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Consequently, any mapping from real world applications into computer applications requires a certain amount of technical background knowledge by the user, where the semantic gap manifests itself. It is a fundamental task of software engineering to close the gap between application specific knowledge and technically doable formalization. For ...
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos.From the perspective of engineering, it seeks to automate tasks that the human visual system can do.
One of the most active areas of knowledge representation research is the Semantic Web. [citation needed] The Semantic Web seeks to add a layer of semantics (meaning) on top of the current Internet. Rather than indexing web sites and pages via keywords, the Semantic Web creates large ontologies of concepts. Searching for a concept will be more ...
Advanced Placement (AP) Biology (also known as AP Bio) is an Advanced Placement biology course and exam offered by the College Board in the United States. For the 2012–2013 school year, the College Board unveiled a new curriculum with a greater focus on "scientific practices".
Computational semantics is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions. [1] It consequently plays an important role in natural-language processing and computational linguistics.
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.
Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content [citation needed] as opposed to lexicographical similarity.