ConDOR: Consequence-Driven Ontology Reasoning

本体和基于本体的词汇表正变得越来越重要。它们提供了一个共同的词汇表，以及通过与其他术语的关系对相关术语含义的计算机可访问的描述。例如，在描述人体解剖学的本体中，词汇表可以包括诸如[器官]、[循环系统]、[心脏]等的术语，并且可以将术语[肌肉器官]定义为是[肌肉系统]的一部分的[器官]，并且将术语[心脏]定义为是[循环系统]的一部分的[肌肉器官]。本体在语义网和e-Science中发挥主要作用，在e-Science中它们被广泛地用于，例如，生物信息学、医学术语和其他知识管理应用。本体论最重要的方面之一是它们包含以特殊方式结构化的知识。本体的用户通常对获得关于本体中描述的概念之间的关系的信息以及查询本体感兴趣。这两项任务都需要推理工具，可以从本体中明确说明的知识中获得新的知识。例如，推理工具应该能够推导出[心脏]是[肌肉系统]的一部分，这在解剖学本体中没有明确说明，但却是上述[心脏]和[肌肉器官]定义的逻辑结果。大多数现有本体推理器没有明确推导出本体公理的逻辑结果，而是检查是否有可能构建目标结果不成立的本体模型，例如，他们试图构建一个[心脏]是[循环系统]的一部分而不是[肌肉系统]的一部分的情况。如果这种情况是不可能的，那么可以得出结论，目标结果遵循本体中的公理。这种技术的一个问题是，当本体表达术语之间的长的且可能循环的依赖关系时，例如，[心]是[循环系统]的一部分，[肺]是[呼吸系统]的一部分，[气管]等。那么推理器必须构造非常大的模型。对于一些现有的医学本体，模型是如此之大，以至于它们不适合计算机的主存储器。另一个问题是本体可能潜在地具有大量不同的模型，其中的每一个模型必须由推理机独立地探索。本体语言提供了称为“数量限制”的构造器，这导致了特别大量的模型。数字限制用于在本体中指定定量信息，并且经常用于生物化学本体，例如表示[乙醇]分子包含{正好6}个[氢原子]。因此，模型构建推理机的这些局限性给大型医学和生物化学本体的开发带来了严重的问题--没有有效的推理工具，例如，在这个项目中，我们研究了另一种结果驱动的推理过程，这种推理过程不建立模型，而是显式地导出本体的逻辑结果。公理我们的初步调查表明，上述两个问题都可以避免后果驱动的推理过程：没有必要保持跟踪的大型模型，和本体公理的逻辑后果的数量通常是远远小于的大小和数量的模型。

Ontologies, and ontology based vocabularies, are becoming increasingly important. They provide a common vocabulary together with computer accessible descriptions of the meaning of relevant terms through relationships with other terms. For example, in an ontology describing human anatomy the vocabulary could include terms such as [Organ], [Circulatory System], [Heart], etc., and one can define a term [Muscular Organ] as an [Organ] that is a part of the [Muscular System] and a term [Heart] as a [Muscular Organ] that is a part of the [Circulatory System].Ontologies play a major role in the Semantic Web and in e-Science where they are widely used in, e.g., bio-informatics, medical terminologies and other knowledge management applications. One of the most important aspects of ontologies is that they contain knowledge structured in a special way. The users of ontologies are typically interested in obtaining information about relationships between concepts described in ontologies and querying the ontologies. Both tasks require reasoning tools that can derive new knowledge from the knowledge explicitly stated in ontologies. For example a reasoning tool should be able to derive that [Heart] is a part of the [Muscular System] which is not explicitly stated in the anatomical ontology but is a logical consequence of the above definitions for [Heart] and [Muscular Organ].Most existing ontology reasoners do not derive logical consequences of ontological axioms explicitly, but instead they check whether it is possible to construct a model of the ontology where the target consequence does not hold, e.g., they try to construct a situation where [Heart] would be a part of the [Circulatory System] but not a part of the [Muscular System]. If such a situation is not possible, then it is concluded that the target consequence follows from the axioms in the ontology. One problem with this technique is that when an ontology expresses long and possibly cyclic dependencies between terms, e.g., [Heart] is a part of [Circulatory System] which has a part [Lung] which is a part of [Respiratory System] which has a part [Trachea], etc., then the reasoner has to construct very large models. For some existing medical ontologies, the models are so big that they do not fit into the main memory of a computer. Another problem is that the ontology may potentially have a large number of different models, each of which must be independently explored by the reasoner. Ontology languages provide for constructors called 'number restrictions', which result in a particularly large number of models. Number restrictions are used to specify quantitative information in ontologies and are often used in bio-chemical ontologies, for example to express that a molecule of [Ethanol] contains {exactly 6} [Hydrogen Atoms]. These limitations of model-building reasoners, therefore, pose a serious problem for the development of large medical and bio-chemical ontologies---without efficient reasoning tools, for example, the users of such ontologies may not be able to obtain the information that they are interested in.In this project we investigate alternative consequence driven reasoning procedures that do not build models but explicitly derive logical consequences of ontological axioms. Our preliminary investigations suggest that both problems mentioned above can be avoided for consequence-driven reasoning procedures: there is no need to keep track of large models, and the number of logical consequences of ontological axioms is typically much smaller than the sizes and the number of the models.