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emergency. Once the solution to the problem was known, there was not a critical demand to store large amounts of data back to a permanent memory store. A more precise statement would be that given the technologies available, researchers compromised and did without these capabilities because they realized they were beyond what could be expected, and they could develop useful solutions to non-trivial problems without them. Even from the beginning, the more astute researchers realized the potential benefits of being able to store, analyze, and reuse knowledge. For example, see the discussion of
Corporate Memory in the earliest work of the
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serve as a repository of manuals, procedures, policies, best practices, reusable designs and code, etc. In both cases the distinctions between the uses and kinds of systems were ill-defined. As the technology scaled up it was rare to find a system that could really be cleanly classified as knowledge-based in the sense of an expert system that performed automated reasoning and knowledge-based in the sense of knowledge management that provided knowledge in the form of documents and media that could be leveraged by humans.
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instead would need to store information about thousands of tables that represented information about specific humans. Representing that all humans are mortal and being able to reason about any given human that they are mortal is the work of a knowledge-base. Representing that George, Mary, Sam, Jenna, Mike,... and hundreds of thousands of other customers are all humans with specific ages, sex, address, etc. is the work for a database.
38:
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339:, and multimedia support were now critical for any corporate database. It was no longer enough to support large tables of data or relatively small objects that lived primarily in computer memory. Support for corporate web sites required persistence and transactions for documents. This created a whole new discipline known as
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but the meaning had a big difference. In the case of previous knowledge-based systems, the knowledge was primarily for the use of an automated system, to reason about and draw conclusions about the world. With knowledge management products, the knowledge was primarily meant for humans, for example to
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As expert systems moved from being prototypes to systems deployed in corporate environments the requirements for their data storage rapidly started to overlap with the standard database requirements for multiple, distributed users with support for transactions. Initially, the demand could be seen in
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Large, long-lived data: A corporate database needed to support not just thousands but hundreds of thousands or more rows of data. Such a database usually needed to persist past the specific uses of any individual program; it needed to store data for years and decades rather than for the life of a
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The volume requirements were also different for a knowledge-base compared to a conventional database. The knowledge-base needed to know facts about the world. For example, to represent the statement that "All humans are mortal", a database typically could not represent this general knowledge but
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Early expert systems also had little need for multiple users or the complexity that comes with requiring transactional properties on data. The data for the early expert systems was used to arrive at a specific answer, such as a medical diagnosis, the design of a molecule, or a response to an
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emerged. These were systems designed from the ground up to have support for object-oriented capabilities but also to support standard database services as well. On the other hand, the large database vendors such as
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Your database is that patient's record, including history... vital signs, drugs given,... The knowledge base... is what you learned in medical school... it consists of facts, predicates, and beliefs...
274:. Not just tables with numbers and strings, but pointers to other objects that in turn have additional pointers. The ideal representation for a knowledge base is an object model (often called an
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actually predated the
Internet but with the Internet there was great synergy between the two areas. Knowledge management products adopted the term "knowledge-base" to describe their
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added capabilities to their products that provided support for knowledge-base requirements such as class-subclass relations and rules.
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The first knowledge-based systems had data needs that were the opposite of these database requirements. An expert system requires
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The term "knowledge-base" was coined to distinguish this form of knowledge store from the more common and widely used term
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Data was usually represented in a tabular format with strings or numbers in each field.
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consists of a knowledge-base representing facts about the world and ways of
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Green, Cordell; D. Luckham; R. Balzer; T. Cheatham; C. Rich (1986).
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about those facts to deduce new facts or highlight inconsistencies.
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Hayes-Roth, Frederick; Donald
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properties: Atomicity, Consistency, Isolation, and
Durability.
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Readings in
Artificial Intelligence and Software Engineering
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The next evolution for the term "knowledge-base" was the
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The other driver for document support was the rise of
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literature) with classes, subclasses and instances.
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Introduction to
Database and Knowledge-base Systems
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335:. With the rise of the Internet, documents,
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678:. Singapore: World Scientific Publishing.
474:Artificial intelligence: a modern approach
238:A database had the following properties:
122:Learn how and when to remove this message
212:. During the 1970s, virtually all large
609:. Reading, MA: Addison-Wesley. p.
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958:Knowledge representation and reasoning
892:Semantic service-oriented architecture
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60:adding citations to reliable sources
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472:(2021). "Knowledge-based agents".
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231:. At this point in the history of
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148:knowledge representation language
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389:Information repository
384:Enterprise bookmarking
341:Web Content Management
233:information technology
192:knowledge-based system
492:, Vikash Mansinghka,
953:Knowledge management
948:Knowledge extraction
648:. Berlin: Springer.
419:Ontology engineering
404:Knowledge management
356:Knowledge Management
348:knowledge management
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963:Library 2.0
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494:Judea Pearl
434:Text mining
290:program by
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790:Ontologies
768:Background
659:1 December
586:1 December
512:1124776132
456:References
243:Flat data:
226:relational
202:Properties
166:used by a
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988:Topic map
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780:Hypertext
775:Databases
352:HCL Notes
337:hypertext
196:reasoning
156:inference
112:June 2014
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978:Metadata
785:Internet
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379:Database
367:See also
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