3 Stage-II: Neural Graph Search Module 对Stage-I输出的错误分析表明,实体链接的性能相当好,但关系链接的性能并非如此。因此,Stage-I生成的SPARQL中的大多数实体是正确的,但是它们之间的关系是不正确的。Stage-II中的图形搜索模块将SPARQL作为输入,并通过替换不正确的关系来生成相同的改进版本。这是一个基于bert的...
Paper: Temporal knowledge graph question answering via subgraph reasoning Github: GitHub - czy1999/SubGTR: Temporal Knowledge Graph Question Answering via Subgraph Reasoninggithub.com/czy1999/SubGTR TL;DR: 本文提出了一个基于子图推理的时序知识图谱问答(TKGQA)模型SubGTR以及一个改进的数据集Complex-...
Knowledge Graph Question Answering (KGQA) is a major branch of question answering tasks, which can answer fact questions effectively by using the reasonable characteristics of the knowledge graph. Currently, lots of related works combined with a variety of deep learning models are presented for the ...
Knowledge Graph Embedding Based Question Answering 本文是论文Knowledge Graph Embedding Based Question Answering的阅读笔记和个人理解. Basic Idea 作者发现KBQA中的三个挑战问题: 谓词(
A collaboration between HTWK Leipzig, HS Anhalt, FhG IAIS and Semantic Systems @ University Hamburg - Knowledge Graph Question Answering
记录下学习过NLP,就是个作业草稿 本次选取论文[1]《Knowledge Graph Embedding Based Question Answering》 论文代码网址: https://github.com/xhuang31/KEQA_WSDM19 概念介绍 Simple Question:I
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1questionAnsweringsystemQuestionAnsweringSystem是一个Java实现的人机问答系统,能够自动分析问题并给出候选答案。 2QABasedOnMedicaKnowledgeGraph从无到有搭建一个以疾病为中心的一定规模医药领域知识图谱,并以该知识图谱完成自动问答与分析服务。python 3DeepPavlovAn open source library for deep learning end-to-end dia...
Question answering is made much easier with a Knowledge Graph. Information is easily prepped for a variety of uses, including queries and search, natural language processing, machine learning algorithms, and even visualization and business intelligence.Graph...
Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large-scale Knowledge Graphs (KGs). Recently Neural Machine Translation based approaches are gaining momentum that translates natural language queries to structured query languages ...