Webinar: Metadata Enrichment in Publishing
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Transcript of Webinar: Metadata Enrichment in Publishing
Metadata Enrichment in Publishing: Boosting Productivity and Increasing User Engagement
Ontotext webinar series – 29th of October 201511am ET | 10am CT |8am PT | 1500 UTC
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Agenda
• Company info• Metadata challenges• Ontotext approach to metadata enrichment – Dr. Georgiev• Live demos• Wrap up + QnA
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Company essentials
• One-stop shop for semantic technology− Text Analytics + Content Enrichment + Search + Graph Database Engine− Over 400 person-years in R&D
• Started in year 2000− As R&D lab within Sirma – the biggest Bulgarian software company
• Got spun-off and took VC investment in 2008 • 70 staff, growing revenue, profitable
− HQ in Sofia, Bulgaria, offices in London and NYC
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Company essentials
Why? enable better search, analytics and content delivery
What? data and content management technology
How? semantic analysis of text, NoSQL graph database
Best for: content publishing and information discovery
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Clients (selection)
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Major financial Information agency
Major business and legalInformation agency
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What others are saying about metadata…
• "Metadata Matters" Bloomberg white paper, 2014“Metadata was once purely the domain of the tech team, today it affects everyone in a media and
publishing organizations—from editorial team members to business leaders."
• John O'Donovan, Former CTO of Financial Times"Everyone forgets about metadata. All your assets are useless to you unless you have metadata –
your archive is full of stuff that is of no value because you can’t find it and don’t know what it’s about.”
• A manifesto on metadata, Thad McllroyThe only way to make publishing's great content discoverable, is "via rich metadata linked into smart
search systems."
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Types of metadata
• Structural metadata • Technical metadata• Descriptive metadata• Administrative metadata• Rights metadata• Commercial metadata
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Technical metadata
• Examples of enrichment of technical metadata
“Tagasauris saw the potential for a semantic engine like GraphDB to add value and intelligence to their tagging services.”
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Descriptive metadata
• Examples of enrichment of descriptive metadata
“With semantic metadata you can describe your content in terms of the locations, people, organizations, brands, etc that the content is about.”
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The risk of being negligent to your metadata
“The failure to adequately account for each type of metadata can affect a publishing company’s ability to efficiently create, store, find, access and publish content of all types!”
"Metadata Matters" Bloomberg white paper, 2014
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Why do we need to care more about metadata?
•Мetadata drives content organization, workflow optimization and automation
• It’s economically smarter to tie assets together throughout the supply chain with metadata
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Typical publishing workflow
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Semantic publishing workflow
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Why do we need to care more about metadata?
•A strong metadata system can power centralized searches, helping find assets across data silos
• If an asset can be easily found, it can be reused and repurposed more easily
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Easily discover all your content assets
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Metadata can empower automation
• Many publishers still rely on big teams of editors to manage their digital offerings while at the same time
• A single editor could aggregate all content that relates to particular topic and with appropriate metadata tagging that could happen automatically
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Use case: BBC
• Goals− Create a dynamic semantic publishing
platform that assembles web pages on-the-fly using a variety of data sources
− Deliver highly relevant data to web site visitors with sub-second response
• Challenges− BBC journalists author and publish
content which is then statistically rendered. The costs and time to do this were high
− Diverse content was difficult to navigate, content re-use was not flexible
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"The goal is to be able to more easily and accurately aggregate content, find it and share it across many sources. From these simple relationships and building blocks you can dynamically build up incredibly rich sites and navigation on any platform."
John O’Donovan, Chief Technical Architect, BBC
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Metadata can greatly enhance discoverability
• United metadata across multiple data silos can provide a universal search solution for editors looking for specific content – internal usage and search
• Publishers need to take full advantage of traffic from search to better expose and potentially monetize their content
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Use case: EuroMoney
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• Goals− Create a horizontal platform to
serve 100 different publications
− Platform which would include the latest authoring, storing, and delivery technologies including, semantic annotation, search and a triple store repository
• Challenges− Multiple domains covered
− Sophisticated content analytics including relation, template and scenario extraction
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Metadata can drive user engagement
•Recommendation engines also rely on metadata to suggest content to users
•So, ultimately, the better structured and more accurate the metadata, the more likely recommended content to be highly relevant
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Use case: Financial Times
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• Goals− Create a horizontal platform for both
data and content based on semantics and serve all functionality through this platform
• Challenges− Critical part of FT.COM
− Personalized recommendation based on user behavior and semantic context (Related Reads)
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Ontotext value proposition
• Make Text and Data Tango Together!− Today there is an artificial divide between text and data − Semantic technology removes the divide and brings them together
• We interlink text and data to unveil their meaning− Large knowledge graphs help text-mining!− Interlinking text and data allows us to add context and meaning to both
• We deliver unmatched search and exploration− Across all sorts of data and at a fraction of the cost of alternative approaches
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ONTOTEXT Technology:Analyzing Text
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• Full spectrum of NLP capabilities
• Semantic indexing− Tag references with entity IDs
− Generate semantic metadata descriptions of documents
− Store metadata in GraphDB
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ONTOTEXT Technology:Interlinking Text and Data
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• Use large knowledge graphs for text analysis
• Semantic annotation and search− Combine structured database queries
with full-text search and inference We make sense of text and data by linking and interpreting them together
Methodology
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Methodology
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Methodology
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Example KB for 50 daily publications
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Methodology
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Design of Machine Learning Pipeline
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Continuous Adaptation
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Examples: Semantic Content Enrichment
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Examples: Semantic Content Enrichment (live)
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Examples: Faceted Search-Co-occurrence (live)
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Examples: Key Entities & Trends (live)
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Proven in Publishing and Other Sectors
• Application: Content production and delivery− Helping for: authoring, enrichment, presentation, re-purposing, personalized recommendation
• Application: Information discovery − Powerful semantic enterprise search for applications like regulation compliance and drug safety
• Valuable collection of use cases: 10+ high-profile projects− Business news: FT, Bloomberg, Euromoney
− Scientific publishing: John Willie & Sons, Oxford University Press, IET
− Media & content publishers: BBC, DK, Getty, Disney, ...
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Semantic News Publishing Solution
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• They have tons of great content− That is expensive to manage and reuse
• But struggle to engage readers− Hard to compete with social networks and
other online and mobile channels
• Solved by Ontotext− Dynamic topic aggregation & feed generation
− Personalized recommendations
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Scientific Publishing Solution
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• They have tons of legacy static content− That is expensive to manage and reuse
• And struggle to monetize their content− Hard to compete with platforms
providers and open access resources
• Solved by Ontotext− Smarter search and recommendations− Taxonomy, Thesauri, Vocabulary
enrichment− Dynamic content aggregation
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Personalized Learning Solution
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• They have tons of static content− That is expensive to manage and reuse
• And struggle to engage learners and educators− Hard to compete with the ed-tech companies and free e-
learning resources
• Solved by Ontotext− Mapping of learning resources to curricula
− Dynamic content aggregation
− Personalized learning
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Wrap up
• Unique Technology Portfolio− Top notch RDF graph database and text-mining− One-stop shop for content enrichment and metadata management
• End-to-end solution for Media and Publishing− Authoring, curation and publishing through adaptive text-mining
• Proven to Deliver – we run FT.COM and BBC.CO.UK/SPORT
• Stable, Sustainable and Growing Company
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Thank you!Experience the technology with NOW: Semantic News Portal
http://now.ontotext.com
Try out our Semantic tagging servicehttp://tag.ontotext.com
Learn more at our website or simply get in touch [email protected], @ontotext
Oct 2015