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Reasoning over Large Amounts of Data in Ontologies via Abstraction and Refinement

通过抽象和细化对本体中的大量数据进行推理

基本信息

批准号：
266736200
负责人：
Professorin Dr. Birte Glimm
金额：
--
依托单位：
Institut für Künstliche Intelligenz (KI)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/266736200?language=en
关键词：
Reasoning over Large Amounts Data

项目摘要

Ontology based data access (OBDA) is an increasingly popular paradigm in the area of knowledge representation and information systems. An ontology in this context is a combination of a TBox with background domain knowledge and an ABox, which contains facts about elements of the application domain. The TBox is used to enrich and integrate large, incomplete, and possibly semi-structured data, which users can then access via queries. For example, a large part of Wikipedia is available in machine-processable form, which, together with an ontological TBox, is an important information source for many applications. To efficiently handle large ABoxes, OBDA approaches assume that the data is stored in a database. Nevertheless, the assumption of complete data that is typically made in databases (closed world assumption) does not hold and reasoning is required to answer queries. A standard reasoning approach is materialization, i.e., all entailed consequences are added to the ABox before the system accepts queries. For large ABoxes, however, the materialization can take several hours.Within this project extension, we suggest a novel approach to materialization, where we do not compute the materialization directly on the (usually large) ABox, but where we work instead on a smaller ``abstraction'' of the data. For the abstraction, we define criteria under which individuals from the ABox are considered equivalent. Such indistinguishable individuals are then represented just once in the abstraction. For TBoxes that are small compared to the ABox, the abstraction is usually significantly smaller than the original ABox and, hence, the entailed consequences can be computed efficiently in main-memory. Through the entailed consequences individuals that were indistinguishable may become distinguishable. To account for that, the initial abstraction is iteratively refined until a fix-point is reached. The results obtained so far are to be extended in several directions: 1) The developed technique for handling disjunctions is to be extended to more expressive ontology languages (while still guaranteeing soundness and completeness). 2) Relevant parts of the abstraction that must be refined, are to be identified and incrementally treated in order to minimize the communication with the database backend. 3) Based on the incremental refinements we plan to develop techniques for handling updates to the ontology. 4) The abstraction approach seems well-suited for improving the ontology debugging process in particular for large ABoxes that are learned from text via the generation of explanations directly from the abstraction. The proposed project supports the efficient use of the ever growing sources of structured data by combining well-established database technologies with in-memory-based reasoning techniques in a novel way.

基于本体的数据访问（OBDA）是知识表示和信息系统领域日益流行的范式。在此上下文中，本体是具有背景领域知识的TBox和ABox的组合，ABox包含关于应用领域元素的事实。TBox用于丰富和集成大型、不完整和可能的半结构化数据，然后用户可以通过查询访问这些数据。例如，维基百科的很大一部分是以机器可处理的形式提供的，它与本体论TBox一起是许多应用程序的重要信息源。为了有效地处理大型ABoxes，OBDA方法假设数据存储在数据库中。然而，通常在数据库中进行的完整数据的假设（封闭世界假设）并不成立，并且需要推理来回答查询。一个标准的推理方法是物化，即，在系统接受查询之前，将所有必然的结果添加到ABox。然而，对于大的ABox，物化可能需要几个小时。在这个项目扩展中，我们提出了一种新的物化方法，我们不直接在（通常很大的）ABox上计算物化，而是在更小的数据“抽象”上工作。对于抽象，我们定义了标准，根据这些标准，来自ABox的个体被认为是等价的。这样的不可区分的个体在抽象中只被表示一次。对于比ABox小的TBox，抽象通常比原始ABox小得多，因此，可以在主存中有效地计算所包含的结果。通过所产生的后果，原本难以区分的个人可能会变得可以区分。为了说明这一点，初始抽象被迭代地细化，直到达到固定点。到目前为止所获得的结果将在几个方向上扩展：1）开发的处理析取的技术将扩展到更具表达力的本体语言（同时仍然保证可靠性和完整性）。2)抽象的相关部分必须被细化，必须被识别和增量地处理，以最小化与数据库后端的通信。3)基于增量改进，我们计划开发技术处理本体的更新。4)抽象方法似乎非常适合于改进本体调试过程，特别是对于通过直接从抽象生成解释从文本中学习的大型ABoxes。拟议的项目支持结构化数据的不断增长的来源的有效利用相结合的成熟的数据库技术与基于内存的推理技术在一个新的方式。