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Fast technology development in recent years has led to a rapid increase in data generation. Most of the data are raw and need to be classified and processed into clean, usable data. However, classifying a huge volume of data is not an easy task, and it requires massive human efforts to label the data. To assist humans and shorten this labelling process time, data clustering is one of the most used methods in the first step of data cleaning. Among popular data clustering algorithms, Hierarchical Clustering is one of the best that can help to separate noise to recognize data patterns. In this paper, we conducted intensive experiments to verify the effectiveness of Hierarchical Clustering on multiple datasets using Apache Spark. We evaluated Hierarchical Clustering by comparing its performance in different sizes. Their volumes are provided in the experiment section.

This paper presents a method for building a real estate chatbot automatically to support customers in Vietnamese. The chatbot is trained with data set collected on Facebook groups and from the famous real estate website in Vietnam. Using Logistic Regression, user’s intent recognition task achieves precision = 0.93, recall = 0.87 and F1-score = 0.89, while the automatic entity labeling achieves 83% accuracy thanks to the development of a real estate knowledge base. Besides, we report our experience on the design of dialog management modules.

The number of smart phone and smartphone users has increased rapidly, boosting the growth of mobile advertising. Predicting the percentage of users who click on an advertisement when displayed on an application (CTR: Click Through Rate) plays an important role in choosing an advertising push strategy for each application. However, because the field of mobile advertising is still new, the study of solutions to these problems is only for Web advertising. This paper presents a new predictive method based on Matrix Factorization (MF) algorithm. The paper also proposes a method of integrating the reliability of data into MF algorithms. The method was tested on actual data provided by Amobi advertising company with 1.7 million records including 300 advertisements and 400 mobile device applications. The AUC (Area Under The Curve) value obtained on the test set is 0.9037 with MF and 0.9411 when integrating reliability into the MF algorithm. Thus, the results show that the predictive quality is significantly improved when integrating data reliability and evaluation into MF algorithms.

DBpedia is a community project upon extracting information from Wikipedia and standardize this data with Semantic Web and Linked Data principles. Therefore, it is a large-scale knowledge base and considered as one of the currently emerging heated issues in the subject of Semantic Web. Moreover, thanks to the development of DBpedia community, many more opportunities are created in the Linked Open Data (LOD) area. These lead to giving birth to the specific DBPedia chapters in different languages. However, there are many languages that do have not their own chapters including Vietnamese. The reason is that the extraction procedure of DBpedia requires manual mappings which match Wikipedia templates to corresponding types and properties of DBpedia ontology. Thus, in this paper, we describe how to identify automatically types for Vietnamese entities as the first step toward building the Vietnamese DBpedia chapter.

This paper focuses on describing the process of building a linked open data repository for presenting tourist resources, which presently contains more than 100,000 places in big cities of Vietnam. This information is extracted and integrated from various sources on the Internet, which are famous social networks or tourism websites, for example, Booking.com, Mytour, Vinalo, etc. These massive and heterogeneous materials would be described via a semantic knowledge base presented in RDF/XML format that is accessible by SPARQL query. Concentrating in studying the concept of linked open data with its publishing processes, this work adjusts and improves the repository to become a standardized linked open data and allows it connect with other famous ontology such as DBpedia.

Purpose – The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems. Design/methodology/approach – The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (name...

Part 1: Semantic Concepts and Open Data; International audience; News on the Internet today plays an important role in helping people access daily information around the world. News aggregators are websites that collect and provide content from different sources in one location for easy viewing. However, the increasing number of news on the Internet makes it difficult for readers when they desire to access news they are concerned. One solution to this issue is based on employing recommender systems. In this research, we propose a novel method for news recommendation based on a combination of semantic similarity with content similarity between news and implement it as a feature of semantic-based news aggregators BKSport. Experimental results have shown that, a combination of both kind of similarity measures will result in better recommendation than when using either measure separately.

In education organizations, a number of quotidian and duplicated consulting questions for training programs, university regulations... make difficulties, inconvenient and time-consuming for consultants. This work proposes a method to suggest answers of similar questions, which may help both students and consultants in orientation. This method includes two main tasks: (i) query generation which extracts key phrases from input questions, (ii) searching which find out most similar questions with their answers and relevant scores using extracted key phrases. For the first task, a check strategy was used to build key phrase candidates and the Naive Bayes Classifier was used to select key phrases. The two different approaches were experimented for the second one: (i) Similarity Comparison Searching, and (ii) Sorl Search Engine. The precision of the key phrase extraction is about 69 %. BKCA, an e-consultancy system for SoICT-HUST, was built and experimented with about 46 % relevant question-answer pairs for the Similarity Comparison searching approach, and about 39 % for the Sorl Search Engine one.

In news genre, the sport domain is one of great interest to audiences on many occasions. The explosion of Internet these days leads to many obstacles for users in searching information due to enormous amount of data collected from multiple media streams. In this case, Semantic Web is believed to bring a solution for information integration. In this paper, we introduce a novel approach for an integration system of sports information using semantic web. Semantic annotation of sport news on the web is a way to bring success to our system. We propose an algorithm to generate semantic annotations of sport news that relies on the ontology. Named entities recognition is improved by integrating the KIM information extraction system with this ontology, which is dedicated for sport domain. The concepts and relations of ontology detected in the text are combined with these entities to capture the semantic. Initial experiments on soccer news demonstrated promising results.

International audience; Cet article décrit brièvement un système multi-agents gérant un Web sémantique d'entreprise avant de se focaliser sur l'extension de ce système par une nouvelle société d'agents responsables de l'intégration au web sémantique interne de l'organisation, de ressources d'information disponibles sur le Web ouvert et utiles à son activité. Cette nouvelle société d'agents repose entièrement sur la technologie XML pour extraire les informations et les mettre à disposition.