This document discusses an enterprise content management solution called Sensefy that provides semantic search capabilities across heterogeneous data sources. It semantically enhances unstructured content using named entity recognition and linking to external knowledge bases. Sensefy uses the Media In Context (MICO) platform for cross-media analysis and metadata extraction from multimedia files. The system allows for federated search across different repositories as well as entity-driven search with disambiguation and suggestion capabilities. A demo is provided to showcase these semantic search features.

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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.