Formal Concept Analysis (FCA) in green farmland databases and Concept Intent Reduction for the mass food production provides a method for extracting concepts from binary contexts. However, FCA-concepts cannot describe negations and disjunctions of attributes. Hence, we take the logic operators into consideration in the process of constructing concepts and obtain new extended concepts, which are more expressive than FCA-concepts. This study mainly discusses the connections between FCA-concepts and concepts with logic values in green farmland databases and concept intent reduction for the mass food production and provides a method for reducing concepts. The reduction does not lose essential information. Results can be used in data mining and construction of architecture ontology.

No abstract

No abstract

An interesting topic on designing entity relationship (ER) schemas is how to transform ER schemas into knowledge bases (KBs) in description logics (DLs). It is significance in translations that one can use automated DL reasoning services to support the development and maintenance of correct ER schemas. This paper proposed a faithful translation, which translates ER schemas and ER models into KBs in the description logic ALENI+. The faithfulness preserves the satisfiability and the unsatisfiability, and therefore the translation is sound. The translation allows us to reduce reasoning on ER schemas to finite models reasoning on ALENI+ KBs

In the design of wireless sensor networks (WSNs), it is important to reduce energy consumption and extend the service life of the sensors. Selecting one of the minimum sensor combinations (MSCs) that can monitor all areas, while the other MSCs are asleep, can effectively extend the lifetime of WSNs. This paper proposes two algorithms based on Formal Concept Analysis (FCA) for extracting some MSCs, to minimize the energy consumption and meet the coverage requirement. These two methods firstly extract the concept lattice from a monitor-areas context, and then it is simple to extract sensor nodes monitoring the overlapping area from the concept lattice. The algorithms consist of three steps as follows: the first step is to transform sensors and monitoring areas into a context, the second is to extract the concept lattice and implications among areas, and the third is to extract some different MSCs that can monitor all areas. Thus, some strategies are designed to awaken different MSCs to achieve the purpose of reducing energy consumption. Experimental results show that these methods have played a positive effect on extracting different MSCs and extending the lifetime of sensor networks.