The Next Generation of Enterprise Search
Enterprise Search applications benefit from Artificial Intelligence (AI) and Machine Learning. The former Enterprise Search based on full text search becomes Cognitive Search - the next generation of Enterprise Search.
Cognitive Search enables more than just the search for information in structured and unstructured company data. As a content analytics platform, it enables completely new application scenarios for the user. The intelligent search application is able to link and correlate data in a meaningful way. Instead of list-based hit lists, smart dashboards present the results and enrich data with interactive and graphic elements for the user in a clear and appealing way.
With Cognitive Search, companies are better able to evaluate complex data sets. In addition, Cognitive Search offers the user more relevant search results than conventional Enterprise Search, which are more individually and precisely tailored to his subject area.
Cognitive search - these are the essential features:
Natural Language Processing (NLP)
Cognitive search applications are able to process natural language search queries. The user places his query in questions that begin with "Who", "How", "Where" or "When" - just as he is used to from his everyday language life.
Search results can be presented in the form of a semantic network on the basis of graph databases. Relationships, links and hierarchies can thus be identified more quickly. Semantic networks are especially helpful for expert searches.
In addition to the existing search input, the intelligent search engine suggests further results and terms that might be relevant for the topic.
Results are not just delivered in a tabular or list-based hit list, but are presented in a graphically appealing way and enriched with further information such as images, videos or news.
By supporting rule-based procedures, the application recognizes entities such as personal names, product names, locations, and even contract clauses.
Machine learning methods such as the recognition of predicate argument structures make it possible to record and evaluate documents thematically. Data that is password-protected or stored in the Deep Web can also be taken into account.
Search results can be output on a person-specific or user group-specific basis. The relevance of the results within the hit list is adjusted accordingly. Certain documents or file types can thus be pushed upwards.
This is how you benefit from Cognitive Search
More insights: Recognize existing relationships between data at a glance and uncover relationships you would otherwise not have come across.
Lower costs: Save costs by relieving employees of standard tasks such as manually assigning documents and presorting e-mails.
Faster service: Answer customer queries even faster, because the intelligent search engine provides you with the right hits that are relevant to you and makes the essential documents available to you in an uncomplicated manner.
Automate work steps: Automate essential work processes such as document classification.
More targeted distribution of knowledge: Avoid annoying clicking through endless hit lists, but get the results that are really relevant for you or your department.
Cognitive Search: Use Cases
Contract Analysis: Save yourself the trouble of reading long contracts. Cognitive search applications extract the most important clauses and list them clearly.
Chatbots: The chatbot records the customer inquiry, extracts the most important statements, compares them with the underlying database and then either answers the question itself or forwards them to the right contact person.
Classification of incoming mail: Automate your e-mail workflow and have e-mails intelligently pre-sorted into the respective mailboxes.
E-Discovery: Identify rule violations and monitor whether sensitive data is leaving your company.
Technology Scouting: Carry out automated market observations and no longer miss any major trends: a crawler crawls relevant websites at regular intervals for a specific issue.