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BIGDATA: F: Open-World Foundations for Big Uncertain Data

BIGDATA：F：大不确定数据的开放世界基础

基本信息

批准号：
1633857
负责人：
Guy Van den Broeck
金额：
$ 43.22万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633857&HistoricalAwards=false
关键词：
BIGDATA Open World Foundations Big

项目摘要

Driven by the need to learn from vast amounts of text data, efforts throughout natural language processing, information extraction, databases, and AI are coming together to build large-scale knowledge bases. These systems continuously crawl the web to extract relational data from text, and have already populated their databases with millions of entities and billions of tuples. Large-scale probabilistic knowledge bases are revolutionizing the way we access data. They are now routinely used by scientists to build knowledge bases of publications, by law enforcement to extract information from the dark web, and by regular search engine users who find their results augmented with structured information. Such knowledge bases are inherently probabilistic: to go from raw text to structured data, a sequence of statistical machine learning techniques associate probabilities with database tuples. This project revisits the semantics underlying such systems, and provide a more adequate foundational framework. In particular, the closed-world assumption of probabilistic databases, that facts not in the database have probability zero, clearly conflicts with their everyday use, and obstructs the progress in this area.More specifically, this project develops a new semantic foundation based on the open-world assumption, that facts not in the database are possible, but have unknown probability. It designs the basic algorithms for query answering in this setting, both exact and approximate. Moreover, in a deep theoretical component, this project studies fundamental questions of data and domain complexity that are unique to open-world reasoning about big uncertain data. Finally it develops proof-of-concept applications in machine learning and data mining, and additional knowledge-representation layers that strengthen open-world reasoning. The developed semantics provide meaningful answers when some tuple probabilities are not precisely known. The developed algorithms allow for efficient query answering, even when reasoning about the open world, in time linear in the database size for tractable queries. This project provides a scientific leap at the fundamental, semantic level. It also provides a context for training undergraduate and graduate students in subjects spanning databases, artificial intelligence, theory, and machine learning, and will target the integration of probabilistic knowledge bases into computer science curricula.

在从大量文本数据中学习的需求的推动下，自然语言处理、信息提取、数据库和人工智能正在共同努力构建大规模的知识库。这些系统不断地抓取网络以从文本中提取关系数据，并且已经在其数据库中填充了数百万个实体和数十亿个元组。大规模概率知识库正在彻底改变我们访问数据的方式。现在，科学家经常使用它们来建立出版物的知识库，执法部门使用它们从暗网上提取信息，普通搜索引擎用户则发现他们的结果增加了结构化信息。这样的知识库本质上是概率性的：为了从原始文本到结构化数据，一系列统计机器学习技术将概率与数据库元组相关联。该项目重新审视了此类系统背后的语义，并提供了更充分的基础框架。特别是概率数据库的封闭世界假设，即不在数据库中的事实的概率为零，显然与其日常使用相冲突，并阻碍了该领域的进展。更具体地说，该项目基于开放世界假设开发了一个新的语义基础，即不在数据库中的事实是可能的，但具有未知的概率。它设计了在此设置中进行查询应答的基本算法，包括精确的和近似的。此外，在深入的理论部分，该项目研究了数据和领域复杂性的基本问题，这些问题是关于大不确定数据的开放世界推理所特有的。最后，它开发了机器学习和数据挖掘中的概念验证应用程序，以及增强开放世界推理的附加知识表示层。当某些元组概率未知时，所开发的语义提供了有意义的答案。所开发的算法可以实现高效的查询应答，即使在推理开放世界时，也可以在时间上与数据库大小呈线性关系，以实现易于处理的查询。该项目在基础语义层面上实现了科学飞跃。它还为培训本科生和研究生提供数据库、人工智能、理论和机器学习等学科的背景，并将目标是将概率知识库整合到计算机科学课程中。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings

DOI：
发表时间：
2020-02
期刊：
ArXiv
影响因子：
0
作者：
Tal Friedman;Guy Van den Broeck
通讯作者：
Tal Friedman;Guy Van den Broeck

New Liftable Classes for First-Order Probabilistic Inference

用于一阶概率推理的新可提升类

DOI：
发表时间：
2016
期刊：
Advances in Neural Information Processing Systems 29 (NIPS
影响因子：
0
作者：
Kazemi, Seyed Mehran;Kimmig, Angelika;Van den Broeck, Guy;Poole, David
通讯作者：
Poole, David

On Tractable Computation of Expected Predictions

DOI：
发表时间：
2019-10
期刊：
ArXiv
影响因子：
0
作者：
Pasha Khosravi;YooJung Choi;Yitao Liang;Antonio Vergari;Guy Van den Broeck
通讯作者：
Pasha Khosravi;YooJung Choi;Yitao Liang;Antonio Vergari;Guy Van den Broeck

On Constrained Open-World Probabilistic Databases