Knowledge-Based Software Engineering: 11th Joint Conference, by Alla Kravets, Maxim Shcherbakov, Marina Kultsova, Tadashi

By Alla Kravets, Maxim Shcherbakov, Marina Kultsova, Tadashi Iijima

This booklet constitutes the refereed complaints of the eleventh Joint convention on Knowledge-Based Software-Engineering, JCKBSE 2014, held in Volgograd, Russia, in September 2014. The fifty nine complete and three brief papers provided have been conscientiously reviewed and chosen from 197 submissions. The papers are geared up in topical sections on method and instruments for wisdom discovery and information mining; tools and instruments for software program engineering schooling; wisdom applied sciences for semantic internet and ontology engineering; knowledge-based equipment and instruments for trying out, verification and validation, upkeep and evolution; traditional language processing, photo research and popularity; knowledge-based equipment and functions in details safety, robotics and navigation; selection help equipment for software program engineering; structure of knowledge-based structures, together with clever brokers and softbots; automating software program layout and synthesis; wisdom administration for enterprise tactics, workflows and firm modeling; knowledge-based tools and purposes in bioscience, medication and justice; knowledge-based requisites engineering, area research and modeling; clever consumer interfaces and human-machine interplay; lean software program engineering; application realizing, programming wisdom, modeling courses and programmers.

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For rate document, previously carried out identification of semantically meaningful part of document and transfer it in natural language. [13]. Then calculates necessary criteria for determine quality of document. If quality of document below threshold set by expert, then such query and rule on which basis it was built must be corrected. To do this, in first step needed to determine nature of existing error, depending on which, should be define procedure for correction rule. After determining nature of occurred error, determines its influence strength on result.

For each received document rates its information content and degree of similarity with knowledge domain that depend on the specified expert preferences [12]. For rate document, previously carried out identification of semantically meaningful part of document and transfer it in natural language. [13]. Then calculates necessary criteria for determine quality of document. If quality of document below threshold set by expert, then such query and rule on which basis it was built must be corrected. To do this, in first step needed to determine nature of existing error, depending on which, should be define procedure for correction rule.

Recursion deep of production inference and references type used for generation query depend on results received from search engine. Each generated subquery sent to search engine that return set of documents with a setting depth of excerpts results. For each received document rates its information content and degree of similarity with knowledge domain that depend on the specified expert preferences [12]. For rate document, previously carried out identification of semantically meaningful part of document and transfer it in natural language.

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