近來政府開放越來越多公開資料，然而許多資料格式未經統一或是資料欄位內容與彼此間之關係未完整詮釋，導致在應用資料時會有「各欄位之間的關聯性未完整說明」、「同一地區名稱指涉不同空間範圍」與「基準或分類標準不同」，尤其都市計畫為跨領域學科，資料的應用分析更加複雜；而常用的解決方式為建構知識本體。因此，本研究以空氣品質、國土利用、人口統計資料為例，先建立較嚴謹的數學形式:多型代數，接著以此為基礎建構資料欄位之間關係的知識本體，更進一步以在專家系統CLIPS之中建立推論規則，推論資料之間所隱含的資訊。
本研究統整出在各資料中解決上述三項議題所需的關係，研究成果發現在推論規則中，時間和地點兩項變數最為重要，相當於連結外部資料的主鍵；而在建構資料欄位之間關係的知識本體時，該關係是否為二元關係對於建構知識本體的方式具有重要的影響。若為單純的二元關係，則基本上在Web ontology language(OWL)的語法當中可將其宣告為一種Property，例如國土利用調查資料中的分類階層、代碼轉換、分類升級、分類降級、分類類別；人口統計資料中的行政區升格、行政區階層、起始時間、被改制之時間、行政區更名、下級行政區。而如果該關係非單純的二元關係，則在OWL中需要將其宣告為Owl:Class，例如空氣品質資料中的轉換PSI值、判斷PSI代表汙染物與汙染值、汙染濃度；國土利用調查資料中的代碼整合、代碼分解；人口統計資料中的行政區合併。
而人口統計資料中的Merge關係因行政區合併後會有合併前後行政區同名的情況，而此種情形需要額外做處理，本研究提出之解決方式為將經過縣市合併後有此類問題的行政區宣告為Administrative的sub class，而合併前後同名的新舊行政區分別皆為此行政區的一種實例。

Due to the ununified forms or the lack of description and explanation of data schemas and contents, there are three common issues when applying data: (1) lack of explanation of the relations between schemas, (2) different regions using the same name, (3) different criterion or standards for classification. A common solution for those kinds of issues is the construction of ontology. Therefore, this research used the air quality, landuse and demographics data to build the ontology of relations between data schemas based many-sorted algebraic, then build the inferring rules in expert system to infer the implied relations between those three datasets.
In this research, all the relations needed to solve the issues were identified. The research result shows that time and location are the most important variables when inferring relations. And whether the relation is a binary relation or a non-binary relation plays an important role on building the ontology.
If it is a binary relation, it can be declared as a property in web ontology language(OWL), such relations including Level, Change, Promote, Degrade, Chinese in Landuse survey data and Hierarchy, Promote, Start_Time, End_Time, Rename, SubAdmin in demographics data. On the other hand, if it is a non-binary relation, it should be declared as a class in OWL, this kind of relations includes Density, Convert, Represent_PSI in air quality data, Merge, Decompose in Landuse survey data and Merge in demographics data.
The relation Merge in demographics data should be carefully discussed because after two or more administratives merged, it is very likely that the administrative name after and before merge are the same. To solve this issue, this research declared those administrative as a sub class of Administrative, and their sub administratives are their instances.

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