Investigation of the viability of direct evaluation of natural language queries with respect to event based triplestores

网络包含了大量的知识；大部分用自然语言（NL）文本编码。为了便于计算机处理，已经开展了几个项目，从文本中提取数据，以通用格式对其进行编码，并将其存储在灵活的数据结构中，这些数据结构被称为二进制关系存储、基于图形的数据库或三重存储，其中实体和关系被赋予称为统一资源标识符（uri）的全球唯一名称。这些灵活的数据结构统称为“语义网”。uri允许世界各地的人添加关于同一实体的数据。triplestore的一个例子是DBpedia，它从Wikipedia的web知识库中提取了大约30亿个事实。***可以使用SPARQL（类似于关系数据库查询语言SQL）对语义web进行查询。SPARQL需要时间来学习，而且对很多人来说并不容易。***这将是有用的，如果三重存储可以使用NL访问。很少或没有计算机专业知识的用户可以用他们喜欢的自然语言提问。建立了几个面向语义网的自然语言查询接口（nlqi）；大多数将NL查询转换为SPARQL。由于后面将解释的原因，现有的NLQI无法容纳包含任意嵌套的量化、否定和链接的复杂介词短语的复杂查询，例如“谁用望远镜发现了绕火星运行的卫星？”、“哪个行星没有卫星环绕？”“***另一种方法是将NL查询视为lambda演算的表达式，并直接对存储的数据进行计算。这类似于计算算术表达式的方式。对于NL查询，一些单词表示从三元组中检索到的值，一些单词表示数学函数，函数应用的顺序是通过使用表示查询的语言的语法解析查询来确定的。***在过去的两年里，我们为各种英语单词开发了一套含义（语义），用于语义网查询处理。作为概念验证，我们将语义与英语解析器集成在一起，并构建了一个原型接口，用于远程三重存储，其中包含有关行星和卫星的6000个事实。查询处理器可以处理数以百万计的查询，例如“尼科尔森或霍尔在1938年用望远镜在mt_wilson发现了木星轨道上的哪个真空卫星？”（后面会给出更多的例子）。查询接口可在以下网址访问：http://speechweb2.cs.uwindsor.ca/solarman2/demo_sparql.html***The接下来的步骤是：1)调查我们的方法在大规模三元存储（如DBpedia）中使用的可行性，2)调查如何帮助克服用户缺乏用于命名实体的uri知识的问题，以及3)使用已建立的基准查询集调查召回率和精度方面的性能。********

The web contains a huge amount of knowledge; much encoded in natural-language (NL) text. To facilitate processing by computer, several projects have been launched to extract data from text, encode it in a common format and store it in flexible data structures, variously called binary-relational stores, graph-based databases or triplestores, in which entities and relationships are given worldwide unique names called Uniform Resource Identifiers (URIs). These flexible data structures are collectively called the “semantic web”. The URIs enable data about the same entity to be added by people across the world. An example triplestore is DBpedia with approximately 3 billion facts extracted from the Wikipedia web knowledge base.***The semantic web can be queried using the formal query language SPARQL (similar to the relational database query language SQL). SPARQL requires time to learn and it is not easy for many people.***It would be useful if triplestores could be accessed using NL. Users with little or no computer expertise could then ask questions in their preferred NL. Several NL query interfaces (NLQIs) to the semantic web have been built; most convert the NL query to SPARQL. For reasons that are explained later, no existing NLQI can accommodate complex queries containing arbitrarily-nested quantification, negation, and chained complex prepositional phrases, such as in the queries “who discovered a moon that orbits mars with a telescope?”, and “which planet is orbited by no moon?”***An alternative approach is to treat NL queries as expressions of the lambda calculus and evaluate them directly with respect to the stored data. This is similar to the way in which arithmetic expressions are evaluated. For NL queries, some words denote values which are retrieved from the triplestores, some words denote mathematical functions, and the order of function application is determined by parsing the query using a grammar for the language in which the query was expressed.***Over the last 2 years, we have developed a set of meanings (a semantics) for various words of English for use in Semantic-web query processing. As proof of concept, we have integrated our semantics with an English parser, and have built a prototype interface to a remote triplestore containing 6,000 facts about the planets and moons. The query processor can handle millions of queries such as “which vacuumous moon that orbits jupiter was discovered by nicholson or hall with a telescope in 1938 in mt_wilson?” (more examples given later). The query interface is accessible at: http://speechweb2.cs.uwindsor.ca/solarman2/demo_sparql.html***The next steps are to 1) investigate the viability of our approach for use with massive triplestores, such as DBpedia, 2) to investigate how to help overcome users lack of knowledge of the URIs used for naming entities, and 3) to investigate performance with respect to recall and precision using established benchmark query sets.********