Using Ontology Reasoning in Building a Simple and Effective Dialog System for a Smart Home System

Huang, Cheng-Chi; Liu, Alan; Zhou, Pei-Chuan

doi:10.1109/smc.2015.267

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…Ontology has been utilized in a lot of research studies to improve the performance of speech recognition or synthesis (Elsayed and Fathy 2020;Huang et al 2015;Reedoy et al 2021). Moreover, the results of using ontology in speech-based user interfaces have been very promising.…”

Section: Related Workmentioning

confidence: 99%

Detecting Persian speaker-independent voice commands based on LSTM and ontology in communicating with the smart home appliances

2022

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Nowadays, various interfaces are used to control smart home appliances. The human and smart home appliances interaction may be based on input devices such as a mouse, keyboard, microphone, or webcam. The interaction between humans and machines can be established via speech using a microphone as one of the input modes. The Speech-based human and machine interaction is a more natural way of communication in comparison to other types of interfaces. Existing speech-based interfaces in the smart home domain suffer from some problems such as limiting the users to use a fixed set of pre-defined commands, not supporting indirect commands, requiring a large training set, or depending on some specific speakers. To solve these challenges, we proposed several approaches in this paper. We exploited ontology as a knowledge base to support indirect commands and remove user restrictions on expressing a specific set of commands. Moreover, Long Short-Term Memory (LSTM) has been exploited for detecting spoken commands more accurately. Additionally, due to the lack of Persian voice commands for interacting with smart home appliances, a dataset of speakerindependent Persian voice commands for communicating with TV, media player, and lighting system has been designed, recorded, and evaluated in this research. The experimental results show that the LSTM-based voice command detection system performed almost 1.5% and 13% more accurately than the Hidden Markov Model (HMM)-based one, in scenarios 'with' and 'without ontology', respectively. Furthermore, using ontology in the LSTM-based method has improved the system performance by about 40%.

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Section: Related Workmentioning

confidence: 99%

Detecting Persian speaker-independent voice commands based on LSTM and ontology in communicating with the smart home appliances

2022

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“…Furthermore, [11] developed a system to control several devices through applications within a house, known as Smart Home; the system uses voice to control devices and ontologies represent the information. The main aim is to execute specific tasks within the environment.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Question Answering System for an Academic Domain

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et al. 2021

NLP Techniques and Applications

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The development of question answering (QA) systems involves methods and techniques from the areas of Information Extraction (EI), Natural Language Processing (NLP), and sometimes speech recognition. A user interface that involves all these tasks requires deep development to improve the interaction between a user and a device. This paper describes a Spanish QA system for an academic domain through a multi-platform user interface. The system uses a voice query to be transformed into text. The semi-structured query is converted into SQWRL language to extract a system of ontologies from an academic domain using patterns. The answer of the ontologies is placed in templates classified according to the type of question. Finally, the answer is transformed into a voice. A method for experimentation is presented focusing on the questions asked in voice and their respective answers by experts from the academic domain in a set of 258 questions, obtaining a 92% accuracy.

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“…Ontological reasoning is used in Vanrompay, Mustapha, and Aufaure () to infuse user preferences into the responses generated by the agent, as well as in Noh, Lee, and Lee () to infer answers from ontologies as domain knowledge for information‐seeking of complicated questions, which cannot be answered with simple domain information. In Huang, Liu, and Zhou (), reasoning is used to map user input to services that control smart devices, as well as to answer contextual questions, for example, the location of certain people inside the house.…”

Section: Background and Related Workmentioning

confidence: 99%

Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems

et al. 2019

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Dialogue‐based systems often consist of several components, such as communication analysis, dialogue management, domain reasoning, and language generation. In this paper, we present Converness, an ontology‐driven, rule‐based framework to facilitate domain reasoning for conversational awareness in multimodal dialogue‐based agents. Converness uses Web Ontology Language 2 (OWL 2) ontologies to capture and combine the conversational modalities of the domain, for example, deictic gestures and spoken utterances, fuelling conversational topic understanding, and interpretation using description logics and rules. At the same time, defeasible rules are used to couple domain and user‐centred knowledge to further assist the interaction with end users, facilitating advanced conflict resolution and personalised context disambiguation. We illustrate the capabilities of the framework through its integration into a multimodal dialogue‐based agent that serves as an intelligent interface between users (elderly, caregivers, and health experts) and an ambient assistive living platform in real home settings.

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Using Ontology Reasoning in Building a Simple and Effective Dialog System for a Smart Home System

Cited by 9 publications

References 12 publications

Detecting Persian speaker-independent voice commands based on LSTM and ontology in communicating with the smart home appliances

Detecting Persian speaker-independent voice commands based on LSTM and ontology in communicating with the smart home appliances

Ontology-Based Question Answering System for an Academic Domain

Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems

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