Knowledge Engineering and Management: Proceedings of the by Zhenkun Wen, Tianrui Li (eds.)

By Zhenkun Wen, Tianrui Li (eds.)

"Knowledge Engineering and administration" provides chosen papers from the 2013 foreign convention on clever platforms and data Engineering (ISKE2013). the purpose of this convention is to collect specialists from assorted services components to debate the state of the art in clever platforms and information Engineering, and to give new study effects and views on destiny improvement. the subjects during this quantity contain, yet no longer constrained to: wisdom illustration and Modeling, wisdom upkeep, wisdom Elicitation, Knowledge-Based platforms (KBS), content material administration and data administration platforms, Ontology Engineering, info Mining and data Discovery, wisdom Acquisition, and so on. The court cases are profit for either researchers and practitioners who are looking to make the most of wisdom engineering tools of their particular study fields.

Dr. Zhenkun Wen is a Professor on the university of desktop and software program Engineering, Shenzhen collage, China. Dr. Tianrui Li is a Professor on the institution of knowledge technological know-how and know-how, Southwest Jiaotong collage, Xi’an, China.

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Section 4 gives some illustrative examples. The paper ends with conclusions in Sect. 5. An Incremental Learning Approach for Updating Approximations 45 2 Preliminaries In this section, we briefly review some concepts, notations, and results of RSMDU [26, 28]. Let U and V be two nonempty finite sets, called universes. R is an arbitrary binary relation from U to V. The triple (U, V, R) is called a generalized approximation space. Definition 1 For a generalized approximation space (U, V, R), 8x 2 U; y 2 V, the neighborhood R(x) of an element x in universe U is defined as follows: RðxÞ ¼ fy 2 Vjðx; yÞ 2 Rg ð1Þ Definition 2 Given a generalized approximation space (U, V, R), 8Y ffi V, the lower and upper approximations of Y in terms of the relation R are defined as: RðYÞ ¼fx 2 UjRðxÞ ffi Y ^ RðxÞ 6¼ ;g RðYÞ ¼fx 2 UjRðxÞ \ Y 6¼ ; _ RðxÞ ¼ ;g: ð2Þ If RðYÞ 6¼ RðYÞ; RðYÞ ¼ ðRðYÞ; RðYÞÞ is called as a rough set of Y respect to universes U and V.

3, we will illustrate the process ontology of geometry. In Sect. 4, a GKB will be built on process ontology. In Sect. 5, construction of abstract models (AMS) and instance models (IMS) will be shown, and automated theorem proving will be given. , workflow) from given conditions to conclusions according to rules such as definitions and theorems. Moreover, the proving process can be divided into a series of logical segments with causality, so that each logical segment is composed of conditions, conclusion, and reason.

Comput Technol Dev, 114–115 5. Bo Z (2011) Chinese views sentence extraction based on SVM. The Academy of Computer of Beijing University of Posts and Telecommunications, Beijing 6. Xi W (2011) Research of methods on multi-granularity fusion of Chinese sentences’ subjectivity and sentiment classification. The Academy of Computer Science of Heilongjiang University, Harbin 7. Ellen R, Janyce W, Phillips W (2003) Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of CoNLL’03:25-3 20 W.

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