Semantic Heterogeneity is the problem that arises when multiple resources present differences in how they represent the same real-world phenomenon. In KR, an early approach was the development of ontologies and, later on, when ontologies showed at the knowledge level the same semantic heterogeneity that they were meant to fix at the data level, to compute mappings among them. In this paper we acknowledge the impossibility of avoiding semantic heterogeneity, this being a consequence of the more general phenomenon of the diversity of the world and of the world descriptions. In this perspective, the heterogeneity of ontologies is a feature (and not a bug to be fixed by aligning them) which gives the possibility to use the most suitable ontology in any given application context. The main contributions of this paper are: (i) a novel articulation of the problem of semantic heterogeneity, as it appears at the knowledge level, as contextuality, (ii) its qualitative and quantitative formalization in terms of a set of diversity and unity metrics and (iii) an Entity Type Recognition algorithm which selects the contextually most appropriate ontology and exploits it to solve the current problem, e.g., the alignment and integration of a set of input schemas. The experimental results show the validity of the approach.
Off-line Handwritten Flowchart Symbol Recognition Algorithm Robust to Variations Based the Normalized Dominant Slope Vector
