政府開放資料目前於各國如火如荼的被大量使用, 自從2009年英國和美國政府開放資料平台上線以來,各個國家陸續發展出政府開放資料平台。當然台灣也不例外,台北市政府首先推出台北市開放資料平台,提供給民眾資料查詢或應用開發使用。綜觀目前台灣的開放資料,雖然涵蓋了各領域的大量資料,但是卻有著格式雜亂、格式不易整合應用、資料授權...等問題,這些因素限制了應用開發者或民眾們對這些資料的應用與開發。
本研究利用開放資料的資料鏈結概念，透過語意網路技術來分析雜亂資料的相互關係,並透過資料轉換成RDF格式來加強資料的可讀性以及系統性，除此之外我們更提供了語意網路的存取介面,讓資料更容易地被使用,且開發上更有彈性。為了貼近使用者的生活,本研究發展以台北市地理位置為基礎的情境感知,讓使用者隨時隨地都可以利用公共腳踏車和停車場的開放資料於交通應用中。我們利用行動裝置的定位感應功能整合到此兩種開放資料集上，並進一步配合開放資料在時間軸的數據分析，讓使用者可以透過目前地理位置以及設定的條件相互查詢與服務整合來串聯成由開車、停車、到租用公共腳踏車的整合情境感知應用。

Government Open data has been widely used at present in various countries. Since Data.gov.uk(England) and Data.gov(US) platform lunch in 2009,lots of countries have developed their own open data platform one after another. Of course, Taiwan is not exception from this trend, first of all, the Taipei City Government launched the Taipei open data platform, available for queries or developments. Looking at the Taiwan open data, which covers various areas of large amounts of data, but it has problems including formatting mess, formats that is not easy to integrate, data licensing...and so on, these problems limit developers or people difficult to apply or develop useful services.
Our research uses the concepts of linked open data to analyze correlated open data via semantic web technology. The linked open data are transformed to an RDF (Resource Description Framework) format to enhance the readability and systematic. In addition, a semantic web access interface is provided to make open data much easier for service integration. In order to illustrate these open data can be applied to user's daily life, we develop a location-based context-aware service based on Taipei City. Our service uses two data sets including public bicycles and parking lots. Users can use mobile devices to locate parking lots and set analysis conditions through time-line for public bicycles. Our application can be applied to the integrated scenario of driving – parking -biking context aware services.

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