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Ontology-Based Learning Assistant Chatbot: Enhancing Accurate and Explanatory Knowledge Provision in Myanmar’s Primary Education
(2025-01-01)
Myo, Su Wai
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Anutariya, Chutiporn
Large language models (LLMs) and LLM-based generative AI tools have demonstrated considerable effectiveness in educational settings by challenging traditional classroom dynamics. They generate answers based on knowledge acquired during pre-training, making the answer construction process and the sources of information ambiguous. This uncertainty in responses complicates the assurance of appropriateness and reliability for young students, particularly in primary education. This paper, therefore, proposes an ontology-based learning assistant chatbot designed to address students’ inquiries using Subject Ontology (SO), which was developed for primary school teachers to model and verify subject knowledge. The chatbot aims to alleviate common academic challenges in Myanmar’s primary education. From the evaluation, teachers valued the chatbot’s transparency and reliability, as they could maintain direct control over the underlying knowledge base, enabling them to efficiently verify the accuracy of the chatbot’s responses and their sources.
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Intelligent approach to automated star-schema construction using a knowledge base
(2021-11-15)
Sanprasit, Non
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Jampachaisri, Katechan
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Titijaroonroj, Taravichet
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Kesorn, Kraisak
Most data-warehouse construction processes are performed manually by experts, which is laborious, time-consuming, and prone to error. Furthermore, special knowledge is required to design complex multidimensional models, such as a star schema. This predicament has motivated computer scientists to propose automation techniques to generate such models. For this reason, we present a new strategy that incorporates knowledge-based models into a framework, named the Semantic-based Star-schema Designer, that assists the automation of star schema construction. Our models provide reasoning capabilities needed by star schema designs, including those that can disambiguate heterogeneous terms, detect appropriate data types and attribute sizes, and organize data hierarchies to support online analytical processes. We also propose strategies to overcome the uncertainty arising when attribute names are not available in the data source. The names of unknown attributes are thus predicted using an arithmetic coding technique to infer column names. Our system also generates star schema from semi-structured data (e.g., comma-separated-value files and spreadsheets), which do not provide primary keys, foreign keys, or relationship cardinalities between tables. Our framework facilitates star schema construction and their relationship information without human intervention using homegrown algorithms. Experiments demonstrate that our technique predicts column names and data types that enable the effective generation of star schema better than baseline approaches.
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Some key issues in information systems, databases and big data integration
(2019-07-01)
Chittayasothorn, Suphamit
Databases and big data integration are keywords which attract attentions from both government and private sectors' administrators. In general, the term big data refers to data of large volume which come in different varieties and high velocity. Typical big data sources are from sensors of various kinds and social media. Transactional data from enterprise information systems with traditional structured databases are also major data sources for management decision support systems. This paper presents some key big data and database integration issues and suggests the level of integration required.
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Event extraction using ontology directed semantic grammar
(2016-01-01)
Kuptabut, Suthasinee
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Netisopakul, Ponrudee
The task of extracting and constructing knowledge base from news is still a subject of ongoing research. The obtained knowledge base is useful for many applications, such as a question answering system. Football news always gains enormous interest from many football fan clubs. Hence, there are needs to extract certain information from this news in timely fashion. This paper proposes a new approach to extract football events from football news webpages and represents them as frames. One of the main contributions of the new approach is the process of dynamically generating semantic grammar from football domain ontology. A semantic parser uses this grammar to recognize events of interest and their details. This paper explains the overall architecture of our system called Ontology Directed Event Extraction System and explains detail implementation of our new proposed approach for extracting football events. The system is demonstrated by extracting events from 40 football news webpages, resulting in 699 frames which evaluated over 85% precision and recall.
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Automated Compliance Checking Methodology for Non-Log Operations
(2015-12-31)
Janpitak, Nanta
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Sathitwiriyawong, Chanboon
Compliance Management (CM) is the management process that an organization implements to ensure organizational compliance with relevant requirements and expectations. The most complicated, time-consuming, and costly process in CM is compliance checking because it requires a person who has a good knowledge in policy to examine whether the current operations meet the policy requirements. Many researchers have tried to study better ways to automate the compliance checking process, but most of them require the operation logs in to the computer systems. This paper proposes a methodology to enable the automation of compliance checking for those operations that have no log in computer systems by using questions and answers principle to cooperate with the semantic web technologies. Since there are some operations that cannot be understood by computer systems, using questions is one way to gather the answers, such as operation log to evaluate their compliance. The proposed methodology can help noncertified auditors perform the compliance checking so that the time and cost of compliance checking would be greatly reduced.
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Semi-automatic relational databases integration using ontology
(2013-12-16)
Phungtua-Eng, Thanapol
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Chittayasothorn, Suphamit
Database integration is an organization-level problem which has become more and more serious when correct and unified databases are required to support business intelligence and management decision making. In many organizations, information systems are developed independently. The database that supports the human resource management system, for example, is developed and implemented separately from the database that supports the main production system. Different naming conventions, data types, and values in such different databases make it hard to consider if the data items from different databases refer to the same real-world objects. The problems become worse in the case that databases have different data structures; different data models. Polyglot environments may be a solution for operational systems but may turn to be problems for decision support systems. This paper presents an approach to the database integration problem. Ontology is used as a central knowledge base where data items and relationships are identified and resolved. Since the database integration process must yield perfect or close to perfect result, any mismatches or errors are not acceptable and user involvements are required. Hence, a semi-automatic approach is adopted.
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Ontology-based nutrient solution control system for hydroponics
(2011-12-01)
Phutthisathian, Areeworn
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Pantasen, Nakulrad
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Maneerat, Noppadol
This paper presents the Ontology-based Nutrient Solution Control System for Hydroponics with Protégé. We consider variables of electrical conductivity (EC), Potential of Hydrogen ion (pH), intensity of solution, species of plants and the relation of the device in the system for supporting to make the suitable decisions with the control system of hydroponic nutrient solution. © 2011 IEEE.
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A meta-reasoning approach for reasoning with SWRL ontologies
(2011-07-26)
Hirankitti, Visit
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Trang, Mai Xuan
SWRL is designed for enhancing inferential power on OWL ontologies by introducing rules to the language. With SWRL, rules are allowed to combine with OWL ontologies in order to support deduction on the semantic web ontologies. Earlier we have developed a meta-logical approach for reasoning with Semantic Web ontologies expressed in OWL (51 and OWL 2 [71; with the advent of SWRL, in this paper we shall extend our framework to support OWL with rules, and hence to support SWRL. Meta-languages together with a meta-interpreter, defined by demo ( . ) predicate, are proposed and used for reasoning with Semantic Web ontologies with rules.
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Ontology directed semantic annotation process
(2010-10-11)
Kuptabut, Suthasinee
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Netisopakul, Ponrudee
This paper proposes a process for annotating semantic concepts to sentences excerpted from webpages. The annotation process is guided by a domain specific ontology and an entities knowledge base. The overall process has four steps: extracting textual contents from a webpage, selecting relevant sentences, extracting clauses and phrases from a sentence and assigning concepts to phrases. The process is demonstrated using weather news webpages.
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Provision of Thai herbal recommendation based on an ontology
(2010-08-23)
Kato, Takumi
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Maneerat, Noppadol
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Varakulsiripunth, Ruttikorn
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Izumi, Satoru
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Takahashi, Hideyuki
In Thailand, most of people like to use Thai herbs for their traditional medical treatments. However, since there are various sorts of Thai herbs, and the Thai herbal knowledge is complicated, it is difficult to find an appropriate one for each health condition. In order to help people to find suitable Thai herbs to cure the diseases, we have developed a system to provide herbal recommendations to users regarding their symptoms. In the development, we represented the Thai herbal knowledge in an ontology, and extracted new facts based on the ontology. Finally, the system provides appropriate herbal recommendations to users based on extracted facts and the ontology. This paper shows how we represented Thai herbal knowledge in an ontology, and how we extract the new facts based on the ontology, as well as how the system provides herbal recommendations to users. © 2010 IEEE.

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