https://arxiv.org/pdf/1707.06690.pdf

https://github.com/xwhan/DeepPath

摘要
我们研究了在大规模知识图(KGs)中学习推理的问题。更具体地说,我们描述了一种用于学习多跳关系路径的新型强化学习框架:我们使用基于知识图嵌入的具有连续状态的基于策略的代理,其通过采样最有希望的关系来扩展它的KG向量空间路径。与之前的工作相比,我们的方法包括一个奖励功能,该功能考虑了准确性,多样性和效率。在实验上,我们表明,我们提出的方法胜过基于路径排序的算法和知识图嵌入方法Freebase和Never-Ending语言学习数据集。

1介绍

用于语音识别中声学建模的深度神经网络近年来,深度学习技术已经在各种分类和识别问题中获得了许多现成的结果(Krizhevsky et al。,2012; Hinton et al。,2012; Kim,2014)。然而,复杂的自然语言处理问题通常需要多个相互关联的决策,并且赋予深度学习模型以学习理性的能力仍然是一个具有挑战性的问题。为了处理没有明显答案的复杂查询,智能机器必须能够推理现有资源,并学会推断未知答案。

更具体地说,我们把我们的研究放在多跳推理的环境中,给出一个大的KG,这是学习显式推理公式的任务。例如,如果KG包含诸如Neymar为巴塞罗那出战的信念,而巴塞罗那在西甲联赛中,那么机器应该能够学习以下公式:playerPlaysForTeam(P,T)∧teamPlaysInLeague(T,L)⇒ playerPlaysInLeague(P,L)。在测试时间内,通过插入学习公式,系统应该能够自动推断一对实体之间的缺失链接。这种推理机可能会成为复杂QA系统的重要组成部分

近年来,路径排序算法(PRA)(Lao et al。,2010,2011a)成为大型幼儿园学习推理路径的一种有前途的方法。PRA使用基于重启的基于推理机制的随机游走来执行多个有界深度优先搜索过程来查找关系路径。加上基于弹性网络的学习,PRA然后使用监督式学习选择更合理的路径。然而,PRA在完全独立的空间中运作,这使得评估和比较KG中类似的实体和关系变得困难。
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https://arxiv.org/pdf/1707.06690.pdf

https://github.com/xwhan/DeepPath

Abstract
We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1

1 Introduction

Deep neural networks for acoustic modeling in speech recognitionIn recent years, deep learning techniques have obtained many state-of-theart results in various classification and recognition problems (Krizhevsky et al., 2012; Hinton et al., 2012; Kim, 2014). However, complex natural language processing problems often require multiple inter-related decisions, and empowering deep learning models with the ability of learning to reason is still a challenging issue. To handle complex queries where there are no obvious answers, intelligent machines must be able to reason with existing resources, and learn to infer an unknown answer.

More specifically, we situate our study in the context of multi-hop reasoning, which is the task of learning explicit inference formulas, given a large KG. For example, if the KG includes the beliefs such as Neymar plays for Barcelona, and Barcelona are in the La Liga league, then machines should be able to learn the following formula: playerPlaysForTeam(P,T) ∧ teamPlaysInLeague(T,L) ⇒ playerPlaysInLeague(P,L). In the testing time, by plugging in the learned formulas, the system should be able to automatically infer the missing link between a pair of entities. This kind of reasoning machine will potentially serve as an essential components of complex QA systems

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机器能像

人类一样思考吗?

http://gdm.fudan.edu.cn/GDMWiki/attach/Yanghuaxiao/Language%20Understanding.pdf

语言是思考的工具


使我们与动物区别开来的是语言能力和理解能力使
人类能够理解人类语言的机器是实现智能信息处理和智能机器人大脑的根本途径。

机器语言
理解的障碍
• 机器的语言理解
需要知识库
•大规模
•语义丰富
•友好结构
•传统知识表示
不能满足这些要求
•本体论
•语义网络
•文本

 
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Can machine think like
humans?

http://gdm.fudan.edu.cn/GDMWiki/attach/Yanghuaxiao/Language%20Understanding.pdf

Language is the tool of thinking


It is the ability of language speaking and understanding that distinguish us from animals
Enabling machine to understand human language is the essential path to realize intelligent information processing and smart robot brain.

Obstacles of machine language
understanding
• Language understanding of machines
needs knowledge bases
• Large scale
• Semantically rich
• Friendly structure
• Traditional knowledge representations
can not satisfy these requirements
• Ontology
• Semantic network
• Texts

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https://arxiv.org/abs/1803.07828v1
https://github.com/AKSW/KG2Vec

Tommaso Soru
1
, Stefano Ruberto
2
, Diego Moussallem
1
, Edgard Marx
1
, Diego
Esteves
3
, and Axel-Cyrille Ngonga Ngomo
4
1 AKSW, University of Leipzig, D-04109 Leipzig, Germany
{tsoru,moussallem,marx
}@informatik.uni-leipzig.de
2 Gran Sasso Science Institute, INFN, I-67100 L’Aquila, Italy
stefano.ruberto@gssi.infn.it
3
SDA, University of Bonn, D-53113 Bonn, Germany
esteves@cs.uni-bonn.de
4 Data Science Group, Paderborn University, D-33098 Paderborn, Germany
axel.ngonga@upb.de
Abstract. Knowledge Graph Embedding methods aim at representing entities
and relations in a knowledge base as points or vectors in a continuous vector
space. Several approaches using embeddings have shown promising results on
tasks such as link prediction, entity recommendation, question answering, and
triplet classification. However, only a few methods can compute low-dimensional
embeddings of very large knowledge bases. In this paper, we propose KG2VEC
,
a novel approach to Knowledge Graph Embedding based on the skip-gram model.
Instead of using a predefined scoring function, we learn it relying on Long ShortTerm
Memories. We evaluated the goodness of our embeddings on knowledge
graph completion and show that KG2VEC is comparable to the quality of the
scalable state-of-the-art approach RDF2Vec and can process large graphs by parsing
more than a hundred million triples in less than 6 hours on common hardware.\
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林开开1,沉士起1,刘志远1,2 *,栾波1,孙茂松1,2 1清华大学计算机科学与技术系,国家智能技术与系统国家重点实验室,清华大学信息科学与技术国家重点实验室,北京,中国2江苏省语言能力协作创新中心

抽象

远程监督关系提取已被广泛用于从文本中找到新的关系事实。然而,遥远的监督不可避免地伴随着错误的标签问题,这些嘈杂的数据将严重损害关系提取的性能。为了缓解这个问题,我们提出了一个关系抽取的句子级关注模型。在这个模型中,我们使用卷积神经网络来嵌入语句的语义。之后,我们在多个实例上构建语句级注意力,这样可以动态减少那些噪音实例的权重。实际数据集的实验结果表明,我们的模型可以充分利用所有信息句子,并有效减少错误标记实例的影响。与基线相比,我们的模型在关系提取方面取得了显着且一致的改进。本文的源代码可以从https://github.com/thunlp/NRE获取。

1介绍

近年来,Freebase(Bollacker et al。,2008),DBpedia(Auer et al。,2007),YAGO(Suchanek et al。,2007)等大型知识库已经建成并得到广泛应用在许多自然语言处理(NLP)任务中,包括网络搜索和问题回答。这些知识库主要由三重格式的关系事实组成,例如(微软,创始人比尔盖茨)。尽管现有的KB包含*

 

通讯作者:刘志远(liuzy@tsinghua.edu.cn)

 

与大量事实相比,与无限的现实世界事实相比,它们还远未完成。为了丰富知识库,已经投入了很多努力来自动发现未知的关系事实。因此,关系抽取(RE)是从纯文本生成关系数据的过程,是NLP中的关键任务。

 

现存最多
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Yankai Lin1 , Shiqi Shen1 , Zhiyuan Liu1,2∗ , Huanbo Luan1 , Maosong Sun1,2 1 Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology, Tsinghua University, Beijing, China 2 Jiangsu Collaborative Innovation Center for Language Competence, Jiangsu, China

Abstract

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of relation extraction. To alleviate this issue, we propose a sentence-level attention-based model for relation extraction. In this model, we employ convolutional neural networks to embed the semantics of sentences. Afterwards, we build sentence-level attention over multiple instances, which is expected to dynamically reduce the weights of those noisy instances. Experimental results on real-world datasets show that, our model can make full use of all informative sentences and effectively reduce the influence of wrong labelled instances. Our model achieves significant and consistent improvements on relation extraction as compared with baselines. The source code of this paper can be obtained from https: //github.com/thunlp/NRE.

1 Introduction

In recent years, various large-scale knowledge bases (KBs) such as Freebase (Bollacker et al., 2008), DBpedia (Auer et al., 2007) and YAGO (Suchanek et al., 2007) have been built and widely used in many natural language processing (NLP) tasks, including web search and question answering. These KBs mostly compose of relational facts with triple format, e.g., (Microsoft, founder, Bill Gates). Although existing KBs contain a ∗

 

Corresponding author: Zhiyuan Liu (liuzy@tsinghua.edu.cn).

 

massive amount of facts, they are still far from complete compared to the infinite real-world facts. To enrich KBs, many efforts have been invested in automatically finding unknown relational facts. Therefore, relation extraction (RE), the process of generating relational data from plain text, is a crucial task in NLP.

 

Most existing

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