KBot: A Knowledge Graph Based ChatBot for Natural Language Understanding Over Linked Data

Ait-Mlouk, Addi; Jiang, Lili

doi:10.1109/access.2020.3016142

Cited by 92 publications

(49 citation statements)

References 22 publications

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“…With the rapid development of the semantic web, a large amount of structured data has been provided in the form of a knowledge based on the web. Making these data accessible and useful to end users is one of the main goals of chatbots based on link data [54]. KG is considered to be a new AI technology trend, which originated from the basic principles of the Semantic Web and the construction of the knowledge base [55].…”

Section: Discussionmentioning

confidence: 99%

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Chao

Trappey

2021

Complexity

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Natural language processing (NLP) is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals.

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Section: Discussionmentioning

confidence: 99%

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Chao

Trappey

2021

Complexity

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“…With the integration of NLU techniques, the system sets SPARQL queries to known Knowledge Bases and external web services. The extracted knowledge is gathered in a knowledge graph and with the help of a chatbot service the users receive their answer [145].…”

Section: Kg-based Ai Chatbotsmentioning

confidence: 99%

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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“…Recent studies have used knowledge graphs to enhance chatbot performance. More specifically, there are general purpose knowledge graph-based chatbots for transferring knowledge or conducting common conversations [20,21], chatbots for item searching and questioning in online shopping services [22], chatbots that provide tourism information [23], and domain-specific chatbots based on knowledge graphs for self-diagnosis, medical system assistance, and online diagnostic assistance [24][25][26][27].…”

Section: Knowledge Graph (Kg)mentioning

confidence: 99%

Deep Learning-Based Knowledge Graph Generation for COVID-19

2021

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Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.

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KBot: A Knowledge Graph Based ChatBot for Natural Language Understanding Over Linked Data

Abstract: This work is funded by Umeå University in Sweden on federated database research.

Cited by 92 publications

References 22 publications

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

Emerging Technologies of Natural Language‐Enabled Chatbots: A Review and Trend Forecast Using Intelligent Ontology Extraction and Patent Analytics

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

Deep Learning-Based Knowledge Graph Generation for COVID-19

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