Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Personalized music recommendations can accurately push the music of interest from a massive song library based on user information when the user’s listening needs are blurred. To this end, this paper proposes a method of national music recommendation based on ontology modeling and context awareness to explore the use of music resources to portray user preferences better. First, the expectation-maximization algorithm is used to cluster users and ethnic music scores, and similar users and music are divided into clusters. The similarity of objects in the same cluster is higher, and the similarity of objects in different clusters is lower. Second, we designed a multilayer collaborative filtering ethnic music recommendation model based on ontology modeling and tensor decomposition. This model uses ontology to construct a user knowledge model and integrates similarity measures in multiple situations. The actual case test and user feedback analysis show that the designed personalized national music model has good application and promotion effects.

AMAΛΘΕΙA: A Dish-Driven Ontology in the Food Domain

We present AΜAΛΘΕΙA (AMALTHIA), an application ontology that models the domain of dishes as they are presented in 112 menus collected from restaurants/taverns/patisseries in East Macedonia and Thrace in Northern Greece. AΜAΛΘΕΙA supports a tourist mobile application offering multilingual translation of menus, dietary and cultural information about the dishes and their ingredients, as well as information about the geographical dispersion of the dishes. In this document, we focus on the food/dish dimension that constitutes the ontology’s backbone. Its dish-oriented perspective differentiates AΜAΛΘΕΙA from other food ontologies and thesauri, such as Langual, enabling it to codify information about the dishes served, particularly considering the fact that they are subject to wide variation due to the inevitable evolution of recipes over time, to geographical and cultural dispersion, and to the chef’s creativity. We argue for the adopted design decisions by drawing on semantic information retrieved from the menus, as well as other social and commercial facts, and compare AMAΛΘΕΙA with other important taxonomies in the food field. To the best of our knowledge, AΜAΛΘΕΙA is the first ontology modeling (i) dish variation and (ii) Greek (commercial) cuisine (a component of the Mediterranean diet).

Ontological Model of School Librarianship Based on Collaboration

Considering the definition of ontology as a common vocabulary for researchers (practitioners) who need to share information in a domain, we have established a set of concepts concerned with identifying, analyzing, describing and representing shared conceptualization in Croatian school librarianship. We have given a survey of research areas in the last 24 years. Approaching the problem of ontology, we applied it in the sense of information science, i.e., modeling language based on a common vocabulary and understanding when and how the modern concept of librarianship like media and information literacy, information needs, curriculum integration, e-learning etc. were introduced into Croatian school librarianship practice. The next step could be to dentify and to evaluate the strength of the various semantic links between found concepts. The key issue of school library ontology modeling is the answer to the question: how well the Croatian school librarian community is prepared to participate in building 21st century learning environments.

An Automatic Generation of Domain Ontologies Based on an MDA Approach to Support Big Data Analytics

This chapter proposes a new methodology for the automatic generation of domain ontologies to support big data analytics. This method ensures the recommendations of the MDA approach by transforming UML class diagrams to domain ontologies in PSM level through ODM, which is an OMG standard for ontology modeling. In this work, the authors have focused on the model-driven architecture approach as the best solution for representing and generating ontology artifacts in an intuitive way using the UML graphical syntax. The creation of domain ontologies will form the basis for application developers to target business professional context; however, the future of big data will depend on the use of technologies to model ontologies. With that said, this work supports the combination of ontologies and big data approaches as the most efficient way to store, extract, and analyze data. It is shown using the theoretical approach and concrete results obtained after applying the proposed process to an e-learning domain ontology.