An Ontology-Based Conversation System for Knowledge Bases

Quamar, Abdul; Lei, Chuan; Miller, Dorian; Özcan, Fatma; Kreulen, Jeffrey; Moore, Robert J.; Efthymiou, Vasilis

doi:10.1145/3318464.3386139

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…IBM offers free (limited), standard, and premium plans for the Watson Assistant. Watson Assistant is designed to work perfectly with the internal components that IBM is offering, such as IBM cloud [92,117,118,123,127,130,131].…”

Section: Ibm Watson Assistant (Iwa)mentioning

confidence: 99%

“…There are many proposed approaches to the recent bibliography that introduce KGs to AI chatbots and QA systems. Hallili [130] has proposed the SynchroBot, a dialog system that a) has connectivity to robust and flexible KBs and KGs for extracting in-formation, and b) could use NLP tools to interpret user's questions and NLG techniques to provide proper answers. This ability differs from other QA systems which are either focusing in providing a logical conversation ability to the users, without caring for the richness of their knowledge source, or, on the other hand, can only provide accurate answers without any conversation skills.…”

Section: Kg-based Ai Chatbotsmentioning

confidence: 99%

“…The researchers chose to develop an OWL ontology-based chatbot because their need was to develop a domain specific chatbot that will be able to provide robust and persistent context to its users. Their methodology relies on knowledge extraction techniques and in the assistance of domain experts which refine and train the chatbot system [130].…”

Section: Kg-based Ai Chatbotsmentioning

confidence: 99%

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Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

et al. 2021

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Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI’s need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.

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Section: Ibm Watson Assistant (Iwa)mentioning

confidence: 99%

Section: Kg-based Ai Chatbotsmentioning

confidence: 99%

Section: Kg-based Ai Chatbotsmentioning

confidence: 99%

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Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

et al. 2021

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“…Hence, a new knowledge-driven automatic approach is needed for GP design to overcome the abovementioned shortages in garment mass customization. Domain-specific knowledge bases (KBs), as curated information repositories, can offer invaluable insights and references to support users making better decisions in their applications [10] and has been successfully implemented in many critical economic industrial fields, such as aeronautic, automotive and pharmaceutical industry [11][12][13]. In fashion industry, the implementation of KB has also attracted increasingly attentions in recent years.…”

Section: Introductionmentioning

confidence: 99%

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Wang¹,

Zeng²,

Tao³

et al. 2021

IJCIS

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Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design.

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“…With ontologies becoming increasingly important across many industries (see for instance [1,2,3]), the need for a strategic debugging methodology is clear. Furthermore, ontologies are being applied to more abstract and complex domains such as business and law.…”

Section: Introductionmentioning

confidence: 99%

Debugging Classical Ontologies Using Defeasible Reasoning Tools

Coetzer

Britz

2021

Frontiers in Artificial Intelligence and Applications

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A successful application of ontologies relies on representing as much accurate and relevant domain knowledge as possible, while maintaining logical consistency. As the successful implementation of a real-world ontology is likely to contain many concepts and intricate relationships between the concepts, it is necessary to follow a methodology for debugging and refining the ontology. Many ontology debugging approaches have been developed to help the knowledge engineer pinpoint the cause of logical inconsistencies and rectify them in a strategic way. We show that existing debugging approaches can lead to unintuitive results, which may lead the knowledge engineer to opt for deleting potentially crucial and nuanced knowledge. We provide a methodological and design foundation for weakening faulty axioms in a strategic way using defeasible reasoning tools. Our methodology draws from Rodler’s interactive ontology debugging approach and extends this approach by creating a methodology to systematically find conflict resolution recommendations. Importantly, our goal is not to convert a classical ontology to a defeasible ontology. Rather, we use the definition of exceptionality of a concept, which is central to the semantics of defeasible description logics, and the associated algorithm to determine the extent of a concept’s exceptionality (their ranking); then, starting with the statements containing the most general concepts (the least exceptional concepts) weakened versions of the original statements are constructed; this is done until all inconsistencies have been resolved.

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An Ontology-Based Conversation System for Knowledge Bases

Cited by 21 publications

References 25 publications

Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Debugging Classical Ontologies Using Defeasible Reasoning Tools

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