Knowledge Graph Embedding当KGE训练完成时, 实体和关系的表示将会固定下来, 这样才能保存住KG的信息. 若继续在后续训练时更新Embedding, 将会对原有信息扰动. 所以KGE只是做了空间指示作用.Neural Network Based Predicate Representation Learning有了KGE中获取的 P, E, 接着需要将自然语言中的谓词表示(在三元组中也...
3 KNOWLEDGE EMBEDDING BASED QA-KG 3.1 Knowledge Graph Embedding 3.2 Predicate and Head Entity Learning Models 3.3 Head Entity Detection Model 3.4 Joint Search on Embedding Spaces 4 EXPERIMENTS 5 RELATEDWORK 6 CONCLUSIONS AND FUTUREWORK ABSTRACT 基于知识图谱的问答致力于使用知识图谱中的事实回答自然语言问...
Knowledge Base Question Answering (KBQA) has been along-standingfield to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA), an emerging task to answer temporal questions. Howev...
Knowledge Graph-based Question Answering with Electronic Health Records.Junwoo ParkYoungwoo ChoHaneol LeeJaegul ChooEdward ChoiPMLR
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:If a natural language question only involves a single head entity and a single predicate in the knowledge graph, and takes their tail entity/entiti...
Liu Z, Li K, Qu D. Knowledge graph based question routing for community question answering. In Proc. International Conference on Neural Information Processing, Nov. 2017, pp. 721-730.Liu Z, Li K, Qu D. Knowledge graph based question rout- ing for community question answering. In Proc. ...
It creates the graph of the information extracted using NetworkX.The source and destination are used as nodes whereas relations are used as the edges. qna.py This involves matching of the entity pairs from the JSON file and that from the question usingfindanswer(question)function. ...
A collaboration between HTWK Leipzig, HS Anhalt, FhG IAIS and Semantic Systems @ University Hamburg - Knowledge Graph Question Answering
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-CronQuestions,取得了最优的效果。
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...