![]() ![]() Entity extraction locates and classifies text data elements into predefined categories such as the names of persons, products, object type, or organizations. Faceted and filtered search narrows the scope and finds specific information in data collections. Machine learning and deep learning create seamless, immediate query suggestions and continuously improve search query result relevancy over time, predicting what information will provide the most value to users. With out-of-the-box capabilities, it can recognize document types such as contracts, purchase orders, and invoices. With this understanding, intelligent search quickly learns and identifies elements such as headers, footers, charts, and tables. Machine learning enables intelligent search to learn the visual structure of documents specific to your enterprise, industry, or domain-space. Intelligent search tools (such as IBM Watson Discovery) have a document understanding AI that scales to understand many data sources. Semantic search and contextual understanding enable intelligent search to breakdown linguistic nuances, synonyms, and relations found in everyday language and inside complex documents. Natural language processing capabilities enable intelligent search applications to understand and query digital content from multiple data sources. Business data is continuously updated and written in domain-specific terminology. Because they deliver the necessary scalability, cloud infrastructures with extensive API-driven integrations and automation are usually best suited for the task.īusinesses can't use Google or other traditional search engines to find business-specific answers, such as "why is our new product shipment delayed?" or "what were our top reported customer challenges last week?" Intelligent search, unlike search engines and web search (such as Bing, Google Search, or AskJeeves), surfaces information and answers specific to your business.Īrtificial Intelligence powers Intelligent search, equipping tools with the ability to: Today’s intelligent search solutions must be built on architectures that can handle the performance demands of big data workloads. In the face of rapid growth in the volume and variety of data that enterprise search tools must examine, result retrieval speed has become a key indicator of cognitive search algorithm performance. However, the rise and popularization of free, publicly accessible web search engines, such as Google (and its predecessor AltaVista), radically transformed user expectations for information retrieval, content discovery and enterprise search platforms. ![]() With the growth of desktop computing and corporate intranets, commercial enterprise search solutions, such as the IBM Storage and Information Retrieval System (STAIRS) and the local search tool FAST (later acquired by Microsoft), became mainstream in enterprise computing. One of the earliest benefits to implementing multi-user mainframe computer systems was that they facilitated information discovery by finding exact matches to text strings in large document repositories. Enterprise information retrieval systems came into existence long before the public internet did.
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