OntoVPA—An Ontology-Based Dialogue Management System for Virtual Personal Assistants

Wessel, Michael; Acharya, Girish; Carpenter, J. P.; Yin, Min

doi:10.1007/978-3-319-92108-2_23

Cited by 18 publications

(18 citation statements)

References 7 publications

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“…This information is then used to fill information slots, changing the state of the dialogue and, therefore, advancing the interaction. More recently, Wessel et al [ 18 ] proposed a platform named OntoVPA, an ontology-based DS for developing conversational virtual personal assistants. Their system provides ontologies for dialogue and domain representation that are used for analysing the user’s utterances and filling the slots.…”

Section: Related Workmentioning

confidence: 99%

“…), while they allow to re-use the slot-filling mechanism for all tasks. In this category, the approach proposed by Wessel et al in [ 18 ] relies on ontologies, so in this case, adapt the system to new tasks, or domains, would require to expand the ontologies with domain-specific information. Pealtson et al were able to overcome the general lack of scalability and re-usability of state-based approaches by finding generic interaction patterns that can be used to develop different tasks by parametrising these patterns differently.…”

Section: Related Workmentioning

confidence: 99%

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Modelling Multimodal Dialogues for Social Robots Using Communicative Acts

Fernández-Rodicio

Castro‐González

Alonso-Martín

et al. 2020

Sensors

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Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot’s applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human–robot interactions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts

Fernández-Rodicio

Castro‐González

Alonso-Martín

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In this system, the conversation with the user is driven by keeping past conversations in memory, which is used as a basis to analyse new information. On the other hand, there is also an ontology-based dialogue system which creates domain model representation, applies reasoning rules, suggests the appropriate dialogue representation, and tracks older dialogue states to result in the most appropriate system response [35]. These studies converge in the great contribution that ontologies provide in a dialogue-management process.…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Conversational Agent in the Rehabilitation Domain

et al. 2019

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Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.

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“…User-defined rules on top of the ontology allow expressing richer semantic relations that lie beyond OWL's expressive capabilities and couple ontological and rule knowledge (Krisnadhi, Maier, & Hitzler, 2011). In this context, OntoVPA (Wessel, Acharya, Carpenter, & Yin, 2017) uses ontology reasoning and rules to generate responses and handle polysemy and ambiguity. In Tielman, Van Meggelen, Neerincx, and Brinkman (2015), reasoning is used to generate personalised questions to assist individuals with memory recollection.…”

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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OntoVPA—An Ontology-Based Dialogue Management System for Virtual Personal Assistants

Cited by 18 publications

References 7 publications

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts

Modelling Multimodal Dialogues for Social Robots Using Communicative Acts

A Context-Aware Conversational Agent in the Rehabilitation Domain

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

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