## 3 Proposed System Technology, Architecture, and Operation  
### Domain Ontology and Related Ontology Services  
This study used Protege ([[http://protege.stanford.edu/](http://protege.stanford.edu/)]([http://protege.stanford.edu/](http://protege.stanford.edu/))) to construct the basic framework of domain knowledge, and used this tool to develop many ontologies, including PC-DIY (Personal Computer Do It Yourself) ontology, researcher ontology, thesis solicitation ontology, network protocol packet format ontology, energy-saving information ontology, and YOHO (Young Home) information ontology, which are used to verify the validity and correctness of the ontology construction methods and tools. Protege API ([[https://protegewiki.stanford.edu/wiki/WebProtegeUsersGuide](https://protegewiki.stanford.edu/wiki/WebProtegeUsersGuide)]([https://protegewiki.stanford.edu/wiki/WebProtegeUsersGuide](https://protegewiki.stanford.edu/wiki/WebProtegeUsersGuide))) was used to construct and enhance the processing technologies for the relevant information systems supported by ontology. In addition, this study constructed a reasoning primitive function that supports the application of knowledge ontology. Finally, the ontology architecture was utilized to index actual information for rapid and accurate access to relevant information (see Figure 1 for the index concept). First, Figure 1a shows how the ontology index is used to index relevant files, then to filter the documents that do not contain this ontological word with the partial full-text index, and, finally, to narrow the search scope of the documents in two stages. This approach can accurately judge useful information, as ontology has the precise meaning of domain words, and can quickly index and filter useful information by combining it with real data to reach the goal of ontology, meaning it supports fast and accurate access to useful information (see Figure 1b for details). Furthermore, the system further modularizes the relevant usage information according to different usage situations, which facilitates the quick access of the system, and thus effectively supports the overall system operation, as shown in Figure 1c. Figure 1: _Cont._Ontological services include the semantic distance conversion of search words and conversion services, such as search words corresponding to upper words, lower words, synonyms, and antonyms. This study adopted WordNet ([[http://wordnet.princeton.edu/](http://wordnet.princeton.edu/)]([http://wordnet.princeton.edu/](http://wordnet.princeton.edu/))) as the base of the comparison model in combination with the Chinese-English bilingual ontology word network ([[https://ckip.iis.sinica.edu.tw/CKIP/engversion/index.htm](https://ckip.iis.sinica.edu.tw/CKIP/engversion/index.htm)]([https://ckip.iis.sinica.edu.tw/CKIP/engversion/index.htm](https://ckip.iis.sinica.edu.tw/CKIP/engversion/index.htm))) of Taiwan's Academia Sinica to discuss the transformation of Chinese-English bilingual information, the link between language information and the conceptual framework, and the link between word sense differentiation and word sense relationship, as well as their fields of use, in order to provide the basic framework for knowledge management in this system. _Jaccard similarity_ (or Jaccard index) was introduced to estimate the consistency between ontological concepts [5; 6]. The basic concept is to index the domain concept by using the consistency between the concept of the search term and the corresponding concept of WordNet and its related position. Finally, the domain concept is supported by the identifier Synset_ID in WordNet to support the overall system operation.