3 research outputs found
Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
Selecting a representative vector for a set of vectors is a very common
requirement in many algorithmic tasks. Traditionally, the mean or median vector
is selected. Ontology classes are sets of homogeneous instance objects that can
be converted to a vector space by word vector embeddings. This study proposes a
methodology to derive a representative vector for ontology classes whose
instances were converted to the vector space. We start by deriving five
candidate vectors which are then used to train a machine learning model that
would calculate a representative vector for the class. We show that our
methodology out-performs the traditional mean and median vector
representations
A Hybrid Adaptive Educational eLearning Project based on Ontologies Matching and Recommendation System
The implementation of teaching interventions in learning needs has received
considerable attention, as the provision of the same educational conditions to
all students, is pedagogically ineffective. In contrast, more effectively
considered the pedagogical strategies that adapt to the real individual skills
of the students. An important innovation in this direction is the Adaptive
Educational Systems (AES) that support automatic modeling study and adjust the
teaching content on educational needs and students' skills. Effective
utilization of these educational approaches can be enhanced with Artificial
Intelligence (AI) technologies in order to the substantive content of the web
acquires structure and the published information is perceived by the search
engines. This study proposes a novel Adaptive Educational eLearning System
(AEeLS) that has the capacity to gather and analyze data from learning
repositories and to adapt these to the educational curriculum according to the
student skills and experience. It is a novel hybrid machine learning system
that combines a Semi-Supervised Classification method for ontology matching and
a Recommendation Mechanism that uses a hybrid method from neighborhood-based
collaborative and content-based filtering techniques, in order to provide a
personalized educational environment for each student
An Ontology Mapping Method Based on Support Vector Machine
Abstract. Ontology mapping has been applied widely in the field of semantic web. In this paper a new algorithm of ontology mapping were achieved. First, the new algorithms of calculating four individual similarities (concept name, property, instance and structure) between two concepts were mentioned. Secondly, the similarity vectors consisting of four weighted individual similarities were built, and the weights are the linear function of harmony and reliability, and the linear function can measure the importance of individual similarities. Here, each of ontology concept pairs was represented by a similarity vector. Lastly, Support Vector Machine (SVM) was used to accomplish mapping discovery by training the similarity vectors. Experimental results showed that, in our method, precision, recall and f-measure of ontology mapping discovery reached 95%, 93.5 % and 94.24%, respectively. Our method outperformed other existing methods. Introduction: In this paper, our study mainly is to discover the mapping [1] between concepts belonging to the different ontologies respectively. The proposed algorith