Relevant bibliographies by topics / Domain expert

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Domain expert'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 November 2024

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Journal articles on the topic "Domain expert"

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MacMillan, Jean, Eileen B. Entin, and Daniel Serfaty. "Evaluating Expertise in a Complex Domain–Measures Based on Theory." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 17 (October 1993): 1152–55. http://dx.doi.org/10.1177/154193129303701706.

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Human factors practitioners are often concerned with defining and evaluating expertise in complex domains where there may be no agreed-upon expertise levels, no single right answers to problems, and where the observation and measurement of real-world expert performance is difficult. This paper reports the results of an experiment in which expertise was assessed in an extremely complex and demanding domain–military command decision making in tactical warfare. The hypotheses of the experiment were: 1) command decisionmaking expertise can be recognized in practice by domain experts; 2) differences in the command decisionmaking expertise of individuals can be identified even under conditions that do not fully replicate the real world; and 3) observers who are not domain experts can recognize the expert behaviors predicted by a mental-model theory about the nature of expertise. In the experiment, the expertise of military officers in developing tactical plans was assessed independently by three “super-expert” judges, and these expertise-level ratings were correlated with independent theory-based measures used by observers who were not domain experts. The results suggest that experts in a domain have a shared underlying concept of expertise in that domain even if they cannot articulate that concept, that this expertise can be elicited and measured in situations that do not completely mimic the real world, and that expertise measures based on a mental-model theory can be used effectively by observers who are not experts in the domain.
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Lepouras, George, Costas Vassilakis, Constantin Halatsis, and Panagiotis Georgiadis. "Domain expert user development." Communications of the ACM 50, no. 9 (September 2007): 79–83. http://dx.doi.org/10.1145/1284621.1284623.

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Poo, Chiang-Choon Danny, and Hongjun Lu. "Multi-domain expert systems." Expert Systems 8, no. 2 (May 1991): 67–73. http://dx.doi.org/10.1111/j.1468-0394.1991.tb00254.x.

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Chan, Christine, and Izak Benbasat. "Case research on knowledge acquisition: observations and lessons." Knowledge Engineering Review 6, no. 2 (June 1991): 97–120. http://dx.doi.org/10.1017/s0269888900005634.

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AbstractExpert systems are being built despite the widely acknowledged problem of acquiring knowledge from experts. This study attempts to understand how knowledge acquisition is conducted in practice by investigating three expert system development projects. A CASE research methodology is adopted, and data is collected through unobtrusive observation, from taped protocols of knowledge acquisition sessions, retrospective interviews with the participants involved, and deliverables produced. The variables examined include the problem domain, the domain expert, the knowledge engineer, the knowledge acquisition process, the expert system construction process, potential users, organizational setting, and the expert system itself. The knowledge acquisition processes for three expert systems in the domains of law of negligence, telephone line fault diagnosis, and wastewater treatment have been examined. By juxtaposing the observations drawn with findings from the relevant literature, the study makes prescriptive suggestions on considerations and techniques for future acquisition efforts, and provides data for hypothesis generation in further research.
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Husain, Omayma, Naomie Salim, Rose Alinda Alias, Samah Abdelsalam, and Alzubair Hassan. "Expert Finding Systems: A Systematic Review." Applied Sciences 9, no. 20 (October 11, 2019): 4250. http://dx.doi.org/10.3390/app9204250.

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The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.
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Prytz, Erik G., Caroline Norén, and Carl-Oscar Jonson. "Fixation Differences in Visual Search of Accident Scenes by Novices and Expert Emergency Responders." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 8 (August 13, 2018): 1219–27. http://dx.doi.org/10.1177/0018720818788142.

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Objective: We sought to investigate whether expert–novice differences in visual search behavior found in other domains also apply to accident scenes and the emergency response domain. Background: Emergency service professionals typically arrive at accidents only after being dispatched when a civilian witness has called an emergency dispatch number. Differences in visual search behavior between the civilian witness (usually a novice in terms of emergency response) and the professional first responders (experts at emergency response) could thus result in the experts being given insufficient or erroneous information, which would lead them to arrive unprepared for the actual situation. Method: A between-subjects, controlled eye-tracking experiment with 20 novices and 17 experts (rescue and ambulance service personnel) was conducted to explore expert–novice differences in visual search of accident and control images. Results: The results showed that the experts spent more time looking at task-relevant areas of the accident images than novices did, as predicted by the information reduction hypothesis. The longer time was due to longer fixation durations rather than a larger fixation count. Conclusion: Expert–novice differences in visual search are present in the emergency domain. Given that this domain is essential to saving lives and also relies heavily on novices as the first link in the chain of response, such differences deserve further exploration. Application: Visual search behavior from experts can be used for training purposes. Eye-tracking studies of novices can be used to inform the design of emergency dispatch interviews.
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Anandhan, Anitha, Maizatul Akmar Ismail, and Liyana Shuib. "EXPERT RECOMMENDATION THROUGH TAG RELATIONSHIP IN COMMUNITY QUESTION ANSWERING." Malaysian Journal of Computer Science 35, no. 3 (July 27, 2022): 201–21. http://dx.doi.org/10.22452/mjcs.vol35no3.2.

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Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.
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Walton, Joan, M. A. Musen, D. M. Combs, C. D. Lane, E. H. Shortliffe, and L. M. Fagan. "Graphical Access to Medical Expert Systems: III. Design of a Knowledge Acquisition Environment*)." Methods of Information in Medicine 26, no. 03 (July 1987): 78–88. http://dx.doi.org/10.1055/s-0038-1635497.

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SummaryKnowledge acquisition for expert systems typically is a tedious, iterative process involving long hours of consultation between the domain experts and the computer scientists who serve as knowledge engineers. For well-understood domains, however, it may be possible to facilitate the knowledge acquisition process by allowing domain experts to develop and edit a knowledge base directly. Administration of protocol-directed cancer chemotherapy is such a well-understood application area, and a knowledge acquisition system, called OPAL, has been developed for eliciting chemotherapy-protocol knowledge directly from expert oncologists. OPAL’s knowledge acquisition approach is based on the interactive graphics environment available on current generation workstations. The use of graphics improves the interface by reducing typing, avoiding natural language interpretations, and allowing flexibility in entry sequence. The knowledge in OPAL is displayed using an arrangement of hierarchically related, graphical forms. The position of a particular form in the hierarchy defines the context of the knowledge contained in the form. Intelligent editing programs such as OPAL can streamline the knowledge engineering process for highly structured domains requiring repetitive knowledge entry.
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Albagli-Kim, Sivan, and Dizza Beimel. "Knowledge Graph-Based Framework for Decision Making Process with Limited Interaction." Mathematics 10, no. 21 (October 26, 2022): 3981. http://dx.doi.org/10.3390/math10213981.

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In this work, we present an algorithmic framework that supports a decision process in which an end user is assisted by a domain expert to solve a problem. In addition, the communication between the end user and the domain expert is characterized by a limited number of questions and answers. The framework we have developed helps the domain expert to pinpoint a small number of questions to the end user to increase the likelihood of their insights being correct. The proposed framework is based on the domain expert’s knowledge and includes an interaction with both the domain expert and the end user. The domain expert’s knowledge is represented by a knowledge graph, and the end user’s information related to the problem is entered into the graph as evidence. This triggers the inference algorithm in the graph, which suggests to the domain expert the next question for the end user. The paper presents a detailed proposed framework in a medical diagnostic domain; however, it can be adapted to additional domains with a similar setup. The software framework we have developed makes the decision-making process accessible in an interactive and explainable manner, which includes the use of semantic technology and is, therefore, innovative.
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Jung, Ju Yeon, Tom Steinberger, John L. King, and Mark S. Ackerman. "How Domain Experts Work with Data: Situating Data Science in the Practices and Settings of Craftwork." Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (March 30, 2022): 1–29. http://dx.doi.org/10.1145/3512905.

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Domain experts play an essential role in data science by helping data scientists situate their technical work beyond the statistical analysis of large datasets. How domain experts themselves may engage with data science tools as a type of end-user remains largely invisible. Understanding data science as domain expert-driven depends on understanding how domain experts use data. Drawing on an ethnographic study of a craft brewery in Korea, we show how craft brewers worked with data by situating otherwise abstract data within their brewing practices and settings. We contribute theoretical insight into how domain experts use data distinctly from technical data scientists in terms of their view of data (situated vs. abstract), purposes for engaging with data (guiding processes over predicting outcomes), and overall goals of using data (flexible control vs. precision). We propose four ways in which working with data can be supported through the design of data science tools, and discuss how craftwork can be a useful lens for integrating domain expert-driven understandings of data science into CSCW and HCI research.
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Dissertations / Theses on the topic "Domain expert"

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Pierre, Mattias. "Combining assembles of domain expert markings." Thesis, Umeå University, Department of Computing Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34405.

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Breast cancer is diagnosed in more than 6300 Swedish women every year. Mammograms, which are X-ray images of breasts, are taken as part of a nationwide screening process and are analyzed for anomalies by radiologists. This analysis process could be made more efficient by using computer-aided image analysis to assist quality control of the mammograms. However, the development of such image analysis methods requires what is called a “ground truth”. The ground truth is used as a key in algorithm development and represents the true information in the depicted object. Mammograms are 2D projections of deformed 3D objects, and in these cases the ground truth is almost impossible to procure. Instead a surrogate ground truth is constructed. ALGSII, a novel method for ranking shapes within a given set, was recently developed for measuring the level of agreement among ensembles of markings produced by experts of glandular tissue in mammograms. It was hypothesized in this thesis that the ALGSII measure could be used to construct a surrogate truth based on the markings from domain experts.Markings from segmentations of glandular tissue, performed by 5 different field experts on 162 mammograms, comprised the working data for this thesis project. An algorithm was developed that, given a fixed set of markings, takes an initial shape and modifies it iteratively until it becomes the “optimal shape” - the shape with the highest level of agreement in the group of markings according to the ALGSII measure. The algorithm was optimized with egard to rate of accepted shape changes and computational complexity.The developed algorithm was successful in producing an optimal shape according to the definition of maximizing the ALGSII measure in 100% of the cases tested. The algorithm showed stability for the given data set, and its performance was significantly increased by the implemented optimizations.

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Wouters, Laurent. "Multi-domain expert-user modeling infrastructure." Paris 6, 2013. http://www.theses.fr/2013PA066200.

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Ce travail a été réalisé dans un contexte industriel dans l’entreprise European Aeronautics Defense and Space Company (EADS). EADS recherche donc constamment de nouveaux moyens d’analyser la sécurité du système homme-machine dans sa globalité, c'est-à-dire l’avion, les pilotes et les procédures, comme un tout. Ces analyses de sécurité sont réalisées tout au long du cycle de conception, depuis la conception préliminaire à la conception détaillée et jusqu'à la phase de certification. EADS essaye de réaliser ces analyses beaucoup plus tôt dans le cycle de conception, lorsque seulement des modèles sont disponibles. Une question en suspend est alors comment assurer la collaboration entre les experts de plusieurs domaines (cockpit, procédures, psychologie cognitive) pour qu’ils puissent construite un artefact commun (un modèle) sur lequel ils pourraient s’appuyer pour réaliser les analyses de la sécurité du système homme-machine dans sa globalité. Cette thèse identifie et adresse trois questions. Premièrement, les langages de modélisation dédiés aux domaines doivent être sémantiquement alignés pour que l’artefact commun puisse être exprimé de manière cohérente. Deuxièmement, des notations visuelles spécifiques aux domaines doivent être produites pour le même artefact commun. Troisièmement, l’activité de modélisation des experts doit être supportée au mieux et pour cela les notations visuelles des langages de modélisation doivent être le plus proche possible des pratiques courantes dans les domaines respectifs. Cette thèse propose l’Infrastructure xOWL comme une solution intégrée à ces trois questions
This work has been realized in an industrial context at the European Aeronautics Defense and Space Company (EADS). EADS is researching new ways to assess the safety of the overall human-machine system, i. E. , the aircraft, pilots and operating procedures as a whole. These safety assessments are conducted throughout the design cycle of the product, from the preliminary design to the detailed design and up to the certification phase. EADS is trying to perform these thorough safety assessments much earlier in the development cycle, when only models are available, thus, phasing-in a model-driven approach of the problem. An issue is then how to enable the collaboration of experts from multiple domains (cockpit, procedures, and cognitive psychology) for them to build a common model artifact that can be leveraged in the safety assessment of the overallhuman-machine system. This work considers that experts in each domain must be provided a domain-specific modeling environment, giving them access to a common model artifact, but through a domain-specific notation. This thesis identifies and considers three issues in this regard. First, the domain-specific modeling languages need to be semantically aligned so that the common model artifact can be consistently expressed. Second, multiple domain-specific visual notations need to be produced for the same underlying common model artifact. Third, domain experts modeling activities need to be supported at best and thus the provided domain-specific notations need to be as close as possible to the existing practices in the domains. This thesis then proposes the xOWL Infrastructure as an integrated solution to the three issues
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Chronister, Julie Anne. "A domain-independent framework for structuring knowledge in the OFMspert architecture." Thesis, Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/25752.

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Nasir, M. L. "Combining domain expert knowledge with neural networks for predicting corporate bankruptcies." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/10715.

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Goswami, Madan Gopal. "An approach towards the development of an expert system for paediatric problem domain." Thesis, University of North Bengal, 1999. http://hdl.handle.net/123456789/1039.

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Nasuti, Frank W. "Knowledge Acquisition using Multiple Domain Experts in the Design and Development of an Expert System for Disaster Recovery Planning." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/746.

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The increasing dependence of organizations on data processing to perform the basic functions of corporate America, together with recent disasters such as earthquakes, tornadoes and hurricanes have awakened management to the realization that they require Disaster Recovery Plans (DRP) and Business Resumption Services (BRS). To address these needs, organizations frequently consult with outsiders to help them develop disaster recovery and business resumption plans. Although consultants and vendors specializing in disaster recovery planning are available, their number is limited and the quality of their services may be questionable. In addition, the information gathering process by consultants is a time consuming process and in most cases requires the use of multiple vendor experts, as well as various resources within the customer's organization. This research proposed, as a solution to address these deficiencies, the design and development of an expert system to assist in the determination of the needs of an organization for disaster recovery and business resumption services, as well as the evaluation of existing plans. This research resulted in the design of an expert system for disaster recovery planning. It included the knowledge acquisition processes necessary to elicit information from multiple domain experts. The specific goals of this research were: (1) knowledge acquisition specific to the problems of using multiple domain experts; (2) design and development of a prototype expert system for disaster recovery planning; and (3) validation of the prototype expert system.
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Sánchez, David. "Domain ontology learning from the web an unsupervised, automatic and domain independent approach." Saarbrücken VDM Verlag Dr. Müller, 2007. http://d-nb.info/991459016/04.

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Lyon, Bruce. "Teraphim : a domain-independent framework for constructing blackboard-based expert systems in Prolog /." Online version of thesis, 1987. http://hdl.handle.net/1850/8858.

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Okoli, Justin. "Expert knowledge elicitation in the firefighting domain and the implications for training novices." Thesis, Middlesex University, 2016. http://eprints.mdx.ac.uk/22940/.

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Background/Purpose: Experienced fireground commanders are often required to make important decisions in time-pressured and dynamic environments that are characterized by a wide range of task constraints. The nature of these environments is such that firefighters are sometimes faced with novel situations that seek to challenge their expertise and therefore necessitate making knowledge-based as opposed to rule-based decisions. The purpose of this study is to elicit the tacitly held knowledge which largely underpinned expert competence when managing non-routine fire incidents. Design/Methodology/Approach: The study utilized a formal knowledge elicitation tool known as the critical decision method (CDM). The CDM method was preferred to other cognitive task analysis (CTA) methods as it is specifically designed to probe the cognitive strategies of domain experts with reference to a single incident that was both challenging and memorable. Thirty experienced firefighters and one staff development officer were interviewed in-depth across different fire stations in the UK and Nigeria (UK=15, Nigeria=16). The interview transcripts were analyzed using the emergent themes analysis (ETA) approach. Findings: Findings from the study revealed 42 salient cues that were sought by experts at each decision point. A critical cue inventory (CCI) was developed and cues were categorized into five distinct types based on the type of information each cue generated to an incident commander. The study also developed a decision making model — information filtering and intuitive decision making model (IFID), which describes how the experienced firefighters were able to make difficult fireground decisions amidst multiple informational sources without having to deliberate on their courses of action. The study also compiled and indexed the elicited tacit knowledge into a competence assessment framework (CAF) with which the competence of future incident commanders could potentially be assessed. Practical Implications: Through the knowledge elicitation process, training needs were identified, and the practical implications for transferring the elicited experts’ knowledge to novice firefighters were also discussed. The four component instructional design model aided the conceptualization of the CDM outputs for training purposes. Originality/Value: Although it is widely believed that experts perform exceptionally well in their domains of practice, the difficulty still lies in finding how best to unmask expert (tacit) knowledge, particularly when it is intended for training purposes. Since tacit knowledge operates in the unconscious realm, articulating and describing it has been shown to be challenging even for experts themselves. This study is therefore timely since its outputs can facilitate the development of training curricula for novices, who then will not have to wait for real fires to occur before learning new skills. This statement holds true particularly in this era where the rate of real fires and therefore the opportunity to gain experience has been on a decline. The current study also presents and discusses insights based on the cultural differences that were observed between the UK and the Nigerian fire service.
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Alshayji, Sameera. "The development of a fuzzy expert system to help top decision makers in political and investment domains." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6977.

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The world’s increasing interconnectedness and the recent increase in the number of notable regional and international events pose greater and greater challenges for political decision-making, especially the decision to strengthen bilateral economic relationships between friendly nations. Typically, such critical decisions are influenced by certain factors and variables that are based on heterogeneous and vague information that exists in different domains. A serious problem that the decision-maker faces is the difficulty in building efficient political decision support systems (DSS) with heterogeneous factors. One must take many factors into account, for example, language (natural or human language), the availability, or lack thereof, of precise data (vague information), and possible consequences (rule conclusions). The basic concept is a linguistic variable whose values are words rather than numbers and are therefore closer to human intuition. A common language is thus needed to describe such information which requires human knowledge for interpretation. To achieve robustness and efficiency of interpretation, we need to apply a method that can be used to generate high-level knowledge and information integration. Fuzzy logic is based on natural language and is tolerant of imprecise data. Fuzzy logic’s greatest strength lies in its ability to handle imprecise data, and it is perfectly suited for this situation. In this thesis, we propose to use ontology to integrate the scattered information resources from the political and investment domains. The process started with understanding each concept and extracting key ideas and relationships between sets of information by constructing object paradigm ontology. Re-engineering according to the object-paradigm (OP) provided quality for the developed ontology where conceptualization can provide more expressive, reusable object and temporal ontology. Then fuzzy logic has been integrated with ontology. And a fuzzy ontology membership value that reflects the strength of an inter-concept relationship to represent pairs of concepts across ontology has been consistently used. Each concept is assigned a fixed numerical value representing the concept consistency. Concept consistency is computed as a function of strength of all the relationships associated with the concept. Fuzzy expert systems enable one to weigh the consequences (rule conclusions) of certain choices based on vague information. Rule conclusions follow from rules composed of two parts, the if antecedent (input) and the then consequent (output). With fuzzy expert systems, one uses fuzzy logic toolbox graphical user interface (GUI) tools to build up a fuzzy inference system (FIS) to aid in decision-making. This research includes four main phases to develop a prototype architecture for an intelligent DSS that can help top political decision makers.
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Books on the topic "Domain expert"

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1957-, Liebowitz Jay, and De Salvo Daniel A, eds. Structuring expert systems: Domain, design, and development. Englewood Cliffs, N.J: Yourdon Press, 1989.

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Sklar, Mitchell Jay. MU: A domain-independent case-based expert system. [New Haven: s.n.], 1988.

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Nunes, José H. T. ESP: a domain-independent expert system framework using PSN. Toronto: University of Toronto, Dept. of Computer Science, 1985.

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Nasir, M. L. Combining domain expert knowledge with neural networks for predicting corporate bankruptcies. Leicester: De Montfort University, 2000.

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Montazeri, M. A. Retrieval strategies for a case-based reasoning expert system in the legal domain. Manchester: UMIST, 1996.

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Gardin, Jean Claude. Artificial intelligence and expert systems: Case studies in the knowledge domain of archaeology. Chichester [England]: E. Horwood, 1988.

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Visser, Pepijn R. S. Knowledge specification for multiple legal tasks: A case studyof the interaction problem in the legal domain. The Hague: Kluwer, 1995.

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Visser, Pepijn R. S. Knowledge specification for multiple legal tasks: A case study of the interaction problem in the legal domain. Leiden: Rijksuniversiteit te Leiden, 1995.

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Rourke, Arianne, and Vaughan Rees. Moving from novice to expert on the road to expertise: Developing expertise in the visual domain. Champaign, Illinois: Common Ground Publishing, LLC., 2015.

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Lee, Wai Shing. Combining the technologies of expert system and conventional data processing system into the domain of internal control within a banking environment. [s.l: The Author], 1991.

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Book chapters on the topic "Domain expert"

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Eisner, Howard. "Expert/Domain Knowledge." In What Makes the Systems Engineer Successful? Various Surveys Suggest An Answer, 67–72. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781003089650-8.

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Halene, Tobias B., and Vilma Gabbay. "Research Domain Criteria (RDoC)." In Mount Sinai Expert Guides, 9–11. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118654231.ch2.

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Chala, Sisay Adugna, and Alexander Graß. "Human-AI Interaction for Semantic Knowledge Enrichment of AI Model Output." In Artificial Intelligence in Manufacturing, 43–54. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_3.

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AbstractModern manufacturing requires developing a framework of AI solutions that capture and process data from various sources including from human-AI collaboration. This chapter tries to describe the concept of domain knowledge fusion in human-AI collaboration for manufacturing. Human interaction with AI is enabled in such a way that the domain expert not only inspects the output of the AI model but also injects engineered knowledge in order to retrain AI models for iterative improvement. Domain knowledge fusion is a technique that involves combining knowledge from multiple domains or sources to produce a more complete solution by augmenting learned knowledge, i.e., the knowledge generated by the AI model with engineered knowledge, i.e., the knowledge provided by the domain expert. The concept developed in this chapter demonstrates how the domain expert interacts with AI systems to observe and decide the veracity of the learned knowledge with respect to the given context. It enables humans to collaborate with AI systems through intuitive interfaces that help domain experts in interpreting insights, validating the findings, and applying domain knowledge to gain a deeper understanding of the data.
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Tanner, Michael C., Anne M. Keuneke, and B. Chandrasekaran. "Explanation Using Task Structure and Domain Functional Models." In Second Generation Expert Systems, 586–613. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77927-5_25.

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Reyes, Denise L., and Eduardo Salas. "What makes a team of experts an expert team?" In Psychology of high performance: Developing human potential into domain-specific talent., 141–59. Washington: American Psychological Association, 2019. http://dx.doi.org/10.1037/0000120-007.

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Damiani, Maria, and Sandro Bottarelli. "A Terminological Approach to Business Domain Modelling." In Database and Expert Systems Applications, 340–45. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-7553-8_55.

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Hunt, John E., and Christopher J. Price. "Integrating Functional Models and Structural Domain Models for Diagnostic Applications." In Second Generation Expert Systems, 131–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77927-5_8.

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Fang, Hsai-Yang, George K. Mikroudis, and Sibel Pamukcu. "Multi-Domain Expert Systems for Hazardous Waste Site Investigations." In Expert Systems in Environmental Planning, 217–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77870-4_11.

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Di Giorgi, Rosa M., Elio Fameli, and Roberta Nannucci. "Expert System and Database Interaction in the Legal Domain." In Database and Expert Systems Applications, 281–84. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-7553-8_45.

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Finlayson, Angela, and Paul Compton. "Using a Domain Expert in Semi-supervised Learning." In Lecture Notes in Computer Science, 99–111. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13332-4_9.

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Conference papers on the topic "Domain expert"

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Witt, Hendrik, and Ernesto Morales Kluge. "Domain expert vs. layman." In the 4th international conference on mobile technology, applications, and systems and the 1st international symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1378063.1378092.

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Harel, Amir, Asaf Shabtai, Lior Rokach, and Yuval Elovici. "Eliciting domain expert misuseability conceptions." In the sixth international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1999676.1999721.

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Sieras, Joseph C. "Intelligent Knowledge-Based System for Environmental Impact Assessment of Mountain Resort Development Project." In International Conference on Digital Transformation and Applications (ICDXA 2020). Tunku Abdul Rahman University College, 2020. http://dx.doi.org/10.56453/icdxa.2020.1027.

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The study aimed to design and develop an intelligent knowledge-based system prototype which mimics the human domain experts’ knowledge that can be used by environmental impact assessment review committee (EIARC) members in the assessment and preparation of environmental impact assessment (EIA) reports for mountain resort projects. Specifically, the system aimed to strengthen and improve the implementation of EIA among mountain resort development projects through an objective, transparent and fast-paced evaluation of EIA documents. The knowledge-based system was developed using e2gRuleWriter to generate its knowledge base and e2gRuleEngine as expert system shell. The human domain experts were purposively selected as the source of knowledge of the system. Through knowledge engineering, the knowledge of the domain experts was captured and became the basis for the development of the expert system rules which were presented in production rules format to assess the project description and activities of mountain resort developments. The developed expert system rules were validated using resolution principle to ensure its correctness. The human domain experts further validated the developed expert system rules by confirming that the knowledge-based system produces correct recommendation. Keywords: Artificial Intelligence, Knowledge Engineering, Environmental Impact Assessment, Expert System
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Ammar, Maan, Khuzama Ammar, Kinan Mansour, and Waad Ammar. "Expert Systems Generating Machine for Image Processing Applications." In 12th International Conference on Computer Science and Information Technology (CCSIT 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121303.

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We introduce in this paper what can be considered a new trend in expert systems field. It is generating different expert systems using the same software platform developed for this purpose, and called “Expert Systems Generating Machine for Image Processing Applications ESGMIPA”. The machine is used to generate different expert systems in completely different application fields which indicates the feasibility of the proposal. Using what we called Domain Expert Guided Heuristic Search (DEGHS) and the machine, we generated an expert system that succeeded in cases where no algorithmic approach can be applied. Generating different expert systems using the same machine depends on the well-known fact that the function of an expert system is determined mainly by its knowledge base. The machine developed expedite very much the development of the expert system to reach best performance. The role of domain expert and the positive effect of the interaction between different domain experts in different fields is highlighted.
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Ghouila, Amel, Olivier Gascuel, Sadok Ben Yahia, and Laurent Brehelin. "Enhancing Protein Domain Detection Using Domain Co-occurrence and Domain Exclusion." In 2012 23rd International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2012. http://dx.doi.org/10.1109/dexa.2012.45.

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Rekik, Karim, Abdelkabir Bouyghf, Olfa Zened, and Tanya Kontsedal. "Augmented Learning Parameter Advisor for Wellbore Domain Interpretations." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216491-ms.

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Abstract The Parameter Advisor introduces an AI-powered solution for automating the selection of optimal parameter values in wellbore data interpretation. The aim is to reduce effort and time required for accurate interpretations. The software leverages machine learning algorithms, a comprehensive knowledge base, and collaboration among experts to enhance the interpretation process. The overall approach includes data gathering, quality control, and validation. Relevant data is collected and stored in a cloud storage system. The software applies statistical techniques and unsupervised learning algorithms to ensure accuracy and identify patterns in the data. Once the database is established, the software provides recommendations for future analyses based on past interpretations and expert knowledge. The results of tests conducted in the GRONINGEN and CASABE fields showed 92% accuracy compared to manual interpretation. The execution time for a Shale Volume interpretation was reduced by 64%. Collaborative studies with AkerBP in the Valhall field demonstrated an interpretation time reduction of approximately 70%. This study presents a novel approach in the petroleum industry by automating parameter initiation using machine learning and cloud computing. It improves the speed, accuracy, and efficiency of wellbore data interpretation. The software's ability to recommend optimal parameter values based on previous interpretations and expert knowledge contributes to better decision-making. The findings emphasize the effectiveness of machine learning in automating interpretation tasks and enabling non-experts to interpret data accurately. In summary, the proposed software streamlines the wellbore data interpretation process, reduces errors, and saves time. It enhances collaboration among experts, captures expert knowledge, and improves decision-making. The solution adds valuable insights to the petroleum industry by showcasing the power of machine learning in interpretation tasks and demonstrating its potential for transforming the field.
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Karimi, Sarvnaz, Falk Scholer, Adam Clark, and Sadegh Kharazmi. "Domain expert topic familiarity and search behavior." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010086.

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Annaiahshetty, K., and N. Prasad. "Expert System for Multiple Domain Experts Knowledge Acquisition in Software Design and Development." In 2013 UKSim 15th International Conference on Computer Modelling and Simulation (UKSim 2013). IEEE, 2013. http://dx.doi.org/10.1109/uksim.2013.124.

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Goško, Didzis, Arturs Znotins, Inguna Skadina, Normunds Gruzitis, and Gunta Nešpore-Bērzkalne. "Domain Expert Platform for Goal-Oriented Dialog Collection." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-demos.35.

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Mohan, Satish. "Expert Systems Technology in the Domain of Construction." In 4th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 1987. http://dx.doi.org/10.22260/isarc1987/0005.

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Reports on the topic "Domain expert"

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Speed, Ann, David John Stracuzzi, Jina Lee, and Lauren Hund. Applying Image Clutter Metrics to Domain-Specific Expert Visual Search. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1603851.

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Baader, Franz, Bernhard Ganter, Ulrike Sattler, and Barış Sertkaya. Completing Description Logic Knowledge Bases using Formal Concept Analysis. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.155.

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We propose an approach for extending both the terminological and the assertional part of a Description Logic knowledge base by using information provided by the assertional part and by a domain expert. The use of techniques from Formal Concept Analysis ensures that, on the one hand, the interaction with the expert is kept to a minimum, and, on the other hand, we can show that the extended knowledge base is complete in a certain sense.
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Borchmann, Daniel, Felix Distel, and Francesco Kriegel. Axiomatization of General Concept Inclusions from Finite Interpretations. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.219.

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Description logic knowledge bases can be used to represent knowledge about a particular domain in a formal and unambiguous manner. Their practical relevance has been shown in many research areas, especially in biology and the semantic web. However, the tasks of constructing knowledge bases itself, often performed by human experts, is difficult, time-consuming and expensive. In particular the synthesis of terminological knowledge is a challenge every expert has to face. Because human experts cannot be omitted completely from the construction of knowledge bases, it would therefore be desirable to at least get some support from machines during this process. To this end, we shall investigate in this work an approach which shall allow us to extract terminological knowledge in the form of general concept inclusions from factual data, where the data is given in the form of vertex and edge labeled graphs. As such graphs appear naturally within the scope of the Semantic Web in the form of sets of RDF triples, the presented approach opens up the possibility to extract terminological knowledge from the Linked Open Data Cloud. We shall also present first experimental results showing that our approach has the potential to be useful for practical applications.
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Schmidt, Thomas J., Björn Niesen, Catherine Whyte, Esther Thürig, Wim Malfait, René Steiger, Evangelos Panos, et al. Swiss Center of Excellence on Net-Zero Emissions (SCENE): annual report 2023. Paul Scherrer Institute, PSI, October 2024. http://dx.doi.org/10.55402/psi:66609.

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The dramatic and much faster than anticipated rise of global sea level temperatures during 2023 em-phasized how little time we have left to fight climate change and reach net-zero greenhouse gas emis-sions. Within our SCENE Joint Initiative, we have joined forces of our research activities to support Swit-zerland in achieving this urgently needed transition towards a sustainable, net-zero future. Involving more than 130 experts from all six institutions of the ETH Domain, SCENE gathers tremendous knowledge and expertise from various disciplines. It has been a pleasure to witness how these research-ers, who previously focused mainly on their specific fields and in many cases had not known each other, started a lively interaction and collaboration, breaking down boundaries between disciplines and insti-tutions from the very beginning of the initiative in January 2023. This collaborative spirit was fostered by our highly engaged Project Management Team, who made sure that SCENE started operations with full speed. We are excited that we could complement the research consortium with a high-profile Advi-sory Board that guides us with their perspectives from industry, public administration, and politics throughout the project. In addition, we established the Expert Hub, our rapid-response team, to swiftly react to inquiries and requests. We are grateful for the entire SCENE consortium for their contributions to the success of this Joint Initiative. Have a look at our team, check for news, and get in touch with us at www.scene-project.ch.
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Kuhn, Katharina. The International Tax Regime Complex: Understanding Change in Global Tax Governance. Institute of Development Studies, October 2024. http://dx.doi.org/10.19088/ictd.2024.105.

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Following a landmark vote in the UN General Assembly in December 2023, negotiations are taking place that will lead to a new framework convention on tax. While the UN is not new to international tax cooperation, ambitions for the convention extend beyond the remit of its current expert committee into policy areas where the OECD – in particular – already has a body of work. This creates risks of duplication, competition and incoherence that will depend on how the convention develops, how other organisations react, and how states engage with them. In this paper we draw three main insights from international relations scholarship to help understand and mitigate these risks. First, duplication is not new in global tax governance, which is already a ‘regime complex’ comprising multiple overlapping institutions. Policy issues with global implications can be governed coherently with multiple organisations coexisting in different ways, but this is more challenging if, as in international tax, states disagree on the content of substantive underlying norms. Second, the demand for a new institution stems from a legitimacy deficit across the whole regime complex that has input, throughput and output dimensions. As more constituencies become involved in an increasingly politicised domain, ‘input’ legitimacy no longer merely concerns which countries are at the table, but also who is represented, and thus requires a globally inclusive body at political level. In turn, this requires greater attention to throughput legitimacy – procedural fairness and transparency. Third, given the path dependence of international tax institutions, radical and comprehensive institutional reform is unlikely to happen overnight, and change is instead likely to be incremental. Literature suggests that processes through which one set of rules is ‘displaced’ by another can happen slowly in a regime complex characterised by competition between institutions.
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Baca, Julie, Daniel Carruth, Alex Calhoun, Michael Stephens, and Christopher Lewis. Challenges in evaluating efficacy of scientific visualization for usability and aesthetics. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40800.

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This paper presents the results of a study to evaluate the efficacy of scientific visualization for multiple categories of users, including both domain experts as well as users from the general public. Efficacy was evaluated for understanding, usability, and aesthetic value. Results indicate that aesthetics play a critical, but complex role in enhancing both user understanding and usability.
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Enlow, Michelle Bosquet, Richard J. Chung, Melissa A. Parisi, Sharon K. Sagiv, Margaret A. Sheridan, Annemarie Stroustrup, Rosalind J. Wright, et al. Standard Measurement Protocols for Pediatric Development Research in the PhenX Toolkit. RTI Press, September 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0049.2209.

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A challenge in conducting pediatric research is selecting reliable, valid measurement protocols, across a range of domains, that are appropriate for the developmental level of the study population. The purpose of this report is to introduce the research community to the Pediatric Development Research Domain of the National Institutes of Health (NIH)–supported PhenX Toolkit (consensus measures for Phenotypes and eXposures). The PhenX Toolkit provides a catalog of recommended measurement protocols to address a wide range of research topics that are suitable for inclusion in a variety of study designs. In 2018, the Pediatric Development Working Group of experts identified 18 well-established protocols of pediatric development for inclusion in the Toolkit to complement existing protocols. Collectively, the protocols assess parenting, child care attendance and quality, peer relationships, home environment, neonatal abstinence, emotional and behavioral functioning, and other factors that influence child development. The Toolkit provides detailed data collection protocols, data dictionaries, and worksheets to help investigators incorporate these protocols into their study designs. Using standard protocols in studies with pediatric participants will support consistent data collection, improve data quality, and facilitate cross-study analyses to ultimately improve child health.
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Colburn, Ben, Fiona Macpherson, Derek Brown, Laura Fearnley, Calum Hodgson, and Neil McDonnell. Policy and Practice Recommendations for Augmented and Mixed Reality. University of Glasgow, March 2024. http://dx.doi.org/10.36399/gla.pubs.326686.

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This policy report arises from the research project Augmented Reality: Ethics, Perception, Metaphysics, conducted at the University of Glasgow’s Centre for the Study of Perceptual Experience between November 2021 and November 2023. It was funded by a grant from the Royal Society of Edinburgh. The project brought together experts in various academic fields, with partners from industry and regulatory bodies, to explore the nature of augmented and mixed reality technology, the theories underpinning them, and the ethical and legal questions prompted by new technology in this domain.
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Martinez, Cindy, and Micah Musser. U.S. Demand for Talent at the Intersection of AI and Cybersecurity. Center for Security and Emerging Technology, November 2020. http://dx.doi.org/10.51593/2020ca009.

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As demand for cybersecurity experts in the United States has grown faster than the supply of qualified workers, some organizations have turned to artificial intelligence to bolster their overwhelmed cyber teams. Organizations may opt for distinct teams that specialize exclusively in AI or cybersecurity, but there is a benefit to having employees with overlapping experience in both domains. This data brief analyzes hiring demand for individuals with a combination of AI and cybersecurity skills.
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West, Jessica, and Almudena Azcárate Ortega. Norms for Outer Space: A Small Step or a Giant Leap for Policymaking? UNIDIR, 2022. http://dx.doi.org/10.37559/wmd/22/space/01.

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Space is increasingly critical to modern life on Earth. But there is growing concern that, as it becomes more economically and strategically important, tensions between different space actors are heightening in a manner that could lead to conflict. The accelerating proliferation of counterspace capabilities, as well as the enactment of national policies that deem space an operational or warfighting domain, underlines the very real nature of threats that exist and highlights the importance of keeping space peaceful. To address these challenges, some experts in space security have called for more robust norms of behaviour in outer space. This report explores the role of norms as a tool for outer space governance, as well as their challenges and limitations.
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