RealPDBs: Realistic Data Models and Query Compilation for Large-Scale Probabilistic Databases

近年来，学术界和工业界对以自动化方式从数据中构建大规模概率知识库产生了浓厚的兴趣，这导致了许多系统，如DeepDive， NELL, Yago, Freebase，微软的Probase和b谷歌的knowledge Vault。这些系统不断地抓取Web并提取结构化信息，从而用数百万个实体和数十亿个元组填充它们的数据库。这些搜索和提取系统在多大程度上可以帮助现实世界的用例？这是一个开放式的问题。例如，DeepDive被用来为古生物学、地质学、医学遗传学和人类运动等领域建立知识库。从更广泛的角度来看，对构建大规模知识库的追求是人工智能研究的新曙光。信息提取、自然语言处理（如问答）、关系学习和深度学习、知识表示和推理以及数据库等领域正朝着共同的目标主动迈进。查询大规模概率知识库通常被认为是这些工作的核心。然而，除了所有这些成功案例之外，概率知识库仍然缺乏将隐藏在其中的一些有价值的知识传递给最终用户的基本机制，这严重限制了它们在实践中的潜在应用。这些问题的根源在于（元独立的）概率数据库的语义，这些数据库用于对大多数概率知识库进行编码。出于计算效率的原因，概率数据库通常基于强大的、不切实际的完整性假设，例如封闭世界假设、元独立性假设和缺乏常识性知识。这些强烈的不切实际的假设不仅会导致不想要的结果，而且还会使概率数据库在知识库学习、完成和查询方面处于薄弱的基础上。更具体地说，上述每个系统只编码了现实世界的一部分，这种描述必然是不完整的；这些系统不断地在Web上爬行，遇到新的来源，从而遇到新的事实，导致它们将这些事实添加到它们的数据库中。然而，当涉及到查询时，这些系统中的大多数都采用封闭世界假设，即，将数据库中不存在的任何事实分配为概率0，从而假定为不可能。作为一个密切相关的问题，通常的做法是将每个提取的事实视为一个独立的伯努利变量，即任何两个事实都是概率独立的。例如，一个人在一部电影中担任主角这一事实与这个人是演员这一事实是独立的，这与知识领域的基本性质相冲突。此外，当前的概率数据库缺乏（特别是本体）常识性知识，这些知识通常可以在推理中利用，从数据中推断出隐含的结果，并且对于在Web等不受控制的环境中查询大规模概率数据库通常是必不可少的。该提案的主要目标是通过更现实的数据模型来增强大规模概率数据库（从而释放其全部数据建模潜力），同时保留其计算特性。我们计划为生成的概率数据库开发不同的语义，并分析它们的计算特性和难处理性的来源。我们还计划为他们设计实用的可扩展的查询回答算法，特别是基于知识编译技术的算法，扩展现有的知识编译方法并细化新的方法，基于张量分解和神经符号知识编译。我们还将制作一个原型实现并实验评估所提出的算法。

In the recent years, there has been a strong interest in academia and industry in building large-scale probabilistic knowledge bases from data in an automated way, which has resulted in a number of systems, such as DeepDive, NELL, Yago, Freebase, Microsoft's Probase, and Google's Knowledge Vault. These systems continuously crawl the Web and extract structured information, and thus populate their databases with millions of entities and billions of tuples. To what extent can these search and extraction systems help with real-world use cases? This turns out to be an open-ended question. For example, DeepDive is used to build knowledge bases for domains such as paleontology, geology, medical genetics, and human movement. From a broader perspective, the quest for building large-scale knowledge bases serves as a new dawn for artificial intelligence research. Fields such as information extraction, natural language processing (e.g., question answering), relational and deep learning, knowledge representation and reasoning, and databases are taking initiative towards a common goal. Querying large-scale probabilistic knowledge bases is commonly regarded to be at the heart of these efforts.Beyond all these success stories, however, probabilistic knowledge bases still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which seriously limits their potential applications in practice. These problems are rooted in the semantics of (tuple-independent) probabilistic databases, which are used for encoding most probabilistic knowledge bases. For computational efficiency reasons, probabilistic databases are typically based on strong, unrealistic completeness assumptions, such as the closed-world assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These strong unrealistic assumptions do not only lead to unwanted consequences, but also put probabilistic databases on weak footing in terms of knowledge base learning, completion, and querying. More specifically, each of the above systems encodes only a portion of the real world, and this description is necessarily incomplete; these systems continuously crawl the Web, encounter new sources, and consequently new facts, leading them to add such facts to their database. However, when it comes to querying, most of these systems employ the closed-world assumption, i.e., any fact that is not present in the database is assigned the probability 0, and thus assumed to be impossible. As a closely related problem, it is common practice to view every extracted fact as an independent Bernoulli variable, i.e., any two facts are probabilistically independent. For example, the fact that a person starred in a movie is independent from the fact that this person is an actor, which is in conflict with the fundamental nature of the knowledge domain. Furthermore, current probabilistic databases lack (in particular ontological) commonsense knowledge, which can often be exploited in reasoning to deduce implicit consequences from data, and which is often essential for querying large-scale probabilistic databases in an uncontrolled environment such as the Web. The main goal of this proposal is to enhance large-scale probabilistic databases (and so to unlock their full data modelling potential) by more realistic data models, while preserving their computational properties. We are planning to develop different semantics for the resulting probabilistic databases and analyse their computational properties and sources of intractability. We are also planning to design practical scalable query answering algorithms for them, especially algorithms based on knowledge compilation techniques, extending existing knowledge compilation approaches and elaborating new ones, based on tensor factorisation and neural-symbolic knowledge compilation. We will also produce a prototype implementation and experimentally evaluate the proposed algorithms.