Knowledge Mining With Azure Search
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Azure Search offers powerful Cognitive search to enrich your index. In this session, we’ll cover the Index, Indexer, and Querying on structured and unstructured data, Cognitive search capabilities using the Azure portal, and using REST API’s using C#.
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Knowledge Mining With Azure Search
- 1. Knowledge Mining with Azure Search Udaiappa Ramachandran ( Udai ) https://udai.io
- 2. Who am I? • Udaiappa Ramachandran ( Udai ) • CTO, Akumina, Inc., • Focus on Cloud Computing • Microsoft Azure, Amazon Web Services and Google • New Hampshire Cloud User Group (http://www.meetup.com/nashuaug ) • https://udai.io
- 3. Agenda • Traditional Search • Cognitive Search • Knowledge Mining • Knowledge Store • Demo • References • Q & A
- 5. Full Text Indexing • Indexer (Crawler) • Index • Query • Analyzer • Score
- 6. Cognitive Services • Vision • Computer Vision, Face, and Content Moderator service • Tag, Face, Get Thumbnail, Color, Image Type, GetAreaOfInterest, OCR, Adult, Celebrity, Landmark, Detect, Objects, Brand, Describe, Recognize Text, Read, Moderate, Review, Face Detection, Face Verification, Face Identification, Face Grouping, Similar Face Search, and Face Storage • Languages • Language Understanding (LUIS), Translator Text and Text Analytics Service • Text Requests, Speech Requests, Sentiment Analysis, Key Phrase Extraction, Language Detection, Named Entity Recognition, Text Translation, Customization, Language Detection, Bilingual Dictionary, and Transliteration
- 7. Azure Cognitive Search https://docs.microsoft.com/en-us/azure/search/cognitive-search-concept-intro
- 9. Reference • https://docs.microsoft.com/en-us/azure/search/search-lucene-query-architecture • https://aka.ms/kmsolutions • https://aka.ms/cognitivesearch • https://github.com/Azure-Samples/cognitive-search-templates
Notas do Editor
- https://www.bostoncodecamp.com/CC32/sessions/details/16821
- Tika for document extraction or anything similar
- Scoring: TF/IDE (term frequency-inverse document frequency). Term Frequencey: The weight of a term that occurs in a document is simply proportional to the term frequency. Inverse document frequency: The specificity of a term can be quantified as an inverse function of the number of documents in which it occurs. Scoring: Scoring profile—pass the query string scoring parameter Term boosting – lucene only Stages: Query Parsing, Lexical analysis, Document Retrieval, Scoring Analyzer (index analyzer, search analyzer)
- Natural language processing skills include entity recognition, language detection, key phrase extraction, text manipulation, and sentiment detection. With these skills, unstructured text can assume new forms, mapped as searchable and filterable fields in an index. Image processing skills include Optical Character Recognition (OCR) and identification of visual features, such as facial detection, image interpretation, image recognition (famous people and landmarks) or attributes like colors or image orientation. You can create text-representations of image content, searchable using all the query capabilities of Azure Cognitive Search. Cognitive skills in Azure Cognitive Search are based on pre-trained machine learning models in Cognitive Services APIs: Computer Vision and Text Analysis.