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A semantic data model in software engineering has various meanings: It is a conceptual data model in which semantic information is included. This means that the model describes the meaning of its instances. Such a semantic data model is an abstraction that defines how the stored symbols (the instance data) relate to the real world. [1]
All these approaches (natural language, keyword-based search and structured queries) have targeted different degrees of sophistication in addressing the problem of supporting a flexible semantic matching between queries and data, which vary from the completely absence of the semantic concern to more principled semantic models.
A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. [1] As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions (e.g., "Bob is 35", or "Bob knows John").
A semantic asset is a specific type of standard which involves: highly reusable metadata (e.g. xml schemata, generic data models) and/or reference data (e.g. code lists, taxonomies, dictionaries, vocabularies) Organisations use semantic assets to share information and knowledge (within themselves and with others).
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
That is unless the semantic data model is implemented in the database on purpose, a choice which may slightly impact performance but generally vastly improves productivity. Semantic data models. [8] Therefore, the need to define data from a conceptual view has led to the development of semantic data modeling techniques. That is, techniques to ...
Syntactic heterogeneity: is a result of differences in representation format of data; Schematic or structural heterogeneity: the native model or structure to store data differ in data sources leading to structural heterogeneity. Schematic heterogeneity that particularly appears in structured databases is also an aspect of structural heterogeneity.
The relation between computation and the underlying mathematical structures from fields such as logic, set theory, model theory, category theory, etc. It has close links with other areas of computer science such as programming language design, type theory, compilers and interpreters, program verification and model checking.