Context-awareness is highly desired across several application domains. Semantic Web Services (SWS) enable the automatic discovery of distributed Web services based on comprehensive semantic representations. However, although SWS technology supports the automatic allocation of resources for a given well-defined task, it does not entail the discovery of appropriate SWS representations for a given situational context. Whereas tasks are highly dependent on the situational context in which they occur, SWS technology does not explicitly encourage the representation of domain situations. Moreover, describing the complex notion of a specific situation in all its facets is a costly task and may never reach semantic completeness. Particularly, following the symbolic SWS approach leads to ambiguity issues and does not entail semantic meaningfulness. Apart from that, not any real-world situation completely equals another, but has to be matched to a finite set of semantically defined parameter descriptions to enable context-adaptability. To overcome these issues, we propose Conceptual Situation Spaces (CSS) which are aligned to established SWS standards. CSS enable the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. Semantic similarity between situations is calculated in terms of their Euclidean distance within a CSS. Extending merely symbolic SWS descriptions with context information on a conceptual level through CSS enables similarity-based matchmaking between real-world situation characteristics and predefined resource representations as part of SWS descriptions. To prove the feasibility, we apply our approach to the domain of E-Learning and provide a proof-of-concept prototype.
在多个应用程序域中都非常需要上下文感知。 语义Web服务(SWS) I>可以基于全面的语义表示自动发现分布式Web服务。但是,尽管SWS技术支持为给定的明确定义的任务自动分配资源,但是它并不意味着需要为给定的情境找到合适的SWS表示形式。尽管任务高度依赖于任务发生的情境,但是SWS技术并未明确鼓励域情况的表示。此外,描述特定情况的所有方面的复杂概念是一项昂贵的工作,并且可能永远无法达到语义上的完整性。特别是,遵循符号SWS方法会导致歧义问题,并且不会带来语义上的意义。除此之外,并非任何现实情况都完全等同于另一种情况,而是必须与语义定义的参数描述的有限集合进行匹配,以实现上下文适应性。为了克服这些问题,我们提出了符合已建立的SWS标准的概念态势空间(CSS) I>。 CSS遵循概念空间的思想,可以将情况特征描述为几何向量空间中的成员。情况之间的语义相似性是根据CSS中的欧式距离来计算的。通过CSS在概念级别上仅使用上下文信息扩展符号化的SWS描述,可以在真实世界中的情况特征和预定义的资源表示之间进行基于相似性的匹配,作为SWS描述的一部分。为了证明其可行性,我们将我们的方法应用于电子学习领域,并提供了概念证明原型。 P>
The Open University, Milton Keynes;
机译:使用NLP技术的基于语义相似性的上下文感知Web服务发现
机译:使用基于过程模型的服务的Web本体语言,基于过程模型的原子服务发现和组合语义Web服务的组合
机译:利用语义Web服务在发展上下文感知系统中
机译:通过概念性环境空间向环境感知语义Web服务发现
机译:DAGIS:自动发现用于地理空间语义Web的带注释的地理空间信息服务框架。
机译:语义自动发现和集成(SADI)Web服务设计模式API和参考实现
机译:通过概念情境空间实现上下文感知语义Web服务发现
机译:用于移动上下文感知服务的语义Web技术