III: Small: Collaborative Research: Generalizable Similarity and Proximity Metrics for Data Exploration

知识库将信息组织到实体图中，数据探索算法可以利用这些图的数学属性来发现关于实体及其关系的有趣和有用的见解。例如，数据探索算法可以使用谷歌知识库的图来识别有共同兴趣的人，可以通过分析Genome知识库的图来发现具有相似行为的基因。目前，数据探索工具往往对这些图中如何表示信息的细节非常敏感，这使得这些工具在某些表示选择上非常有效，但在其他选择上却不那么有效。因此，数据探索在很大程度上仍然是专家和数据科学家的领域。该项目旨在克服这种依赖，并启用新一代更通用的数据探索工具，普通用户可以使用这些工具自行探索数据，而无需专家陪伴。更具体地说，这个项目正在创建有效的相似度和接近度搜索算法，这些算法可以在底层知识库的各种表示选择上提供相同的结果。该项目的关键思想是使用统计度量来量化实体或模式之间的相似程度，这种方式对数据的特定表示不敏感。这种新颖的理论框架作为更通用的数据探索算法的基础，其通用性和有效性正在大型现实世界的知识库中得到验证。

Knowledge bases organize information into graphs of entities, and data exploration algorithms can leverage mathematical properties of these graphs to discover interesting and useful insights about the entities and their relationships. For example, data exploration algorithms can use the graph of Google Knowledge Base to identify people who have common interests, and can discover genes with similar behavior by analyzing the graph of the Genome Knowledge Base. Currently, data exploration tools tend to be quite sensitive to the details of how information is represented in these graphs, making the tools highly effective over some choices of representation but not so effective with others. As a result, data exploration has largely remained the province of experts and data scientists. This project seeks to overcome this dependency and enable a new generation of more general data exploration tools that ordinary users can use to explore data on their own, without an expert by their side.More specifically, this project is creating effective similarity and proximity search algorithms that deliver the same results over various choices of representation for the underlying knowledge base. The key idea of the project is to use statistical metrics to quantify the degree of similarity between entities or patterns, in a manner that is not sensitive to the specific representation of the data. This novel theoretical framework serves as the foundation of more general data exploration algorithms, whose generality and effectiveness is being validated on large real-world knowledge bases.