Knowledge density in raw text: a criterion for assessing the usefulness of texts for expert systems

Knowledge density in raw text: a criterion for assessing the usefulness of texts for expert systems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 April 2026 V1 Latest version Share on Knowledge density in raw text: a criterion for assessing the usefulness of texts for expert systems Author : Olegs Verhodubs 0000-0002-3430-0760 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177574466.69425139/v1 22 views 13 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Raw (unprocessed) text can serve as a source of rules for a rule-based expert system [1]. Several types of sentences from which rules can be generated have been described [2] [3], but this list is far from exhaustive. A prototype Python package for generating rules from raw text has been developed with great potential for further development [4]. Different texts yield different numbers of generated rules. The more rules that can be generated from a text, the more valuable the text is for an expert system and the greater the likelihood that the user will receive a high-quality answer from the expert system. To evaluate a text for the number of rules it contains, and therefore for its usefulness for an expert system, a criterion called knowledge density is introduced. This paper is devoted to familiarization with the knowledge density criterion. This paper describes: the knowledge density of the whole text, point or local knowledge density, text integrity or the emergence criterion of raw text, as well as various properties of the knowledge density of raw text. Supplementary Material File (knowledge_density.pdf) Download 226.03 KB Information & Authors Information Version history V1 Version 1 09 April 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords expert systems knowledge knowledge density knowledge from text natural language processing rules Authors Affiliations Olegs Verhodubs 0000-0002-3430-0760 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 22 views 13 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Olegs Verhodubs. Knowledge density in raw text: a criterion for assessing the usefulness of texts for expert systems. Authorea . 09 April 2026. DOI: https://doi.org/10.22541/au.177574466.69425139/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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