scholarly journals A Modular System Oriented to the Design of Versatile Knowledge Bases for Chatbots

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Giovanni Pilato ◽  
Agnese Augello ◽  
Salvatore Gaglio
Keyword(s):  
Corpus Callosum ◽  
Knowledge Representation ◽  
User Behavior ◽  
Knowledge Bases ◽  
Modular System ◽  
Conversational Agent ◽  
Proof Of Concept ◽  
Conversational Agents ◽  
Specific Domain ◽  
Representation Techniques

The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate.

scholarly journals Propagators and Solvers for the Algebra of Modular Systems

10.29007/t7r9 ◽  
2018 ◽  
Author(s):  
Bart Bogaerts ◽  
Eugenia Ternovska ◽  
David Mitchell
Keyword(s):  
Knowledge Representation ◽  
Learning Algorithm ◽  
Knowledge Bases ◽  
Black Box ◽  
Modular System ◽  
General Notion ◽  
Combined System ◽  
Modular Systems ◽  
Problem Solvers ◽  
Model Expansion

Solving complex problems can involve non-trivial combinations of distinct knowledge bases and problem solvers. The Algebra of Modular Systems is a knowledge representation framework that provides a method for formally specifying such systems in purely semantic terms. Many practical systems based on expressive formalisms solve the model expansion task. In this paper, we con- struct a solver for the model expansion task for a complex modular system from an expression in the algebra and black-box propagators or solvers for the primitive modules. To this end, we define a general notion of propagators equipped with an explanation mechanism, an extension of the algebra to propagators, and a lazy conflict-driven learning algorithm. The result is a framework for seamlessly combining solving technology from different domains to produce a solver for a combined system.

MODULAR KNOWLEDGE REPRESENTATION IN ADVISOR AGENTS FOR SITUATION AWARENESS

2011 ◽  
Vol 05 (01) ◽  
pp. 33-53 ◽  
Author(s):  
GIOVANNI PILATO ◽  
AGNESE AUGELLO ◽  
SALVATORE GAGLIO
Keyword(s):  
Knowledge Representation ◽  
Design Process ◽  
Situation Awareness ◽  
Strategic Decision ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Decision Under Uncertainty ◽  
Semantic Computing

A modular knowledge representation framework for conversational agents is presented. The approach has been realized to suit the situation awareness paradigm. The modularity of the framework makes possible the composition of specific modules that deal with particular features, simplifying both the chatbot design process and its smartness. As a proof of concepts we have developed a modular, situation awareness oriented, KB for a conversational agent, which plays the role of an advisor aimed at helping a user to be in charge of a virtual town, inspired to the SimCity series game. The agent makes an extensive use of semantic computing techniques and is able to perceive, comprehend and project consequences of actions in order to handle strategic decision under uncertainty conditions.

Ontology Driven Document Identification in Semantic Web

2010 ◽  
pp. 186-220
Author(s):  
Marek Reformat ◽  
Ronald R. Yager ◽  
Zhan Li
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Bases ◽  
Prototype System ◽  
Specific Domain ◽  
Web Documents ◽  
Web Environment ◽  
New Concepts ◽  
Simple Query ◽  
The Web

The concept of Semantic Web (Berners, 2001) introduces a new form of knowledge representation – an ontology. An ontology is a partially ordered set of words and concepts of a specific domain, and allows for defining different kinds of relationships existing among concepts. Such approach promises formation of an environment where information is easily accessible and understandable for any system, application and/or human. Hierarchy of concepts (Yager, 2000) is a different and very interesting form of knowledge representation. A graph-like structure of the hierarchy provides a user with a suitable tool for identifying variety of different associations among concepts. These associations express user’s perceptions of relations among concepts, and lead to representing definitions of concepts in a human-like way. The Internet becomes an overwhelming repository of documents. This enormous storage of information will be effectively used when users will be equipped with systems capable of finding related documents quickly and correctly. The proposed work addresses that issue. It offers an approach that combines a hierarchy of concepts and ontology for the task of identifying web documents in the environment of the Semantic Web. A user provides a simple query in the form a hierarchy that only partially “describes” documents (s)he wants to retrieve from the web. The hierarchy is treated as a “seed” representing user’s initial knowledge about concepts covered by required documents. Ontologies are treated as supplementary knowledge bases. They are used to instantiate the hierarchy with concrete information, as well as to enhance it with new concepts initially unknown to the user. The proposed approach is used to design a prototype system for document identification in the web environment. The description of the system and the results of preliminary experiments are presented.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

Web knowledge

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Web Applications ◽  
Semantic Network ◽  
Knowledge Bases ◽  
Web Standards ◽  
Description Framework ◽  
The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

A Procedure to Create a Pedagogic Conversational Agent in Secondary Physics and Chemistry Education

2013 ◽  
Vol 9 (4) ◽  
pp. 94-112 ◽  
Author(s):  
Diana Pérez-Marín ◽  
Antonio Boza
Keyword(s):  
Natural Language ◽  
Chemistry Education ◽  
Teaching Method ◽  
Computer Applications ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Innovative Teaching ◽  
Language Support ◽  
Teachers And Students ◽  
Innovative Teaching Method

Pedagogic Conversational Agents are computer applications that can interact with students in natural language. They have been used with satisfactory results on the instruction of several domains. The authors believe that they could also be useful for the instruction of Secondary Physics and Chemistry Education. Therefore, in this paper, the authors present a procedure to create an agent for that domain. First, teachers have to introduce the exercises with their correct answers. Secondly, students will be presented the exercises, and if the students know the answer, and if it is correct, more difficult exercises will be presented. Otherwise, step-by-step natural language support will be provided to guide the student towards the solution. It is the authors’ hypothesis that this innovative teaching method will be satisfactory and useful for teachers and students, and that by following the procedure more computer programmers can be encouraged to develop agents for other domains to be used by teachers and students at class.

scholarly journals Changes to the Psychiatric Chatbot Landscape: A Systematic Review of Conversational Agents in Serious Mental Illness: Changements du paysage psychiatrique des chatbots: une revue systématique des agents conversationnels dans la maladie mentale sérieuse

2020 ◽  
pp. 070674372096642
Author(s):  
Aditya Nrusimha Vaidyam ◽  
Danny Linggonegoro ◽  
John Torous
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Bipolar Disorder ◽  
Mental Illness ◽  
Serious Mental Illness ◽  
Therapeutic Efficacy ◽  
Digital Tools ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Diagnostic Quality

Objective: The need for digital tools in mental health is clear, with insufficient access to mental health services. Conversational agents, also known as chatbots or voice assistants, are digital tools capable of holding natural language conversations. Since our last review in 2018, many new conversational agents and research have emerged, and we aimed to reassess the conversational agent landscape in this updated systematic review. Methods: A systematic literature search was conducted in January 2020 using the PubMed, Embase, PsychINFO, and Cochrane databases. Studies included were those that involved a conversational agent assessing serious mental illness: major depressive disorder, schizophrenia spectrum disorders, bipolar disorder, or anxiety disorder. Results: Of the 247 references identified from selected databases, 7 studies met inclusion criteria. Overall, there were generally positive experiences with conversational agents in regard to diagnostic quality, therapeutic efficacy, or acceptability. There continues to be, however, a lack of standard measures that allow ease of comparison of studies in this space. There were several populations that lacked representation such as the pediatric population and those with schizophrenia or bipolar disorder. While comparing 2018 to 2020 research offers useful insight into changes and growth, the high degree of heterogeneity between all studies in this space makes direct comparison challenging. Conclusions: This review revealed few but generally positive outcomes regarding conversational agents’ diagnostic quality, therapeutic efficacy, and acceptability, which may augment mental health care. Despite this increase in research activity, there continues to be a lack of standard measures for evaluating conversational agents as well as several neglected populations. We recommend that the standardization of conversational agent studies should include patient adherence and engagement, therapeutic efficacy, and clinician perspectives.

Legal Knowledge Systems

2011 ◽  
pp. 1161-1166 ◽  
Author(s):  
T. F. Gordon
Keyword(s):  
Life Cycle ◽  
Knowledge Bases ◽  
Knowledge Systems ◽  
Information And Communications Technology ◽  
Legal Knowledge ◽  
Levels Of Abstraction ◽  
Research Issues ◽  
Implementation Phase ◽  
Knowledge Based ◽  
Representation Techniques

There are many conceptions of e-governance (Malkia, Anttiroiko, & Savolainen, 2004; Reinermann & Lucke, 2002). Our view is that e-governance is about the use of information and communications technology to improve the quality and efficiency of all phases of the life cycle of legislation. In this conception, computer models of legislation play a central role. We use the term “model” in a broad way, to cover every kind of data model of legislation or metadata about legislation, at various levels of abstraction or detail, including full text, hypertext, diagrams and other visualization methods, and legal knowledge-bases using Artificial Intelligence knowledge representation techniques. The appropriate kind of model depends on the particular task to be supported. In this article, the focus will be on the use of legal knowledge systems (LKS) to support the implementation phase of the life cycle of legislation. Legal Knowledge Systems are also known as legal knowledge-based systems (LKBS). LKS can greatly improve the correctness, consistency, transparency and, last but not least, the efficiency of the administration of complex legislation. The rest of this article is organized as follows. The next section explains the relevance of legal knowledge systems for governance. This is followed by a section motivating the use of LKS to support tasks in the implementation phase of the life cycle of legislation and providing a brief introduction to LKS technology. Next, various application scenarios for implementing public policy and legislation using LKS are discussed. Although research on technology for legal knowledge systems continues, it is a mature technology with many impressive applications in regular use by public administration. The article concludes by reiterating its main points and identifying open research issues.

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