8/15/2023 0 Comments Contextual pdf searchstructured), but are rapidly approaching each other in terms of data and feature capabilities. These technologies were originally developed with different data in mind (unstructured vs. The embedded contextual metatags are essential in mining for facts, since global metatags lose valuable implicit information.Ĭontextual search can also be viewed as a bridging technology, merging search engines and databases. Contextual search combines ITCM, used for entity extraction and other content enrichment, with QTCM, used to find new relationships at query time based on the structural information preserved in the indexed content. Contextual search also provides "query time content mining" (QTCM), allowing end users to be content miners, on structured, unstructured or rich media content-an area previously reserved for deep experts. Traditional systems allow only "index time content mining" (ITCM), where data models are defined before indexing time after indexing, the original content is typically discarded, so important relations built in the context are irretrievably lost, and the discovery is based on models that are on average 80% correct. The combination gives unprecedented freedom to both front-end providers and end users. This power depends on simultaneous scalability in several dimensions: data volume, query traffic, data and query complexity, fault tolerance, realtime capabilities, etc. Scalability and consistency of the system are the very foundation for heavy data crunching behind the scenes, seamlessly filtering and improving structured, unstructured and rich media content, queries and results, with no performance penalty for the users. For example, the users can formulate queries in XPath or an optimal subset of XQuery. With this, new types of precise queries can be asked that combine structure and content, imposing contextual constraints on the content. Retrieval systems dealing with a large number of sources need the same flexibility -they must be "schema independent." Contextual search engines provide this independence by replacing predefined index layouts with a nested structure that has scopes and tags. With the advancement of XML technologies, a de facto standard document structuring framework allows authors to define their own sets of tags and document structures, also known as schemas. The discovery is even extended to rich media. They can, for example, be used to discover new patterns in consumer behavior, or help research in biosciences by exploring implicit links between concepts in scientific texts. Text and data mining are the automated discovery of new information by extraction of patterns and relationships between entities in text or structured data sources, respectively. High-performing search technology, enabling scalable and fast content gathering, processing and retrieval in face of exploding information volume and complexity. Flexible content structuring technologies, enabling XML schema independence and cross-connection of content from structured and unstructured sources andģ. Intelligent text and data mining, enabling information discovery through automated concept modeling and exploration of patterns Ģ.
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