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Knowledge Engineering: Building Cognitive Assistants for Evidence-based Reasoning
Abstract
This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of systems that use expert knowledge and reasoning to solve complex problems. It covers the main stages in the development of a knowledge-based system: understanding the application domain, modeling problem solving in that domain, developing the ontology and the reasoning rules, and testing the system. The book focuses on a special class of systems - learning assistants for evidence-based reasoning that learn complex problem solving expertise directly from human experts, support experts and non-experts in problem solving and decision making, and teach their problem solving expertise to students. A powerful learning agent shell, Disciple-EBR, is included with the book, enabling students, practitioners, and researchers to rapidly develop learning assistants in a wide variety of domains that require evidence-based reasoning, including intelligence analysis, cyber security, law, forensics, medicine, and education.
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“Automated systems that digest large sets of data using named entity recognition, NER (18), are an integral part of public domain databases, for example, DISEASES for gene-disease associations (19); STRING for protein-protein interactions (20); and OpenTargets (21) and Pharos (22) for complex disease-protein-drug annotations, to name a few.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [18]
Citations
Cited by
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
“Specifically for the “why” and “what if” types of questions, relevant, preferably reliable data must be identified, inferred when absent, and connected through evidence-based reasoning, illustrated by the metaphor "connecting the dots" (17).”
BackgroundFilip, F. G., Zamfirescu, C.-B., & Ciurea, C. E. (2016) · Automation, collaboration, and e-services · doi:10.1007/978-3-319-47221-8_2 · Cited by 64
Automated Rational Design of Metal–Organic Polyhedra
Kondinski, A., Menon, A., Nurkowski, D., Farazi, F., Mosbach, S., Akroyd, J., & Kraft, M. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c03402 · Cited by 49
DSS—A Class of Evolving Information Systems
Filip, F. G. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-39250-5_14 · Cited by 43
Algorithms, ontology, and social progress
Iliadis, A. (2018) · Global Media and Communication · doi:10.1177/1742766518776688 · Cited by 34
“Today, ontologies are used in many data-driven media technologies like virtual assistants and social media platforms (Tecuci et al., 2016).”
BackgroundKnowledge Management and Data Analysis Techniques for Data-Driven Financial Companies
Pisoni, G., Molnár, B., & Tarcsi, Á. (2023) · Journal of the Knowledge Economy · doi:10.1007/s13132-023-01607-z · Cited by 32
“…• Inference: deduction, induction, abduction, analogy • Computational or representational mechanism: rules, trees, neural networks (NN) • Learning goal: to learn a concept, discover a formula, acquire new facts, acquire new knowledge about an entity, and refine an entity (Tecuci et al., 2016).”
InfluentialKnowledge Engineering in Chemistry: From Expert Systems to Agents of Creation
Kondinski, A., Bai, J., Mosbach, S., Akroyd, J., & Kraft, M. (2022) · Accounts of Chemical Research · doi:10.1021/acs.accounts.2c00617 · Cited by 23
Knowledge graph representation of zeolitic crystalline materials
Kondinski, A., Rutkevych, P. P., Pascazio, L., Tran, D. N., Farazi, F., Ganguly, S., & Kraft, M. (2024) · Digital Discovery · doi:10.1039/d4dd00166d · Cited by 19
Reflection in Transformative Learning: The Challenge of Measurement
Savicki, V., & Price, M. V. (2021) · Journal of Transformative Education · doi:10.1177/15413446211045161 · Cited by 21
“Omitting context constrains interpretation (von Glaserfeld, 2005). Rather, a focus on the context of the experience presumes that events may be changeable depending on the situation and one’s point of observation. Descriptions of experiences devoid of emotional content (feelings, values, and attitudes) lead to intelle…”
BackgroundContext-aware cognitive design assistant: Implementation and study of design rules recommendations
Huet, A., Segonds, F., Pinquié, R., Veron, P., Guegan, J., & Mallet, A. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101419 · Cited by 14
Evidence-Based Reasoning in Intelligence Analysis: Structured Methodology and System
Tecuci, G. D., Kaiser, L., Marcu, D., et al. (2018) · Computing in Science & Engineering · doi:10.1109/mcse.2018.2873852 · Cited by 12
Self-citationA Property Graph Data Model for a Context-Aware Design Assistant
Pinquié, R., Véron, P., Segonds, F., et al. (2019) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-42250-9_17 · Cited by 11
Will Artificial Intelligence Outperform the Clinical Neurologist in the Near Future? Yes
Bhidayasiri, R. (2021) · Movement Disorders Clinical Practice · doi:10.1002/mdc3.13202 · Cited by 10
“To the best of my knowledge, such advances of knowledge engineering, which aim to solve problems that usually require a high level of human expertise, still remains to be accomplished.(10) Despite these limitations, AI still has an enormous potential in the management of PD, especially when it is operating in socalled…”
BackgroundShould I Interfere' AI-Assistants' Interaction with Knowledge Workers
Ferreira, J. J., Fucs, A., & Segura, V. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3299052 · Cited by 10
“The investigation of AI-as (especially conversational agents or chatbots) interaction with users is presenting interesting results related to general tasks, like add an appointment to a calendar, call another person, shop online, and so on [4][8][12] using mainly voice commands.”
BackgroundIntelligent Services Development Technology Using the IACPaaS Cloud Platform
Gribova, V. V., Moskalenko, P., Timchenko, V. A., et al. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-15882-7_2 · Cited by 7
Benetis, D., Vitkus, D., Janulevičius, J., et al. (2024) · Electronics · doi:10.3390/electronics13132642 · Cited by 6
Evidence-Based Detection of Advanced Persistent Threats
Tecuci, G. D., Marcu, D., Meckl, S., et al. (2018) · Computing in Science & Engineering · doi:10.1109/mcse.2018.2873854 · Cited by 6
“The ontology language is an extension of RDFS(13,14) with additional features to facilitate learning and evidence representation.(7) The bottom right part in Figure 2 shows the ontological representation of a trigger.”
BackgroundMethodInfluentialSelf-citationKnowledge Management Technology Using Ontologies, Cognitive Models and Production Expert Systems
Massel, L., Massel, A. G., & Пестерев, Д. В. (2019) · doi:10.2991/csit-19.2019.49 · Cited by 5
Filip, F. G., Zamfirescu, C.-B., & Ciurea, C. E. (2023) · Automation, collaboration, and e-services · doi:10.1007/978-3-031-44373-2_5 · Cited by 4
Leclerc, O., Vergès, É., & Vial, G. (2022) · The International Journal of Evidence & Proof · doi:10.1177/13657127221076172 · Cited by 3
“As Twining had anticipated,1 the development of cognitive agents (Tecuci et al., 2016) has renewed interest in Wigmore’s chart method, since, by providing a structure for the ordering of reasoning, the method helped prepare the ground for its automation by software (Vignaux and Robertson, 1992; Bex et al.”
MethodCoRgI: Cognitive Reasoning Interface
Segura, V., Ferreira, J. J., Fucs, A., Moreno, M. F., de Paula, R., & Cerqueira, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91244-8_32 · Cited by 2
Evidence-Based Reasoning and Applications
Tecuci, G. D. (2018) · Computing in Science & Engineering · doi:10.1109/mcse.2018.2875784 · Cited by 2
Self-citationSecurity Vulnerability Assessment of SCADA System for Evidence-Based Reasoning and Bayesian Network
Luo, X., Duan, B., Peng, X., et al. (2019) · IEEE PES Asia-Pacific Power and Energy Engineering Conference · doi:10.1109/appeec45492.2019.8994582 · Cited by 2
“It consists of 3 processes: Evidence in search of hypotheses; Hypotheses in search of evidence; Evidentiary testing of hypotheses [8].”
BackgroundJuez inteligente. Sistema experto que asiste al Juez en la valoración de la Prueba Judicial
Vélez, O. V. (2021) · Derecho & Sociedad · doi:10.18800/dys.202102.009 · Cited by 2
Process-Based Knowledge Organization
Bi, H. H. (2022) · Journal of Database Management · doi:10.4018/jdm.299558 · Cited by 1
Keraia: A Knowledge Engineering and Reference AI Architecture
Varey, S. R., Stefano, A. D., & Han, T. A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3562-4_27 · Cited by 1
Прототип интеллектуальной электронной книги с использованием технологии прямого наложения знаний
Бронфельд, Г. Б., Bronfeld, G. B., Киров, Д. И., et al. (2019) · Международный журнал Программные продукты и системы · doi:10.15827/0236-235x.127.403-410
Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents
Meckl, S., Tecuci, G. D., Marcu, D., et al. (2021) · Springer eBooks · doi:10.1007/978-3-030-55692-1_7
Self-citationExpert Systems in Innovation Project Management: Architecture and Application
Kultin, N. B. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-98832-6_36
Duran, V., & Alma, M. H. (2023) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub149
Evidentiary Reasoning and Artificial Intelligence in the Assessment of the Judicial Proof
Vélez, O. V., & Restrepo, A. F. J. (2022) · Opinión Jurídica · doi:10.22395/ojum.v21n46a3
“(Tecuci, Schum et al. , 2016).”
InfluentialA Weighted Bipolar Argumentation Framework for Reasoning with Incomplete Information
Tong, T., Zhang, Z., & Li, W. (2025) · doi:10.1145/3788149.3788208
“To handle these problems, we proposes the Weighted Bipolar Ar-gumentation Framework for Incomplete Information (WBAFII), inspired by Tecuci’s Evidence-based Reasoning theory [14], a methodology for evaluating hypotheses from uncertain evidence.”
Influential
Metadata
- Authors
- Tecuci, G., Marcu, D., Boicu, M., & Schum, D. A.
- Affiliations
- George Mason University (US)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2016-07-31
- OpenAlex
- W4236488977
- Cited by
- 33
- References
- 0
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 4 · 2022: 8 · 2021: 5 · 2020: 1 · 2019: 5 · 2018: 5 · 2016: 1
- FWCI
- 0.87
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Computational Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- knowledge engineering, expert systems, evidence-based reasoning, learning assistants, ontology construction, reasoning rules, decision support
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