The Construction and Application of Online Programming Learning System with Interactive Video Based on Semantic Web

The deep integration of the Internet, intelligent technology and education is profoundly changing the paradigm of learning. This paper uses modern web technologies to build a platform for online programming learning through interactive video, which allows learners to interact with the programming learning video when watching, and directly modify the code and run it, thus achieves active interactive learning. Meanwhile, the semantic web technology is used to define the semantics of the knowledge unit to construct a learning map, and combined with the evaluation of the learner’s learning status and the zone of proximal development theory, the function of the intelligent recommendation learning unit is designed. A prototype system is constructed based on a specific programming course. Through the feedback of learners, it is verified that this platform improves learners’ interest and initiative, and can well meet learners’ personalized learning needs.

Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “computable knowledge object” remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.

A Novel Approach to Discover Ontology Alignment

Background: The rise of knowledge-rich applications has made ontologies as a common reference point to link the legacy IT systems. The interoperability and integration of two disparate systems in the same domain demand for the resolution of the heterogeneity problem. The major source of heterogeneity lies in the classical representation scheme of ontologies. Objective: Our objective is to present a novel approach to discover ontology alignment by exploiting the comprehensive knowledge structure, where every entity is represented and stored as a knowledge unit. Method: We have created the dataset ourselves by using protégé tool because no dataset is available based on the idea of comprehensive knowledge structure. Result: The proposed approach always detects correct alignments and achieves optimal or near to optimal performance (in term of precision) in case of equivalence relationship. Conclusion: The aim of this paper is not to make a full-fledged matching/alignment tool, but to emphasize the importance of distinctive features of an entity while performing entity matching. The matchers are therefore used as black boxes and may be filled based on user’s choice.

CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE

Modern educational methods emphasize the necessity to transfer knowledge instead of data or information within the educational process. Thus it is important to the educational texts supporting the educational process contain knowledge in a particular textual representation. But it is not trivial to decide whether the particular piece of text contain knowledge or not. The solution is to measure the similarity between the particular text structure and a typical structure of a knowledge-designed text. This research aims at analysing the classification ability of three commonly-used classification techniques: artificial neural networks (ANNs), classification and regression trees (CARTs) and decision trees (bigMLs) to separate texts or text fragments into two groups. The texts in the first group contain mainly data and information (common texts), the texts in the other group contain knowledge in one of the particular knowledge representations (knowledge texts). The sample of 120 text fragments was used for the analysis. The results show that the ANN techniques are significantly more able to make the right classification of the text than the CART or bigML ones, and evidence good classification abilities. Thus the ANN approach could broaden the set of methods used for evaluation of difficulty of educational texts or textbooks. Keywords: artificial intelligence, classification and regression trees, educational texts, knowledge representation, knowledge unit, production rules, stylometric analysis.