Research on Manufacturing Resource Domain Ontology Modeling Method

2012 ◽  
Vol 220-223 ◽  
pp. 3058-3063
Author(s):  
Zhong Liu ◽  
Xian Guo Yan ◽  
Hong Guo ◽  
Shi Su ◽  
Chang Gui Xu ◽  
...  

Retrieval, recall rate and precision are not acceptable according to the general search engine in searching networked manufacturing resource. Manufacturing Resource Domain Ontology was derived and established based on the concept of the ontology in this paper. Model description was completed with protégé.

2012 ◽  
Vol 241-244 ◽  
pp. 1659-1663
Author(s):  
Shu Dong Zhang ◽  
Can Zhang ◽  
Jing Wang

With the development of the Semantic Web, ontology has become the primary means of expression of many fields of knowledge. Introducing the Semantic Web technology into the field of search engine is a valuable research topic. In order to meet the complex semantic retrieval demands, the paper proposes a search engine model based on multi-domain ontology, the model using ontology mapping rewrite the user query to achieve multiple ontology query, and provide a richer and accurate semantic information for the retrieval of cross-domain knowledge; And the paper proposes a method of cross-domain ontology annotation, providing a basis for the user semantic retrieval. The experimental results show that the search results improve the precision and recall rate.


2013 ◽  
Vol 774-776 ◽  
pp. 1382-1385
Author(s):  
Xin Shang ◽  
Jing Liu

Aimed at The key problem for manufacturing resource integration and retrieval of the networked manufacturing resources sharing and reuse, by analyzing the characteristics of the enterprise manufacturing resource and ontology,OWL ontology modeling method based on manufacturing resource carrier was proposed,manufacturing resource was integrated and index by modules; so, manufacturing resource was network reused and shared by carrier was evaluated and index, product development time was reduced, competitiveness was enhance. Finally, wrapper module of paper packaging machine as an example to proves the feasibility of the method.


2011 ◽  
Vol 6 (1) ◽  
pp. 81
Author(s):  
Laura Newton Miller

A Review of: Jamali, H. R., & Asadi, S. (2010). Google and the scholar: The role of Google in scientists' information seeking behaviour. Online Information Review, 34(2), 282-294. Objective – To determine how Google’s general search engine impacts the information-seeking behaviour of physicists and astronomers. Design – Using purposive stratified non-random sampling, a mixed-methods study was conducted which included one-on-one interviews, information-event cards, and an online questionnaire survey. Setting – Department of Physics and Astronomy at University College London. Subjects – The researchers interviewed 26 PhD students and 30 faculty members (23% of the department’s 242 faculty and students), and 24 of those participants completed information-event cards. A total of 114 respondents (47.1% of the department members) participated in the online survey. Methods – The researchers conducted 56 interviews which lasted an average of 44 minutes each. These were digitally recorded, fully transcribed, and coded. The researchers asked questions related to information-seeking behaviour and scholarly communication. Four information-event cards were given to volunteer interviewees to gather critical incident information on their first four information-seeking actions after the interview. These were to be completed preferably within the first week of receiving the cards, with 82 cards completed by 24 participants. Once initial analysis of the interviews was completed, the researchers sent an online survey to the members of the same department. Main Results – This particular paper examined only the results related to the scholars’ information-seeking behaviour in terms of search engines and web searching. Details of further results are examined in Jamali (2008) and Jamali and Nicholas (2008). The authors reported that 18% of the respondents used Google on a daily basis to identify articles. They also found that 11% searched subject databases, and 9% searched e-journal websites on a daily basis. When responses on daily searching were combined with those from participants who searched two to three times per week, the most popular method for finding research was by tracking references at the end of an article (61%). This was followed by Google (58%) and ToC email alerts (35%). Responses showed that 46% never used Google Scholar to discover research articles. When asked if they intentionally searched Google to find articles, all except two participants answered that they do not, instead using specific databases to find research. The researchers noted that finding articles in Google was not the original intention of participants’ searches, but more of a by-product of Google searching. In the information-event card study, two categories emerged based on the kinds of information required. This included participants looking for general information on a specific topic (64%, with 22 cases finding this information successfully), and participants knowing exactly what piece of information they were seeking (36%, with 28 cases finding information successfully). There was no occurrence of using Google specifically to conduct a literature search or to search for a paper during this information-event card study, although the researchers say that Google is progressively showing more scholarly information within its search results. (This cannot be ascertained from these specific results except for one response from an interviewee.) The researchers found that 29.4% of respondents used Google to find specific pieces of information, although it was not necessarily scholarly. Conclusion – Physics and astronomy researchers do not intentionally use Google’s general search engine to search for articles, but, Google seems to be a good starting point for problem-specific information queries.


2018 ◽  
Vol 72 (11) ◽  
pp. 1059-1063 ◽  
Author(s):  
Brian G Southwell ◽  
Milton Eder ◽  
John Finnegan ◽  
Alan T Hirsch ◽  
Russell V Luepker ◽  
...  

BackgroundLiterature on health promotion evaluation and public understanding of health suggests the importance of investigating behaviour over time in conjunction with information environment trends as a way of understanding programme impact. We analysed population response to online promotion of an educational tool built by the Ask About Aspirin campaign in the USA to inform people about aspirin as a preventive aid.MethodsWe collected 156 weeks of time series data on audience behaviour, namely use of a self-assessment tool. We then used the Autoregressive Integrated Moving Average (ARIMA) modelling to predict that outcome as a function of paid search engine advertising, paid social media promotion and general search interest in aspirin.ResultsThrough ARIMA modelling of tool engagement data adjusted for outcome series autocorrelation, we found a significant effect of online promotional effort on audience behaviour. Total paid search advertising positively predicted weekly total of individuals who started using the self-assessment tool, coefficient=0.023, t=3.28, p=0.001. This effect did not appear to be an artefact of broader secular trends, as Google search data on the topic of aspirin use did not add explanatory power in the final model nor did controlling for general search interest eliminate the significant coefficient for paid search promotion.ConclusionResults hold implications both for educational tool development and for understanding health promotion campaign effects. We witnessed substantial but ephemeral effects on tool use as a function of paid search efforts, suggesting prioritisation of efforts to affect search engine results as a dissemination tactic.


2014 ◽  
Vol 926-930 ◽  
pp. 2263-2266
Author(s):  
Li Juan Diao ◽  
Jun Zhong Gu ◽  
Liang Chun

Ontology definition metamodel has been widely adopted in aspect of building ontology. However existing ontology metamodel is only suitable for building ontology in a certain domain. With collaboration and sharing among multiple domains, we face the seriously problem that is how to overcome semantic interoperability. For this problem, we need to combine general ontology with domain ontology and merge all existing ontologies by ontology metamodel. In this paper, we define main components of ontology metamodel and present conditional context and contextual concept unit. In addition, we introduce the method of mapping between conditional context and contextual concept unit. Finally, we use an example about information retrieval to illustrate its function and analysis its feasibility.


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