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Instance segmentation is an approach that identifies, for every pixel, the specific belonging instance of the object. It detects each distinct object of interest in the image. [19] For example, when each person in a figure is segmented as an individual object. Panoptic segmentation combines both semantic and instance segmentation. Like semantic ...
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 ]
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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.
Semantic queries work on named graphs, linked data or triples. This enables the query to process the actual relationships between information and infer the answers from the network of data . This is in contrast to semantic search , which uses semantics (meaning of language constructs) in unstructured text to produce a better search result.
A semantic field denotes a segment of reality symbolized by a set of related words. The words in a semantic field share a common semantic property. [6] A general and intuitive description is that words in a semantic field are not necessarily synonymous, but are all used to talk about the same general phenomenon. [7]
A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples. Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3]
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 ...