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In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.

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On social networks, each message has many features where the interested topics and the actors sending and receiving topics are important features. Unlike the traditional approach, which views each message belonging to a topic, the topic model is based on the approach, which indicates that each message has a mixture of many topics. However, topic model has limitations about discovering interested topics of actors with temporal factor and labelling latent topics. The article proposes a temporal-author-recipient-topic (TART) model based on: (i) discovering interested topics and analyzing the role of actors on social networks with the temporal factor; (ii) labelling the latent topics from topic model based on topic taxonomy; (iii) applying the temporal factor for finding the relation among factors in model; and (iv) finding out the variation of interested topics of actors with each period of time. An experimenting TART model on two corpora with 1,004,396 messages in Vietnamese and 25,009 actors by the software is built for SNA.