III: Small: Using Knowledge Resources to Improve Information Retrieval

目前的搜索引擎了解人类如何使用语言，但不了解语言本身。它们将查询中的单词与文档中的单词以及以某种方式链接到文档的单词（例如，“单击此处获取员工手册”）进行匹配，以查找可能满足查询的文档。然后，他们使用统计方法和搜索类似信息的其他人的行为来对这些潜在的匹配进行排名。虽然目前的技术在大多数情况下运行良好，但它有时会严重失败，因为搜索引擎并不真正理解它排名的文档的含义。最近，公司、研究机构和志愿者社区已经开始创建大型知识图谱来描述重要的、必要的或众所周知的信息。知识图谱在精神上与维基百科相似，但它们是为计算机而不是人类设计的。例如，知识图谱可能包含实体“克利夫兰骑士”和“勒布朗詹姆斯”，这两个实体可能通过“雇佣”关系连接起来。信息可以由具有中等专业知识的人输入，也可以通过机器学习软件输入，因此构建涵盖广泛人类知识的大型知识图谱是可行的。Freebase现在归谷歌所有，它是一个著名的知识图谱，包含25亿个“事实”，大约4400万个“主题”，并且正在迅速增长。目前，知识图谱仅用于一些定义明确的任务，例如，生成谷歌在某些搜索结果旁边显示的信息框。将知识图谱用于更多不同任务的新方法具有重要的科学和商业意义。该项目开发了使用知识图谱的新方法，以提高搜索引擎的准确性，特别是对于模糊，模棱两可或指定不明确的查询。搜索引擎使用知识图来识别查询词的可能含义，然后使用这些知识来提高识别与这些含义匹配的文档的能力。该项目具有实际意义，因为它有可能提高搜索引擎对当前困难查询的准确性。它具有科学意义，因为它有可能为搜索引擎注入对意义和关系的更多理解。该项目具有教育意义，因为它为研究生提供了做课堂项目和独立研究的机会，从而参加美国国家标准与技术研究院（NIST）的年度TREC会议，这是一个半竞争性的年度活动，吸引了来自世界各地的一些最好的研究小组。知识图谱的结构化程度低于典型的关系数据库和语义网络资源，但比存储在全文搜索引擎中的文本更结构化。这些半结构化信息资源中使用的弱语义足以支持有趣的应用程序，但也能够容纳矛盾、不一致和错误，这使得它们更容易扩展到大量信息。半结构化资源的典型使用将其视为具有某种限制功能的结构化资源。应用程序必须理解与它使用的每种类型的实体、属性和关系相关联的语义。尽管这种方法是有效的，但是由于需要理解实体类型和关系类型的语义，限制了应用程序在资源发展和增长时自动合并新类型信息的能力。该项目开发了使用半结构化信息资源的新方法，这些方法对半结构化知识资源的结构和语义做出更少的假设，从而使他们能够随着资源的增长和演变而充分利用资源。资源被视为实体和关系的网络，每个实体和关系都由“词包”描述来描述。实体和关系使用全文检索方法的扩展进行检索。诸如权威评估或相关语言模型之类的证据可以与实体和关系类型相关联，并沿着特定的网络链接传播，以改进实体和关系模型。本项目应用这种通用体系结构对全文搜索引擎的准确性进行了若干改进，例如，提供了一种回答实体属性查询的替代方法和一种更稳定、更有效的查询扩展方法。研究成果通过科学出版物、开源软件和项目网站（http://www.cs.cmu.edu/~callan/Projects/IIS-1422676/）传播。

Current search engines understand how humans use language, but they do not understand the language itself. They match the words in a query to the words in a document and words that are linked somehow to the document (e.g., "Click here to get the employee handbook") to find documents that might satisfy the query. Then they use statistical methods and the behavior of other people who searched for similar information to rank these potential matches. Although current technology works well most of the time, it sometimes fails badly because the search engine does not really understand the meanings of the documents that it ranks. Recently, companies, research organizations, and volunteer communities have begun to create large knowledge graphs that describe important, essential, or well-known information. Knowledge graphs are similar in spirit to Wikipedia, but they are designed to be used by computers instead of humans. For example, a knowledge graph might contain the entities "Cleveland Cavaliers" and "LeBron James", and these two entities might be connected by an "employs" relationship. Information can be entered by people with moderate expertise, and by machine learning software, thus it is practical to build large knowledge graphs that cover a wide range of human knowledge. Freebase, which is now owned by Google, is a well-known knowledge graph that contains 2.5 billion "facts" about 44 million "topics" and is growing rapidly. Currently knowledge graphs are used for just a few well-defined tasks, for example, to produce the info boxes that Google displays next to some search results. New methods of using knowledge graphs for more varied tasks are of significant scientific and commercial interest. This project develops new methods of using knowledge graphs to improve the accuracy of search engines, especially for vague, ambiguous, or poorly-specified queries. The search engine uses the knowledge graph to identify the probable meanings of query terms, and then uses this knowledge to improve its ability to identify documents that match those meanings. The project is of practical significance for its potential to improve search engine accuracy on queries that are currently difficult. It is of scientific significance for its potential to inject greater understanding of meaning and relationships into search engines. The project is of educational significance because it provides opportunities for graduate student to do class projects and independent studies that lead to participation in the National Institute of Standards and Technology's (NIST) annual TREC conference, which is a semi-competitive annual event that attracts some of the best research groups from around the world.Knowledge graphs are less structured than typical relational databases and semantic web resources but more structured than the text stored in full-text search engines. The weak semantics used in these semi-structured information resources is sufficient to support interesting applications, but is also able to accommodate contradictions, inconsistencies, and mistakes, which makes them easier to scale to large amounts of information. The typical use of a semi-structured resource treats it like a structured resource that has somewhat restricted functionality. The application must understand the semantics associated with each type of entity, attribute, and relation that it uses. Although this approach is effective, the need to understand the semantics of entity types and relation types limits the application's ability to automatically incorporate new types of information as the resource evolves and grows. This project develops new methods of using semi-structured information resources that make fewer assumptions about the structure and semantics of a semi-structured knowledge resource, thus enabling them to make full use of the resource as it grows and evolves. The resource is treated as a network of entities and relations that are each described by a "bag of words" description. Entities and relations are retrieved using extensions of full-text retrieval methods. Evidence such as estimates of authority or related language models can be associated with entity and relation types, and propagated along specific network links to improve entity and relation models. This project applies this general architecture to make several improvements in the accuracy of a full-text search engine, for example, providing an alternative method of answering entity-attribute queries and a more stable and effective method of query expansion. Research results are disseminated through scientific publications, open-source software, and the project's web site (http://www.cs.cmu.edu/~callan/Projects/IIS-1422676/).