**6. References**

Al-Feel, H., Koutb, M. A., & Suoror, H. (2009). Toward An Agreement on Semantic Web Architecture, *World Academy of Science, Engineering and Technology* (49), 806-810.


188 Semantics – Advances in Theories and Mathematical Models

different sources. This research attempts to break that trend and use knowledge to manage the spatial data through knowledge management techniques. In the process, it provided the mechanisms to infer spatial rules through spatial built-ins for SWRL. This was done first through populating domain ontology with the spatial components so that spatial knowledge could be enriched into it and this spatially rich knowledge base is inferred through SWRL. It could also be queried through SPARQL. However there are number of issues that need to be addressed in future work. The first one is about the dependability on the database systems to conduct the spatial operations and functions. This research uses the spatial operations and functions provided by PostGIS, the spatial extension of PostgreSQL to enrich the knowledge base through their result. Future works should make an attempt to free them with such dependency through providing such functionalities within spatial built-ins

Another area where the research could concentrate is the area of using current reasoning engines to reason the spatial knowledge base and deduce the implicit spatial knowledge. In other words addition to the the inference engine to infer the rules through SWRL, the constraint axioms should be introduced within the ontology which automatize the enrichment of knowledge base through reasoning mechanism. The constraint axioms in particular should be able to include the spatial built-ins and run through the respective spatial operations and functions to automatize the enrichment process while reasoning the knowledge base. It can be clarified with one of the typical examples in industrial archaeology: "chimney should be 5 meters around an oven and should be round". Currently

feat:Object(?x) ^ feat:Oven(?y) ^ spatialswrlb:Buffer(?y, 5, ?x) ^ att:hasShape(?x, round) feat:Chimney(?x) (3)

This infers the spatial knowledge base to annotate the result to the class feat:Chimney.

 feat:Chimney ⊑ Within(Buffer(feat:Oven,5)) ⊓ hasShape.{round} (4) can be thought upon. The existing reasoning engine then reasons every object with round

Lastly, it is important to have standard terms for every built-in that will be developed to process spatial knowledge. With other built-ins in the tools standardized by W3C, the spatial built-ins should also get standardized by the consortium. In addition to W3C, OGC should also get involved in standardizing the built-ins. An effort in this direction should be

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Baader, F., & Nutt, W. (2002). Basic Description Logics, *Description Logic Handbook* (pp. 47 -

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it is possible to execute this only through SWRL rule.

However an alternative could be a theorem

themselves.

carried out.

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**8** 

*Japan* 

**Representation System** 

**for Quality Indicators by Ontology** 

*Industrial Science and Technology and Kitasato University Hospital* 

*Japan Advanced Institute of Science and Technology, National Institute of Advanced* 

We have not yet established a proper methodology to accurately evaluate the quality of medical services, although such a method is necessary for fair comparison between hospitals and/or improvement in the quality of medical services. The reason is that such a methodology needs a reasonable way to transform qualitative properties of medical services such as doctors' skill or patient satisfaction into quantitative properties that are measurable by data existing in medical databases, but, it has not yet been researched sufficiently. In general, it is not easy to fairly evaluate abstract things such as intelligence and performances by measuring quantitative aspects of them although we often have opportunities to evaluate such things. Moreover, even though we have quantitative properties denoting some useful properties, we need a proper method to accurately represent such quantitative properties in order to make users understand the definitions of

In this chapter, we introduce a representation system of quality indicators. Quality indicators are barometers that indicate processes, results and/or other things of medical services numerically, in order to evaluate medical services. The representation system helps to define quality indicators and to calculate their values in a coherent manner that is based on the data in medical databases. The representation system primarily consists of three parts. The first one is an ontology to define concepts related to medical services. The second one is a set of graphs that express the targets of quality indicators. We call these graphs "objective graphs". The third one is a set of "quantifying concepts" that abstract the quantities of the subjects. The proposed system represents a quality indicator as a

An objective graph can be interpreted as a set of instances of a concept. The set is defined by the properties described by the labels of the arrows in the graph. We also explain the

 The first advantage is that by representing a quality indicator with the representation system one can avoid the problem that occurs from a word in the quality indicator that

combination of an objective graph and a quantifying concept.

interpretation of objective graphs for the sets in this paper.

The representation language provides the following advantages.

**1. Introduction**

the properties correctly.

Osamu Takaki, Izumi Takeuti, Koichi Takahashi, Noriaki Izumi, Koichiro Murata and Koiti Hasida

