Integration of chatbots with Knowledge Graphs in eGovernment: The case of Getting a Passport

Patsoulis, Georgios; Promikyridis, Rafail; Tambouris, Efthimios

doi:10.1145/3503823.3503901

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…In chatbots, a KG is usually built upon existing modules to connect all gathered information, bridging structured and unstructured information, and representing this information in Figure 1: An example of a KG of two-dimensional shape ("bangun datar"). a clear and structured form to users [5]. Another example of the usage of KG is in Kbot to select responses and provide feedback to the user.…”

Section: B Knowledge Graph (Kg)mentioning

confidence: 99%

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The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Salsabila

2024

Arch. Sci.

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The development of chatbot systems for mathematics courses in elementary school has gained significant attention due to their potential to enhance the learning experience. This study proposes a novel approach that combines knowledge graph (KG) and Named Entity Recognition (NER) methods using Neo4j and SpaCy within the Rasa Open-Source v3.0 platform as chatbot frameworks. The knowledge graphs represent mathematical concepts and their relationships, enabling the chatbot to provide accurate and relevant responses to user queries. The NER SpaCy is employed to identify and extract mathematical entities from user inputs, ensuring a precise understanding of the context. The integrations of Neo4j and NER using SpaCy with Rasa Open-Source v3.0 facilitate efficient information retrieval and improve the conversational abilities of the chatbot. Experimental results demonstrate the effectiveness of the proposed approach, showcasing its potential as an educational tool for fifth-grade students in elementary schools.

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Section: B Knowledge Graph (Kg)mentioning

confidence: 99%

“…It provides efficient storage and retrieval of information, making it ideal for building intelligent chatbot systems [4]. Patsoulis et al provided chatbot integration with knowledge graphs in e-government [5]. Timon-Reina et al proposed graph databases and their implementations in the biomedical fields [6].…”

Section: Introductionmentioning

confidence: 99%

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Salsabila

2024

Arch. Sci.

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“…How can response generation be implemented? All of the above types utilize some type of knowledge graph to formalize the configuration [70,6] and the intended output format of the conversation [50,3] [22], as well as BPMN based solutions to encode potential progressions of a conversation [55] have been proposed. One example of such a system is PACA [38].…”

Section: How Do Chatbots Work and What Are Important Areas Of Applica...mentioning

confidence: 99%

Conversational Process Modelling: State of the Art, Applications, and Implications in Practice

Klievtsova,

Benzin,

Kampik

et al. 2023

Lecture Notes in Business Information Processing

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Chatbots such as ChatGPT have caused a tremendous hype lately. For BPM applications, it is often not clear how to apply chatbots to generate business value. Hence, this work aims at the systematic analysis of existing chatbots for their support of conversational process modelling as process-oriented capability. Application scenarios are identified along the process life cycle. Then a systematic literature review on conversational process modelling is performed. The resulting taxonomy serves as input for the identification of application scenarios for conversational process modelling, including paraphrasing and improvement of process descriptions. The application scenarios are evaluated for existing chatbots based on a real-world test set from the higher education domain. It contains process descriptions as well as corresponding process models, together with an assessment of the model quality. Based on the literature and application scenario analyses, recommendations for the usage (practical implications) and further development (research directions) of conversational process modelling are derived.

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“…The extracted entities are used to query Wikidata and thus obtain information to respond to users. In another work, a chatbot was developed to respond to various e-government initiatives in order to provide citizens with personalized information about public services [3]. This chatbot was built using the RASA framework combined with a knowledge graph designed to provide information to the chatbot.…”

Section: Introductionmentioning

confidence: 99%

ChatBot for student service based on RASA framework

Fonseca

Rodrigues

2023

Preprint

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The availability of face-to-face attendance at the School’s Administrative Services for Students is limited to one schedule, which may prevent the timely clarification of students' questions, causing a decrease in their level of satisfaction. To solve this problem, a conversational agent was designed, consisting of a Portuguese language interpretation module using natural language processing and machine learning techniques. To keep the system abstracted from any technical dependency, a web service that manages the agent's knowledge base was developed. In the evaluation of the solution, the performance of several learning models was compared, and the results emphasize the superiority of BERT language model of Google, combined with the DIET classifier, obtaining a F1-Score of 0.965. The system was implemented through a prototype and, for a total of 256 questions, around 70% of correct responses were obtained, with a positive average satisfaction rating of 4.20 on a 0-5 scale.

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Integration of chatbots with Knowledge Graphs in eGovernment: The case of Getting a Passport

Cited by 12 publications

References 8 publications

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Conversational Process Modelling: State of the Art, Applications, and Implications in Practice

ChatBot for student service based on RASA framework

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