A Hybrid KR&R/Information Theoretic Model for Relationship Simplification

证据链接和“浸渍”是在语义丰富的多源数据库中探索实体及其连接的强大分析范式。从一小组种子（如已知的可疑对象或某些探索性查询的结果）开始，分析人员可以从各种数据源中提取数据，以探索通过任意数量的关系连接到这些种子的实体空间。然而，由于通常存在与每个实体相关联的许多关系、事实、属性和事务，因此它们可以被丰富地互连，这可以快速地导致非常大量的对象链接到初始种子。关系简化是一种有效地减少该空间并通过以下方式为分析人员提供更抽象的视图的方法：（1）通过抽象和规范化减少不同关系的数量，以及（2）通过计算连接强度或相关性的度量来聚焦于强相关连接的对象。为了解决（1），我们建议使用知识表示和推理（KR R）技术，它允许我们以非常高保真的方式表示证据，利用复杂的本体和领域理论，具有表示抽象和元知识的自然方法，在不同的表示之间轻松映射，并利用强大的推理程序来显式地表示隐式关系。为了解决（2），我们需要合并和聚合两个对象之间的所有关系，将它们与其他实体之间的连接进行统计对比，并计算接近度或兴趣度的度量，以过滤掉不相关或不感兴趣的对象和连接。为了动态计算连接强度，我们建议使用信息理论模型来确定每个关系的权重，并考虑到关系的上下文。这将使我们能够聚合对象之间的所有关系，并测量它们之间的紧密程度，从而简化大型数据空间，并将其压缩为更抽象、更简化的视图。我们将把Relationship Simplifier与BLACKBOOK系统集成，以便统一访问数据和结果，并与其他组件进行通信。此外，我们的组件还可以直接从一个或多个关系数据库访问关系数据，这在处理非常大的数据集时非常有用。

Evidence chaining and "dipping" are powerful analytical paradigms to explore entities and their connections in semantically rich, multi-source databases. Starting from a small set of seeds such as known suspects or the result of some exploratory query, an analyst can draw from various data sources to explore the space of entities connected to these seeds via any number of relations. However, since there are often many relations, facts, attributes and transactions associated with each entity, they can be richly interconnected which can quickly lead to very large numbers of objects linked to the initial seeds.Relationship simplification is one approach to reduce this space effectively and provide a more abstract view for analysts by (1) reducing the number of different relations through abstraction and normalization, and (2) focusing on strongly and relevantly connected objects by computing a measure of connection strength or relevance. To address (1) we propose to use knowledge representation and reasoning (KR&R) technology, which allows us to represent evidence at very high fidelity, utilize sophisticated ontologies and domain theories, have a natural means to represent abstraction and meta-knowledge, map easily between different representations and exploit powerful inference procedures to make implicit relationships explicit. To address (2) we need to consolidate and aggregate all relations between two objects, statistically contrast them with connections to and among other entities and compute a measure of closeness or interestingness to filter out irrelevant or uninteresting objects and connections. To dynamically compute connection strength, we propose to use an information theoretical model to determine the weight of each relation as well as to take the context of a relationship into account. This will allow us to aggregate all relations between objects and measure closeness between them to simplify a large data space and compress it into a more abstract, simplified view.We will integrate the Relationship Simplifier with the BLACKBOOK system for uniform access to data and results and communication with other components. In addition, our components can also access relational data directly from one or more relational databases which is useful when dealing with very large datasets.