Educators have been calling for reform for a decade. Recent technical breakthroughs have led to various improvements in the semantic web-based education system. After last year's COVID-19 outbreak, development quickened. Many countries and educational systems now concentrate on providing students with online education, which differs greatly from traditional classroom education. Online education allows students to learn at their own pace and the system. As a consequence, we may say that education has become more dynamic. In the educational system, this changing nature makes user demands difficult to identify. Many instructors suggest using machine learning, artificial intelligence, or ontology to improve traditional teaching methods. Due to the lack of survey studies examining and comparing all of the researcher's semantic web-based teaching methodologies, we decided to conduct this survey. This paper's goal is to analyse all available possibilities for semantic web-based education systems that enable new researchers to develop their knowledge.

Evaluation and Comparative Analysis of Semantic Web-Based Strategies for Enhancing Educational System Development

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.

/pdf/recommendation-of-healthcare-services-based-on-an-embedded-2brpysvl.pdf

Recommendation of Healthcare Services Based on an Embedded User Profile Model

Facilitating innovation and knowledge transfer between homogeneous and heterogeneous datasets: Generic incremental transfer learning approach and multidisciplinary studies

A Part-of-Speech (POS) tagger and Chunker (or shallow parser) are sequence labeling tools, crucial for improving the accuracy of Natural Language Processing (NLP) tasks like parsing, named entity recognition, sentiment analysis, information extraction, etc. Developing such tools for a low-resource language is an arduous task. Nepali is a relatively resource-poor Indian language and has not been able to evolve from a computational perspective. Therefore, we present effective part-of-speech tagging and chunking tools for the Nepali text using sequential deep learning models - Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer (BI-LSTM-CRF) and other LSTM based models exploring both character and word embeddings of the Nepali texts. Word Embedding has been used to capture syntactic as well as semantic information whereas character embedding has been applied to capture the morphological as well as shape information of words and also to handle the out-of-vocabulary problem. The developed chunker is the first statistical chunker for the Nepali language. A baseline model with a Conditional Random Field has also been developed to identify the optimum feature set for the aforementioned tasks. The BI-LSTM-CRF model produced an accuracy of 99.20 \(\% \) and 98.40 \(\% \) , for Nepali POS tagging and chunking, respectively. This is the highest-ever accuracy for Nepali. Thorough error analysis and observations have also been reported with examples. The developed tools can help advance research in Nepali language processing, improve the accuracy of language technology applications, and contribute to the preservation and promotion of the Nepali language.

https://dl.acm.org/doi/pdf/10.1145/3606696

Deep Learning based Sequence Labeling Tools for Nepali

Knowledge Graph Generation is a requisite in the modern era, where Knowledge is scarce whereas the data is exponentially surplus and extensively high. So, in this paper Knowledge Graph Generation framework, the SMDKGG has been proposed, is a Semantically Inclined Metadata driven model for Knowledge Graph Generation, which is a text to Knowledge Graph generation strategy where the document dataset is subjected to a lateral mapping of categories and alignment of generated ontologies with that of the categories for auxiliary Knowledge enhancement. Subsequently, applying Structural Topic Modeling (STM) and harvesting entities from LOD Cloud, NELL, DBPedia, and CYC Knowledge sources ensures the cohesiveness of the auxiliary Knowledge encompassed in the framework becomes extensively high, making the density of the harvested auxiliary Knowledge high. Apart from this, the Metadata generation and its classification using Transformers ensure that many more auxiliary knowledge substrates are selectively added to the framework. Its classification by a Deep Learning Transformer classifier ensures its ease of handling. Subsequently, the classification of the dataset using the AdaBoost, the encompassment of Twitter Semantic Similarity (TSS) for intra-class similarity computation, and the integration of Normalized Google Distance (NGD) with selective thresholds at various stages of the model ensure robust relevance computation mechanism for conceiving the best-in-class Knowledge Graph. Moreover, the optimization of the initial solution set is carried out using the Chemical Reaction metaheuristic optimization algorithm with NGD as the criterion function, ensuring the most relevant entities are alone conceived in the finalized Knowledge Graph. The proposed SMDKGG yields an overall Precision of 96.19%, Recall of 98.33%, Accuracy of 97.26%, F-Measure of 97.25%, and an FNR of 0.02. 

SMDKGG: A Socially Aware Metadata Driven Knowledge Graph Generation for Disaster Tweets

The simplest and effective way to store human knowledge through centuries was using text. Along with the advancement of technology nowadays, the volume of text has grown to be larger and larger. To extract useful information from this amount of text becomes an exceptionally complex task. As an effort to solve that problem, in this paper, we present a pipeline to extract core knowledge from large quantity text using distributed computing. The components of our pipeline are systems that were known to yield good results. The outputs of our proposed system are stored in a knowledge graph. A knowledge graph is a graph for storing knowledge in the form of triples (head, relation, tail). Some of the existing knowledge graphs in the world are Google knowledge graph, YAGO, DBLP, or DBpedia. These knowledge graphs have one thing in common—they are in English. The English language is studied by many researchers in the world and it had become a rich-resource language (with many natural language processing tools and data set). Vietnamese, on the other hand, is a low-resource language. Therefore, we use cross-lingual transfer method to build a Vietnamese knowledge graph. Firstly, we collect data in form of text about Vietnam tourism, which was written mostly in Vietnamese, using Google search and Wikipedia. In the next step, we translate them into English with Google Translate and use English Natural Language Processing tools like Stanford Parser, Co-referencing, ClausIE, MinIE to extract useful triples from this text. Lastly, the triples are translated back to Vietnamese to build a Vietnam tourism knowledge graph. Since we are working with massive text, we develop a distributed algorithm to extract triples from sentences of massive text. This is a distributed version of MinIE, which was originally developed for a single machine model. In Apache Spark framework, we divide massive text into many smaller parts and move them to the worker nodes with distributed MinIE function. Spark distributed MinIE will extract the triples of sentences in the local text of this worker node in parallel. Finally, the result of worker nodes will be sent back to the master node for building the knowledge graph. We conduct experiments with the distributed MinIE on spark cluster to prove the outperformance of our proposed algorithm.

Building a knowledge graph by using cross-lingual transfer method and distributed MinIE algorithm on apache spark

Using BERT and Knowledge Graph for detecting triples in Vietnamese text

Most data in the world is in form of text. Therefore, we can say text stores large amount of the knowledge of human beings. Extracting useful knowledge from text, however, is not a simple task. In this paper, we present a complete pipeline to extract knowledge from paragraph. This pipeline combines state-of-the-art systems in order to yield optimal results. There are some other Knowledge Graphs such as Google Knowledge Graph, YAGO, or DBpedia. Most of the data in these Knowledge Graphs is in English. On the other hand, the results from our system is used to build a new Knowledge Graph in Vietnamese of Vietnam Tourism. We use the rich resources language like English to process a low resources language like Vietnamese. We utilize the NLP tools of English such as Google translate, Stanford parser, Co-referencing, ClausIE, MinIE. We develop Google Search to find the text describing the entities in the Internet. This text is in Vietnamese. Then, we translate the Vietnamese text into English text and use English NLP tools to extract triples. Finally, we translate the triples back into Vietnamese and build the knowledge graph of Vietnam tourism. We conduct experiment and discover the advantages and disadvantages of our method.

Building a Knowledge Graph of Vietnam Tourism from Text

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Building a knowledge graph by using cross-lingual transfer method and distributed MinIE algorithm on apache spark

Using BERT and Knowledge Graph for detecting triples in Vietnamese text

Building a Knowledge Graph of Vietnam Tourism from Text