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The concept of a knowledge interface for library users is developed as an extension of intelligent knowledge‐base system (IKBS) concepts. Contemporary directions in intelligent decision support, particularly in the role of search intermediaries, are then examined to identify the significance of intelligent intermediaries as a solution to unstructured decision support requirements of library users. A DISCOURSE SCRIPT is given to illustrate one form of intelligent intermediary.

Mobile handheld devices, such as cellular phones and personal digital assistants, are inherently small and lack an intuitive and natural user interface. Speech recognition and synthesis technology can be used in mobile handheld devices to improve the user experience. The purpose of this paper is to describe a prototype system that supports multiple speech‐enabled applications in a mobile handheld device.

The main component of the system, the Program Manager, coordinates and controls the speech‐enabled applications. Human speech requests to, and responses from, these applications are processed in the mobile handheld device, to achieve the goal of human‐like interactions between the human and the device. In addition to speech, the system also supports graphics and text, i.e., multimodal input and output, for greater usability, flexibility, adaptivity, accuracy, and robustness. The paper presents a qualitative and quantitative evaluation of the prototype system. The Program Manager is currently designed to handle the specific speech‐enabled applications that we developed.

The paper determines that many human interactions involve not single applications but multiple applications working together in possibly unanticipated ways.

Future work includes generalization of the Program Manager so that it supports arbitrary applications and the addition of new applications dynamically. Future work also includes deployment of the Program Manager and the applications on cellular phones running the Android Platform or the Openmoko Framework.

This paper presents a first step towards a future human interface for mobile handheld devices and for speech‐enabled applications operating on those devices.

Research in the area of human‐computer interfaces offers guidance for enhancing online catalogs to satisfy patrons' information needs in a “user‐friendly” environment. This article briefly describes human‐computer interfaces and the fundamentals of good human‐computer communication. These concepts are then used as the criteria for evaluating the benefits and shortcomings of San Diego State University Library's INNOPAC, the PAC. In the final analysis, the PAC is not perfect—no system is. It has, however, made strides to overcome some of the failings of second‐generation online public access catalogs.

The purpose of this paper is to present an approach where a novel user modeling wizard for people with motor impairments is used to gain a deeper understanding of very specific (touch-based and touchless) interaction patterns. The findings are used to set up and fill a user model which allows to automatically derive an application- and user-specific configuration for natural user interfaces.

Based on expert knowledge in the domain of software/user interfaces for people with special needs, a test-case –based user modeling tool was developed. Task-based user tests were conducted with seven users for the touch-based interaction scenario and with five users for the touchless interaction scenario. The participants are all people with different motor and/or cognitive impairments.

The paper describes the results of different test cases that were designed to model users’ touch-based and touchless interaction capabilities. To evaluate the tool’s findings, experts additionally judged the participants’ performance (their opinions were compared to the tool’s findings). The results suggest that the user modeling tool could quite well capture users’ capabilities.

The paper presents a tool that can be used to model users’ interaction capabilities. The approach aims at taking over some of the (very time-consuming) configuration tasks consultants have to do to configure software according to the needs of people with disabilities. This can lead to a wider accessibility of software, especially in the area of gesture-based user interaction.

Part of the approach has been published in the proceedings of the Interactional Conference on Advances in Mobile Computing and Multimedia 2014. Significant additions have been made since (e.g. all of the touchless interaction part of the approach and the related user study).

Despite the technological paradigm shift presented to the architecture, engineering, construction and operations sector (AECO), the full-fledged acceptance of the building information modelling (BIM) methodology has been slower than initially anticipated. Indeed, this study aims to acknowledge the need for increasing supportive technologies enabling the use of BIM, attending to available human resources, their requirements and their tasks.

A complete case study is described, including the development process centred on design science research methodology followed by the usability assessment procedure validated by construction projects facility management operational staff.

Results show that participants could interact with BIM using openBIM processes and file formats naturally, as most participants reached an efficiency level close to that expected for users already familiar with the interface (i.e. high-efficiency values). These results are consistent with the reported perceived satisfaction and analysis of participants’ discourses through 62 semi-structured interviews.

The contributions of the present study are twofold: a proposal for a virtual reality openBIM framework is presented, particularly for the semantic enrichment of BIM models, and a methodology for evaluating the usability of this type of system in the AECO sector.

Healthcare facility managers work in complex and dynamic environments where critical decisions are constantly made. Providing them with enhanced decision support systems would result in a positive impact on the productivity and success of the projects they undertake, as well as the sustainability of critical healthcare infrastructure. The purpose of this paper is to propose a conceptual ambient intelligent environment for enhancing the decision-making process of the facility managers. This low-cost data-rich environment would use building information modeling (BIM) and mobile augmented reality (MAR) as technological bases for the natural human-computer interfaces and aerial drones as technological tools.

This paper presents a scenario for the integration of augmented reality (AR) and building information modeling (BIM) to build an ambient intelligent (AmI) environment for facility managers where mobile, natural, user interfaces would provide the users with required data to facilitate their critical decision-making process. The technological requirements for having such an intelligent environment are also discussed.

The proposed BIM-MAR-based approach is capable of enhancing maintenance related practices for facility managers who are mobile to integrate with their facilities' intelligent environment. This approach is also capable of providing a collaborative environment in which different stakeholders, across geographically distributed areas, could work together to solve facility management tasks.

In this paper ambient intelligence will be considered for the first time in the area of healthcare facility management practices to provide facility managers with an intelligent BIM-based environment to access facility information and consequently enhance their decision-making process.

At present the end user must often rely on an intermediary to make use of available online systems. Artificial intelligence (AI) holds the possibility of making systems accessible to a wider range of people. AI research is a branch of computer science which attempts to develop programs to enable computer systems to communicate more fluently, to explain why they have taken certain actions, to handle unforeseen situations, and to exhibit other similar signs of intelligent behavior. This paper reviews a number of studies which demonstrate how AI techniques can be applied in the design of end user‐oriented interfaces to existing online systems as well as in the development of future generations of online systems intended for the end user. Criteria proposed for user‐oriented systems are summarized and suggestions are given of the ways in which AI techniques may enable one to realize them. These criteria are found to focus on the language for man—machine communication and on approaches to delegating more aspects of the retrieval process to the system. Available technology such as intelligent terminals can be used to augment the interface of existing online systems by varying command languages and display formats. The features of two prototypes demonstrating this approach are described. AI work on man—machine communication and knowledge‐based systems which is likely to influence the design of future online systems is highlighted. Finally, Licklider's proposal for “procognitive systems” is recalled, and it is suggested that AI techniques provide indications of how such systems could eventually be built.

This study aims to investigate the applications of knowledge graphs in developing artificial intelligence (AI) assistants and chatbots by reviewing scholarly publications from different lenses and dimensions. The authors also analyze the various AI approaches used for knowledge graph-driven chatbots and discuss how implementing these techniques makes a difference in technology.

Over recent years, chatbots have emerged as a transformational force in interacting with the digital world in various domains, including customer service and personal assistants. Recently, chatbots have become a revolutionary tool for interacting with the digital world in various contexts, such as personal assistants and customer support. Incorporating knowledge graphs considerably improved the capabilities of chatbots by allowing them access to massive knowledge bases and enhancing their ability to understand queries. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements and improve response quality. This study highlights the role of knowledge graphs in chatbots following a systematic review approach. They have been integrated into major health-care, education and business domains. Beyond improving information retrieval, knowledge graphs enhance the user experience and increase the range of fields in which chatbots can be used. Improving and enriching chatbot answers was also identified as one of the main advantages of knowledge graphs. This enriched response can increase user confidence and improve the accuracy of chatbot interactions, making them more trustworthy information sources.

Knowledge graph-based chatbots leverage extensive data retrieval to provide accurate and enriched responses, increasing user confidence and experience without requiring extensive training. The three major domains where knowledge graph-based chatbots have been used are health care, education and business.

Knowledge graph-based chatbots can better comprehend user queries and respond with relevant information efficiently without extensive training. Furthermore, knowledge graphs enable chatbots to understand semantic links between elements, allowing them to answer complicated and multi-faceted questions. This semantic comprehension improves response quality, making chatbots more successful in providing accurate and valuable information in various domains. Furthermore, knowledge graphs enable chatbots to provide consumers with individualized experiences by storing and recalling individual preferences, history or previous encounters. This study analyzes the role of knowledge graphs in chatbots following a systematic review approach. This study reviewed state-of-the-art articles to understand where and how chatbots have used knowledge graphs. The authors found health care, business and education as three main areas in which knowledge-graph-based chatbots have been mostly used. Chatbots have been developed in text, voice and visuals using various machine learning models, particularly natural language pocessing, to develop recommender systems to recommend suitable items, content or services based on user preferences and item associations.

This paper provides a comprehensive review of the current state of the field in using knowledge graphs in chatbots, focusing on machine learning models, domains and communication channels. The study highlights the prevalence of text and voice channels over visual ones and identifies research gaps and future directions. The paper’s insights can inform the design and development of chatbots using knowledge graphs and benefit both researchers and practitioners in AI, natural language processing and human–computer interaction. The paper is of interest to professionals in domains such as health care, education and business.

Shows how to introduce computer support for the implementation of different decision‐making settings. Provides a comparison between decision support and expert systems. Analyses the similarities and dissimilarities of decision support systems (DSS) and expert systems (ES) and presents a conceptual framework for the integration of these two technologies. Numerous examples illustrate operationally integrated DSS and ES.

The Internet and related information services attract increasing interest from information retrieval researchers. A survey of recent publications show that frequent topics are the effectiveness of search engines, information validation and quality, user studies, design of user interfaces, data structures and metadata, classification and vocabulary based aids, and indexing and search agents. Current research in these areas is briefly discussed. The changing balance between CD‐ROM sources and traditional online searching is quite important and is noted.