The DOI matches its Crossref record.
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.
Overview
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In the citing paper
“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).”
Background
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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