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The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. According to Andreas M. Hein, the semantic gap can be defined as "the difference in meaning between constructs formed within different representation systems". [ 1 ]
Semantic intelligence [1] is the ability to gather the necessary information to allow to identify, detect and solve semantic gaps on all level of the organization.. Similar to Operational intelligence or Business Process intelligence, which aims to identify, detect and then optimize business processes, semantic intelligence targets information instead of processes.
The semantic gap is often mentioned in regard to CBIR. The semantic gap refers to the gap between the information that can be extracted from visual data and the interpretation that the same data have for a user in a given situation. [17] The ACM SIGMM Workshop on Multimedia Information Retrieval [18] is dedicated to studies of CBIR.
In linguistics an accidental gap, also known as a gap, paradigm gap, accidental lexical gap, lexical gap, lacuna, or hole in the pattern, is a potential word, word sense, morpheme, or other form that does not exist in some language despite being theoretically permissible by the grammatical rules of that language. [1]
Before the RISC philosophy became prominent, many computer architects tried to bridge the so-called semantic gap, i.e., to design instruction sets that directly support high-level programming constructs such as procedure calls, loop control, and complex addressing modes, allowing data structure and array accesses to be combined into single instructions.
X Wikipedia Google Wikipedia as a subject of academic studies health information on Wikipedia: Semantic Wikipedia: 2007-12 Wikipedia semantic wiki Semantic MediaWiki Semantic Web Wikipedia as a subject of academic studies: Overcoming the brittleness bottleneck using Wikipedia: enhancing text categorization with encyclopedic knowledge: 2006-07
Distributional–relational models were first formalized, [3] [4] as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the ...
Semantic spaces [note 1] [1] in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can ...