Legal queries are often expressed in unstructured, ambiguous, or noisy natural language, which poses significant challenges for accurate information retrieval. This paper presents a comprehensive framework for legal question answering that integrates semantic technologies—including an ontology platform, knowledge graph, and large language models (LLMs)—to improve question understanding and response accuracy. We propose a multi-step pre-processing pipeline that standardizes legal questions using spelling correction, abbreviation expansion, syntactic restructuring, and semantic summarization supported by LLMs. Besides, a query system is designed that maps pre-processed questions into graph-based representations and performs subgraph matching over a legal knowledge graph. The system is evaluated on real-world legal questions collected from online forums covering traffic law and social insurance. The results show that the proposed approach achieves a high semantic similarity score (avg. cosine similarity of 0.9078 after standardization) and outperforms baseline LLMs like ChatGPT and Gemini in query accuracy (up to 82.25% in certain question categories). These findings highlight the effectiveness of combining LLMs and semantic structures for robust legal information retrieval.

In Vietnam, accessing accurate and personalized legal advice remains a significant challenge due to the complexity of legal documents and limited public legal literacy. This paper introduces an intelligent legal consulting system that integrates three key components: the Legal-Onto knowledge representation model, the VietnamLegalText-SBERT embedding model, and a ReAct-based multi-agent framework. Legal-Onto organizes legal knowledge into semantic graphs of legal concepts, capturing internal relationships, definitions, and key attributes from legal texts. Each node in the knowledge graph is embedded using VietnamLegalText-SBERT—a BERT-based model fine-tuned on Vietnamese legal corpora—and stored in Elasticsearch to enable efficient vector-based retrieval. A multi-agent architecture facilitates step-by-step reasoning, query decomposition, document retrieval, legal interpretation, and output validation. The system is evaluated on real-world legal queries collected from public sources, showing superior performance over a general-purpose GPT-4o baseline in terms of answer accuracy, citation correctness, and contextual relevance. The results demonstrate that combining domain-specific semantic modeling with reasoning agents yields interpretable, reliable, and context-aware legal support, paving the way for broader public access to legal guidance in Vietnam.

Nowadays online learning is growing strongly, with many diverse options. Learners can utilize electronic devices and internet platforms for studying, searching for materials, and looking up knowledge. In this study, a method for design a knowledge chatbot system in education is proposed. This method includes a knowledge base, which is a knowledge model integrating ontology of relations and operators and knowledge graph, and the algorithms for solving problems on intellectual querying. The proposed method is utilized to build an intelligent chatbot system in the course of Foudation of Database. This chatbot can assist the online learning by querying subject knowledge content, categorizing concepts, and providing support for various types of exercises in the subject. The experimental results show that the built system meets the requirements of an intelligent educational system and outperforms than currently GenAI systems.

The revelation of cognitive knowledge, development, and planning of distinct economic societies in human civilization have mostly been influenced by historically asymmetric availability or ownership of Data Asset, Information Asset, Knowledge Asset, and Wisdom Asset. The availability of asymmetric information and the asymmetry of demand for commercial goods or services serve as the foundation for many economic models and theories. However, from the DIKWP (Data, Information, Knowledge, Wisdom, and Purpose) Capital materialization and DIKWP Governance perspective, with the rapidly development of information technology and widely progressing digital communication facilities, Asymmetric Information Economy is increasingly replaced with essentially Symmetric Knowledge Economy and Symmetric Wisdom Economy. The inevitability of the replacement of Asymmetric Economy by Symmetric Economy in terms of DIKWP-12-Chains is formalized, and proposed uniformly semantic processing crossing DIKWP.

Nowadays the video game market is the highest in the entertainment industry. There are many architectures supporting the creation of a game, such as Unreal Engine and Unity. Multi-agent system is a popular and suitable solution to design platform games. It is employed to place streets, buildings and other items, resulting in a playable video game map. The system utilizes computational agents that act in conjunction with the human designer to produce maps that exhibit desirable characteristics. This paper proposes a new architecture for organizing in game development based on the multi-agents system (MAS). This architecture includes the structure of an agent with its attributes and internal behaviors, and the structure of relations between agents for action coordination in a determined strategy This structure of MAS is also applied to build a demonstration video game as an action game through Unreal Engine, which is a complete suite of development tools made for anyone working with real-time technology.

Buying a house is considered to be an increasing necessary demand. However, choosing the right one is a complex and time-costing decision. In this research, a method for knowledge representation of an expert system in real-estate investment is proposed. This method organizes the knowledge base of the system based on combining market surveying data in real-estate for each category of customers. Through that, a content-based searching system using the personal demographics, as an expert system, is proposed. This system supports both customer and real estate agent to find the suitable house fitting their desire and budget. This system is designed to extract information of the customer information to enhance the conventional searching method which only treat house property as the only input.

Social network is one of efficient tools for spreading information. The evaluation of the content creation of a user is a useful feature to improve the ability of information propagation on social network. In this paper, the measures for evaluating the user’s content creation are proposed. They include the passion point of a user with a brand and the quality of the user’s posts. The passion point is computed based on the sentiment score of posting and the activity of the user. The quality of the user’s posts is computed through the analyzing of the post’s content. Those measures are combined to analyze the interesting of posts. The proposed method has been tested and get the positive experimental results.

The active learning methods bring enthusiasm for studying of students, especially in learning of algorithms and programming. Graphical and dynamic web based tools as visualization in learning these courses make more attractive to learners for spending much more their study time. In this paper, a method for designing an Intelligent system in learning of algorithms is proposed. This method proposes the model for representation the knowledge of an algorithm, called Algo-model. This model has the structure of tasks which are actions in the algorithm's processing. The proposed method also includes the processing to visualize the happen change in an algorithm's running. It will visualize on three contents synchronously: displaying the current state of the algorithm, results of data structures at this state, and visualizing this current state on the graphic environment. Based on the proposed method, an Intelligent learning system for Graph Theory at university has been constructed. This system meets requirements of an Intelligent system in learning of algorithms. It is useful for students to study and understand how the algorithm runs. It can interact to the student step-by-step based on the visualization of algorithms in learning tutors.