# Knowledge Base Relation Detection via Multi-View Matching

Source: [Knowledge Base Relation Detection via Multi-View Matching](<https://www.sota2.com/research/paper/68fa64461e209131cea03a47>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:00:11.434Z

Authors: Yang Yu, Kazi Saidul Hasan, Mo Yu, Wei Zhang, Zhiguo Wang

Published: 2018-03-01T20:17:02.000Z

## Abstract

Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a KB relation detection model via multi-view matching which utilizes more useful information extracted from question and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are designed in an end-to-end trainable neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1803.00612>)

[PDF](<https://arxiv.org/pdf/1803.00612.pdf>)