A Semantic-Driven Knowledge Representation Model for the Materials Engineering Application
نویسندگان
چکیده
منابع مشابه
A Semantic-Driven Knowledge Representation Model for the Materials Engineering Application
A Materials Engineering Application (MEA) has been presented as a solution for the problems of materials design, solutions simulation, production and processing, and service evaluation. Large amounts of data are generated in the MEA distributed and heterogeneous environment. As the demand for intelligent engineering information applications increases, the challenge is to effectively organize th...
متن کاملWebQR: Building a knowledge representation application on the Semantic Web
The Semantic Web (SW) was originally positioned as a combination of Knowledge Representation (KR) and the Web. However, most applications that use SW data today lean more towards the Information Retrieval spectrum. The reason for this is that traditional KR systems are designed to work with datasets that are small, curated, homogeneous, and application-specific. However, the SW is large-scale, ...
متن کاملA Model Driven Framework for Integrated Computational Materials Engineering
Integrated computational materials engineering (ICME) is a new approach to the design and development of materials, manufacturing processes and products. The approach proposes using a combination of modeling and simulation, data driven reasoning and knowledge guided decision making to a) speed up the development of new materials and manufacturing processes, and b) enhance the quality and time-t...
متن کاملA novel representation for search-based model-driven engineering
Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE) are development approaches that focus on automation to increase productivity and throughput. MDE focuses on high-level domain models and the automatic management of models to perform development processes, such as model validation or code generation. SBSE on the other hand, treats software engineering problems as optimi...
متن کاملA Joint Semantic Vector Representation Model for Text Clustering and Classification
Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Data Science Journal
سال: 2014
ISSN: 1683-1470
DOI: 10.2481/dsj.13-061