Ontology-Driven Automatic Entity Disambiguation in Unstructured Text Jed Hassell.
Chalitha Perera | Cross Media Concept and Entity Driven Search for Enterprise
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Transcript of Chalitha Perera | Cross Media Concept and Entity Driven Search for Enterprise
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Cross Media Concept and Entity Driven Search for Enterprise
Chalitha Perera and Dileepa Jayakody R&D Engineers
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• Headquartered in London with office in Colombo, Sri Lanka
• Focused on delivering enterprise content management solutions
• Our Skills
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Zaizi R&D Department
• 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
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Agenda
• Problem
• Solution
• Sensefy and MICO
• Demo
• Q&A
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Problem • Unstructured Text Content
– Text documents, PDFs, Word …
• Rapid growth in multimedia content
• Heterogeneous Data Sources
– ECMs (Alfresco, Sharepoint), File System, Confluence, JIRA …
• Data is not useful without effective methods for – Knowledge Extraction – Information Retrieval
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Current Enterprise Search Limitations
• Limited to keyword based search
• Search context is not considered
• Ambiguity of terms
• Low precision
• Inability to properly handle multimedia files
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Desired traits of Solution
• Semantically Enhance documents – Unstructured text – Multimedia documents
• Cross media search
• Search with semantic concepts and entities
• Federated Search
– Search across different content repositories – User permissions
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Sensefy • Semantic Enterprise Search Engine
• Cross Media Search
• Federated Search
• Smart Search Assistance
• Open Source
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Sensefy Architecture
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Repository Crawler • Four types of connectors
– Repository Connectors – Authority Connectors – Transformation Connectors – Output Connectors
• Connect different source repositories with different target indexes – Source repositories (Alfresco, Sharepoint, Confluence etc) – Target Indexes (Solr, ElasticSearch, Amazon CloudSearch)
• Security Model to enforce source repository security policies
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Media In Context (MICO) Platform
• MICO provides an integrated platform for – Cross media analysis – Metadata publishing – Metadata querying
• Sensefy uses MICO as the cross media analysis engine to extract entities and concepts
from multimedia
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Cross Media Extraction Pipeline
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Semantic Content Enrichment
• Named Entity Recognition – People, places, organizations and concepts
• Entity Linking – DBpedia, Yago, Custom Enterprise knowledge bases
• Entity Disambiguation
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Entity Search with Suggestions
• Named Entity Suggestions • Ability to query with disambiguated entities
• Search results with high precision – Keyword search results for “ronaldo” - “Cristiano Ronaldo” and “Ronaldo” – Entity Search - will contain only the documents related to selected entity
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Entity Search with Suggestions
• Combine entities and concepts for more complex queries
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DEMO
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Q&A
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Thank you.