXiv Detail & Related papers (2022-10-24T02:41:10Z) - DecAF: Joint Decoding of Answers and Logical Forms for Question
Answering over Knowledge Bases [81.19499764899359]
We propose a novel framework DecAF that jointly generates both logical forms and direct answers.
DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks.
arXiv Detail & Related papers (2022-09-30T19:51:52Z) - Beyond I.I.D.: Three Levels of Generalization for Question Answering on
Knowledge Bases [63.43418760818188]
We release a new large-scale, high-quality dataset with 64,331 questions, GrailQA.
We propose a novel BERT-based KBQA model.
The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.
arXiv Detail & Related papers (2020-11-16T06:36:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.