ECIR-2014: Multilanguage Content Discovery Through Entity Driven Search
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The document outlines a presentation on entity-driven search by Alessandro Benedetti and Antonio David Pérez Morales, focusing on enhancing search capabilities in heterogeneous data repositories. The presentation discusses the limitations of traditional keyword-based search and introduces a semantic enterprise search approach that leverages natural language processing and machine learning for improved user experience and accuracy. It also touches on future developments in semantic search, including smart autocomplete and secured entity searches.
ECIR-2014: Multilanguage Content Discovery Through Entity Driven Search
- 1. ECIR 2014 Industry Day Content Discovery Through Entity Driven Search Alessandro Benedetti http://uk.linkedin.com/in/alexbenedetti Antonio David Perez Morales http://es.linkedin.com/in/adperezmorales 16th April 2014
- 2. • Experienced at building and delivering a wide range of enterprise solutions across the whole information life cycle • Alfresco & Ephesoft certified Platinum Partner • Red Hat Enterprise Linux Ready Partner • Crafter & Varnish Gold Partners • Search Solutions Consultant Alfresco Partner of the Year 2012 and 2013
- 3. Working effectively together Who We Are 3 Antonio David Pérez Morales - R&D Senior Engineer - Master in Engineering and Technology Software - Digital Identity and Security expert - Enterprise Search Background - Semantic, NLP, ML Technologies and Information Retrieval lover - Apache Stanbol Committer - Apache contributor @adperezmorales http://es.linkedin.com/in/adperezmorales/ Alessandro Benedetti - R&D Senior Engineer - Master in Computer Science - Information Retrieval background -- Enterprise Search specialist - Semantic, NLP, ML Technologies and Information Retrieval lover @AlexBenedetti http://uk.linkedin.com/in/alexbenedetti
- 4. Working effectively together Agenda 4 • Context • Problem • Solution • Demo • Future Works
- 5. Working effectively together Agenda 5 • Context • Problem • Solution • Demo • Future Works
- 6. Working effectively together Zaizi R&D Department 6 •Giving sense to the content • Enriching it semantically •Adding value to ECM/CMS • More structured content, easy to manage, link and search, •Improving search • Across different domains, data sources, User Experience • Machine Learning applied research • Content Organization – Recommendation Systems
- 7. Working effectively together Agenda 7 • Context • Problem • Solution • Demo • Future Works
- 8. Working effectively together Enterprise Search Problems 8 Challenge : Search within Big and Heterogeneus Repositories • Heterogeneus Data Sources • Filesystem, DB, ECM/CMS, Email, … • Unstructured Content • PDFs, text plain, Word, … • Documents not linked between each other • Federated Search needed • Search across data sources • Different permissions • Centralized endpoint
- 9. Working effectively together Current Enterprise Search Weaknesses 9 • Keyword based • Low precision • Ambiguous terms not in context • Not accurate weighting when keywords are combined in a query
- 10. Working effectively together Agenda 10 • Context • Problem • Solution • Demo • Future Works
- 11. Working effectively together Entity Driven Search 11 • Moves from keywords to Entities •More understandable to a Human • Process the unstructured text • Enrich it • Build specific indexes • Use entities and concepts in searches
- 12. Working effectively together Sensefy 12 • Semantic Enterprise Search Engine • Federated Search • Evolved User Experience • Based on cutting-edge Open Source Frameworks
- 14. Working effectively together RedLink 14 • Semantic Cloud platform • Providing Software as a Service • Manage unstructured data • Extract knowledge and intelligence • Make sense of information • Feed into business processes • Open-Source based components • Entity Linking using Knowledge Bases
- 15. Working effectively together NLP & Semantic Enrichment 15 • From unstructured to structured • NLP Analysis. POS Tagging • Named Entities Recognition • Linked Data • Entity Linking using Knowledge Bases • Disambiguation • Indexing in Solr
- 16. Working effectively together Smart Autocomplete 16 • Multi Phase suggestions • Closer to natural language query formulation • Named Entities infix • Entity types infix • Multi Language entity type support • Properties driven query approach
- 17. Working effectively together Smart Autocomplete Configuration 17 • Entity type properties •Interesting to our use case and scenario • Properties inheritance through type hierarchy • Enhance type information from external resource •Freebase, DbPedia , Custom Data Set
- 18. Working effectively together Semantic Search 18 • Search by Named Entity • Search by Entity Type • Search by Entity Type properties • Grouping Results by Sense • Contextualize Results Using Semantic Information
- 19. Working effectively together Semantic More Like This 19 • Search for Similar Documents based on Entities and Entities’ categories • Similarity Function based on Documents’ Sense • Not based on text tokens • Entity Frequency / Inverted Document Frequency • Entity Type Frequency / Inverted Document Frequency
- 20. Working effectively together Agenda 20 • Context • Problem • Solution • Demo • Future Works
- 21. Working effectively together Agenda 21 • Context • Problem • Solution • Demo • Future Works
- 22. Working effectively together Future Work 22 • Semantic More Like This new approach (Graph relations) • Machine Learning components: Classification, Topic annotation, Clustering • Semantic facets • Secured Entity Search • Image and Media searches
- 23. Working effectively together Conclusions 23 • Better user experience • More precision in search results • Closer to human language
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