【论文推荐】最新六篇知识图谱相关论文—全局关系嵌入、时序关系提取、对抗学习、远距离关系、时序知识图谱

【导读】专知内容组整理了最近六篇知识图谱(Knowledge Graph)相关文章,为大家进行介绍,欢迎查看!

1. Approaches for Enriching and Improving Textual Knowledge Bases(丰富和改进文本知识库的方法)

作者:Besnik Fetahu

机构:der Gottfried Wilhelm Leibniz Universität Hannover

摘要:Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added statements. Statements can be any arbitrary piece of text, ranging from a sentence up to a paragraph. However, in many cases, citations are either outdated, missing, or link to non-existing references (e.g. dead URL, moved content etc.). In total, 20\% of the cases such citations refer to news articles and represent the second most cited source. Even in cases where citations are provided, there are no explicit indicators for the span of a citation for a given piece of text. In addition to issues related with the verifiability principle, many Wikipedia entity pages are incomplete, with relevant information that is already available in online news sources missing. Even for the already existing citations, there is often a delay between the news publication time and the reference time. In this thesis, we address the aforementioned issues and propose automated approaches that enforce the verifiability principle in Wikipedia, and suggest relevant and missing news references for further enriching Wikipedia entity pages.

期刊:arXiv, 2018年4月20日

网址

http://www.zhuanzhi.ai/document/d414a76c4b97a6c3c04e89e5c79cf28e

2. Global Relation Embedding for Relation Extraction(关系提取的全局关系嵌入)

作者:Yu Su,Honglei Liu,Semih Yavuz,Izzeddin Gur,Huan Sun,Xifeng Yan

机构:University of California

摘要:We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%.

期刊:arXiv, 2018年4月19日

网址

http://www.zhuanzhi.ai/document/f18e8240ce087b972a8c9f67059d6826

3.Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource(利用全局获得的统计资源改善时序关系提取)

作者:Qiang Ning,Hao Wu,Haoruo Peng,Dan Roth

机构:University of Illinois at Urbana-Champaign

摘要:Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource -- a probabilistic knowledge base acquired in the news domain -- by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987--2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.

期刊:arXiv, 2018年4月17日

网址

http://www.zhuanzhi.ai/document/0f06d52ab1185faaf2f85cfdc70f1c76

4.KBGAN: Adversarial Learning for Knowledge Graph Embeddings(KBGAN:基于对抗学习的知识图谱嵌入)

作者:Liwei Cai,William Yang Wang

机构:University of Washington

摘要:We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TransE and TransD, each with assistance from one of the two probability-based models, DistMult and ComplEx. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.

期刊:arXiv, 2018年4月16日

网址

http://www.zhuanzhi.ai/document/29c85bf5138945822db0c2ed07173bde

5.CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web(CERES:从半结构化的网络中提取远距离关系)

作者:Colin Lockard,Xin Luna Dong,Arash Einolghozati,Prashant Shiralkar

机构:University of Washington

摘要:The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.

期刊:arXiv, 2018年4月13日

网址

http://www.zhuanzhi.ai/document/d0062d1b0b884311f9141d9db22bb1c3

6.EventKG: A Multilingual Event-Centric Temporal Knowledge Graph(EventKG:一个多语言以事件为中心的时序知识图谱)

作者:Simon Gottschalk,Elena Demidova

机构:Leibniz Universit¨at Hannover

摘要:One of the key requirements to facilitate semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. EventKG presented in this paper is a multilingual event-centric temporal knowledge graph that addresses this gap. EventKG incorporates over 690 thousand contemporary and historical events and over 2.3 million temporal relations extracted from several large-scale knowledge graphs and semi-structured sources and makes them available through a canonical representation.

期刊:arXiv, 2018年4月12日

网址

http://www.zhuanzhi.ai/document/04a517ce46aeaca5970203a50e07d926

-END-

原文发布于微信公众号 - 专知(Quan_Zhuanzhi)

原文发表时间:2018-04-24

本文参与腾讯云自媒体分享计划,欢迎正在阅读的你也加入,一起分享。

发表于

我来说两句

0 条评论
登录 后参与评论

相关文章

来自专栏生信技能树

lncRNA实战项目-第五步-差异表达的mRNA和lncRNA

上一步骤得到了表达矩阵,两个样本分别是F_1yr.OC和M_1yr.OC, 所以接下来的差异分析就是比较1岁猕猴脑OC区域女性和男性的差别,差异分析的分析方法很...

1.1K4
来自专栏大数据挖掘DT机器学习

详细步骤:用R语言做文本挖掘

目录 Part1 安装依赖包 Part2 分词处理 Part3文本聚类 Part4 文本分类 Part5情感分析 Part1 安装依赖包 R语言中中文分析的...

82712
来自专栏机器学习人工学weekly

机器学习人工学weekly-2018/3/17

1. PyTorch构架分析 PyTorch – Internal Architecture Tour 链接:http://blog.christianper...

3177
来自专栏量子位

海量ICLR论文点评公开,用这几个工具可以读得更轻松

允中 李林 编译整理 量子位 出品 | 公众号 QbitAI NIPS 2017开幕在即,这两天twitter上却在热火朝天地聊着还有点遥远的ICLR 2018...

2283
来自专栏生信技能树

第41周生信文献分享:肝癌复发的CpG甲基化信号特征

前面我们讲解了一篇2013年多组学数据探索乳腺癌细胞系药物敏感性使用的也是两个机器学习算法,不过是LS-SVM和RF,但是也有借鉴意义。

1842
来自专栏量化投资与机器学习

【连载干货】中国人民大学统计数据挖掘中心专题报告资料之线性判别、Logistic回归

谢谢大家支持,可以让有兴趣的人关注这个公众号。让知识传播的更加富有活力,谢谢各位读者。 很多人问我为什么每次的头像是奥黛丽赫本,我只能说她是我女神,每天看看女神...

3218
来自专栏PPV课数据科学社区

决策树:使用SPSS分析银行拖欠货款用户的特征

前两文章,已经从理论上解释了构造决策树进行分类的做法。 下面将利用工具SPSS来实现决策树分类。 案例: 某银行收集了2064个银行货款客户的信息,并且标识出...

3566
来自专栏Y大宽

Cytoscape插件7:MCODE

MCODE,Molecular COmplex Detection 发现PPI网络中紧密联系的regeions,这些区域可能代表分子复合体。 根据给定的参数...

9512
来自专栏Crossin的编程教室

Python+OpenCV 十几行代码模仿世界名画

现在很多人都喜欢拍照(自拍)。有限的滤镜和装饰玩多了也会腻,所以就有 APP 提供了模仿名画风格的功能,比如 prisma、versa 等,可以把你的照片变成 ...

3533
来自专栏大数据智能实战

基于tensorflow的人脸识别技术(facenet)的测试

人脸识别的应用非常广泛,而且进展特别快。如LFW的评测结果上已经都有快接近99.9%的。 Uni-Ubi60 0.9900 ± 0.0032 FaceNet62...

1.3K7

扫码关注云+社区

领取腾讯云代金券