Semantic attribute correspondences

Semantic attribute correspondences can be modeled using the similarity matrix model (see Sect. 2). Each ontological relationship is modeled as a separate matrix (one matrix for equivalence, one for subsumption, etc.). These matrices represent the confidence level in an ontological relationships, as generated in the first two... 

This chapter introduces three recent advances to the state-of-the-art, extending the abilities of attribute correspondences. Contextual attribute correspondences associate selection conditions with attribute correspondences. Semantic matching extends attribute correspondences to be specified in terms of ontological relationship. Finally, probabilistic attribute correspondences extend attribute correspondences by generating multiple possible models, modeling uncertainty about which one is correct by using probability theory. 

A model was proposed in Magnani et al. [2005] for combining semantic and probabilistic attribute correspondences using constructs of uncertain semantic relationships in an ER model. An uncertain semantic relationship is a distribution of beliefs over the set of all possible semantic relationships, using belief functions [Shafer 1976], The set of possible semantic relations serve as the frame of discernment (marked ), based on which two functions are defined, namely belief and plausability. Both functions assign a value to a subset of the frame of discernment, starting from the basic probability mass that is assigned with each element in the frame of discernment. Belief of a set A e sums the probability mass of all subsets B c A. Plausability of a set A is the sum of all subsets that intersect with A, i.e., all B such that A n B 0. 

For AG driven ASAP scheduling, a synthesized attribute called control step is used and the semantic rule corresponding to (1) is ... 

As defined in Subsect. 2.1.2, a document is an aggregation of data and acts as a carrier of product data in a certain work context. One possibility to represent models of document contents is the use of the extensible Markup Language (XML) [1060] and its Document Type Definitions (DTDs), as suggested by Bayer and Marquardt [19]. Within a DTD, the structure of a specific document type can be described. Figure 2.9 shows the (incomplete) DTD of a specification sheet for vessels. Here, the different data items like temperature or construction material are indicated to specify the piece of equipment. However, the expressiveness of such document type definitions is rather restricted. A DTD specifies only the syntax of the document content but not its semantics. One possibility to enrich the DTD with meaning is to relate the single DTD elements to the classes and attributes of a product data model. This is exemplarily shown Fig. 2.9, where relations between some DTD elements and the corresponding classes of the product data model CLiP (cf. Subsect. 2.2.3) are indicated. 

Each symbd in the vocabulary V (V=NJT) d G has an assodated set d attributes A( Q. Each attribute rqnesents a specific context-sensitive property d the corresponding syttibd. The notation X.a is used to indicate that attribute a is an element d ACIQ. A(X) is partitioned into two digdnt sets the set d synthesized attributes AS( and the set d inherited attributes AI(1Q. Synthesized attributes X.s are those vdiose values are defined in terms d attributes at descendant nodes d node X d the corresponding semantic tree. Inherited attributes X.i are those vdiose values are d ned in terms d attributes at the parrait and (possiUy) the siUing nodes d node X d the corresponding semantic tree. 

Figure 6.6 Examples of the loadings of individual attributes with similar semantic meaning. Groups of attributes are represented on separate plots for better legibility although they all result in the same GPA. Each attribute appears with the corresponding subject s number.

Meta-Model. Our current meta-model is limited to a selection of nine types of software safety requirements, which we derived from the mechanisms captured in our classification, briefly described in Section 3.1. This selection of software safety requirements was driven by our evaluation scenario and it covers all relevant mechanisms for our case study described in Section 4. Each requirement from our selection corresponds to one mechanism in our classification, but not all mechanisms in our classification have been included in the meta-model. For the formalization of this selection, we captured the necessary attributes in a metamodel and specified their semantics in natural language. 

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