Knowledge Representation (KR) Engineering Task
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A Knowledge Representation (KR) Engineering Task is a knowledge creation task that is an engineering task (to created engineered knowledge).
- AKA: Knowledge Representation Modeling.
- Context:
- It can be performed by a Knowledge Engineer (with knowledge engineering skills).
- It can be Labor Intensive (due in part to the Knowledge Acquisition Bottleneck).
- It can be a part of an Knowledge Engineering Authoring Task.
- It can affect an Knowledge Engineering Population Task.
- It can involve the use of an Knowledge Engineering Design System.
- ...
- Example(s):
- Counter-Example(s):
- See: Domain Knowledge, Conceptual Knowledge, Knowledge Base, Expert Systems.
References
2015
- (Gomes, 2015) ⇒ Lee Gomes. (2015). “Facebook AI Director Yann LeCun on His Quest to Unleash Deep Learning and Make Machines Smarter.” In: Spectrum, 18 Feb. 2015.
- QUOTE: LeCun - … The question here is how to represent knowledge. In “traditional” AI, factual knowledge is entered manually, often in the form of a graph, that is, a set of symbols or entities and relationships. But we all know that AI systems need to be able to acquire knowledge automatically through learning. The question becomes, “How can machines learn to represent relational and factual knowledge?” Deep Learning is certainly part of the solution, but it’s not the whole answer. The problem with symbols is that a symbol is a meaningless string of bits. In Deep Learning systems, entities are represented by large vectors of numbers that are learned from data and represent their properties. Learning to reason comes down to learning functions that operate on these vectors. …
2013
- (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/knowledge_engineering Retrieved:2013-12-6.
- Knowledge engineering (KE) was defined in 1983 by Edward Feigenbaum, and Pamela McCorduck as follows:
It is used in many computer science domains such as artificial intelligence, [1] including databases, data mining, expert systems, decision support systems and geographic information systems. Knowledge engineering is also related to mathematical logic, as well as strongly involved in cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works.
Various activities of KE specific for the development of a knowledge-based system:
- Assessment of the problem
- Development of a knowledge-based system shell/structure
- Acquisition and structuring of the related information, knowledge and specific preferences (IPK model)
- Implementation of the structured knowledge into knowledge bases
- Testing and validation of the inserted knowledge
- Integration and maintenance of the system
- Revision and evaluation of the system.
Being still more art than engineering, KE is not as neat as the above list in practice. The phases overlap, the process might be iterative, and many challenges could appear.
- Knowledge engineering (KE) was defined in 1983 by Edward Feigenbaum, and Pamela McCorduck as follows:
2007
- (Kendal, 2007)
- the building, maintaining and development of knowledge-based systems.
2004
- (Krauthammer & Nenadic, 2004) ⇒ Michael Krauthammer, and Goran Nenadic. (2004). “Term Identification in the Biomedical Literature.” In: Journal of Biomedical Informatics, 37(6). doi:10.1016/j.jbi.2004.08.004
- Rule-based approaches generally attempt to recover terms by re-establishing associated term formation patterns that have been used to coin the terms in question.6 The main approach is to (typically manually) develop rules that describe common naming structures for certain term classes using either orthographic or lexical clues, or more complex morpho-syntactic features. Also, in many cases, dictionaries of typical term constituents (e.g. terminological heads, affixes, specific acronyms) are used to assist in term recognition. However, knowledge engineering approaches are known to be extremely time-consuming for development, and – since rules are typically very specific – their adjustment to other entities is usually difficult.
1983
- (Feigenbaum & McCorduck, 1983) ⇒ Edward A. Feigenbaum, and Pamela McCorduck. (1983). “The Fifth Generation (1st edition).” Addison-Wesley. ISBN:9780201115192
- QUOTE: KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.
- (Hayes-Roth et al., 1983) ⇒ Frederick Hayes-Roth, Donald Arthur Waterman, and Douglas B. Lenat, Eds. (1983). “Building Expert Systems.” Addison-Wesley. ISBN:0201106868
- QUOTE: Over time, the knowledge engineering field will have an impact on all areas of human activity where knowledge provides the power for solving important problems. We can foresee two beneficial effects. The first and most obvious will be the development of knowledge systems that replicate and autonomously apply human expertise. For these systems, knowledge engineering will provide the technology for converting human knowledge into industrial power. The second benefit may be less obvious. As an inevitable side effect, knowledge engineering will catalyze a global effort to collect, codify, exchange and exploit applicable forms of human knowledge. In this way, knowledge engineering will accelerate the development, clarification, and expansion of human knowledge.