隨著語意網(Semantic Web)的興起,帶動著網際網路未來的發展趨勢。語意網能區別出詞彙(Vocabulary)的意義,且利用知識本體(Ontology)的知識架構能正確判斷詞義,知識本體也可以進行推論和訊息整合等能力,因此使用網路上的語意規則與知識本體,將分散各處的訊息結合,並且正確的找出搜尋結果。 知識本體應用於資訊系統建構時,可以將它視為對真實世界的一種感知,轉換成為資訊系統的過程,則知識本體在建構程序有三項:(1)知識擷取、(2)知識塑模(Knowledge Modeling)、(3)知識表達。知識的分類在以往使用人工方式去建置時,其過程是非常繁瑣且困難,而建立出的知識模型也將決定此領域知識本體的品質,因此本研究在知識之塑模階段,提出一套自動化建置知識分類架構的方法,結合混合式機器學習法則,應用於電腦病毒(Computer Virus)領域的查詢與推論,電腦經由既有的病毒特徵,仿造人類學習的模式產生電腦病毒的知識階層架構,知識工程師參照此知識階層架構,並可塑造出電腦病毒概念與屬性之間的關聯階層架構,最後利用知識本體的優點,使用SWRL規則推論出電腦病毒間隱含的關係,並且提供相關的解決方案給予使用者。
Semantic Web was pushing the development of the internet forward. Semantic web can distinction the meaning of vocabulary and use ontology of knowledge structure can be correct discrimination the meaning in term. Therefore, user can integrates the information and reason the result by the rule of semantic web and ontology. Ontology system development can be viewed as a transformation from perceptions of a real-world system to a working information system which is a representation of the real system. Ontology building requires a set of engineering processes that include knowledge acquisition, knowledge modeling, and knowledge representation. It is difficult to classify the knowledge by artificial in the past. Therefore, this study proposed the ontology combine with machine learning that can detection and search the computer virus. Knowledge engineers collect the characters of the computer viruses, then make in imitation of human learning through the computer which can generate the knowledge hierarchical structure of computer viruses. This method can model the knowledge structure of ontology quickly and conveniently. Knowledge engineers refer to the hierarchical structure and build the ontology of this domain. Finally, according to the advantage of ontology, manager can infer the hidden relationship of computer virus by SWRL and provide the solution to users.