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Smartlogic, Semaphore and
Semantically Enhanced Search
– For “Discovery”
Zoeken & Vinden
3rd March 2016, Amsterdam
Paul Gu...
The Company & The Opportunity
We believe there is huge business value locked away in content, because content
contains the...
Some of Smartlogic’s customers
Search “Maturity” - why am I searching?
Search Volume
Search Value
Document Search Subject Search
“Euraka” Search
€0
€1M?
...
• Digital universe is growing
dramatically
• Most of this information is
unstructured
• Only a small fraction of the
digit...
The Challenges …. Part 2
We don’t all speak the same language ……
The Challenges …. Part 3
The proliferation of systems within and beyond the “Hyper-Connected Enterprise”
is creating a HUG...
Contextual metadata driven experience
User interfaces to leverage the ontologies to deliver the
richest experience for use...
Conceptual architecture
SharePoint 2013 integration
SharePoint Online integration
Solr native integration
MarkLogic integration
Generic CMS integration
A presenter’s nightmare …
I will now demonstrating 3 components to show how
each plays a part in making content discoverab...
Search and Publishing Enhancement
Executing a SOLR Search
Executing a Solr Search
The screen-shot
shows the
Semaphore SAF
(Search Application
Framework) fro...
Standard Search ResultsStandard SOLR Search
Results
You can see there are
over 2000 results.
The standard SOLR
method for ...
More sophisticated searching
still doesn’t get better results
A more
knowledgeable user
might search for the
phrase “moon ...
Standard Search ResultsOntology Driven Widgets
provide “Did You Mean?”
Each set of results
includes some
suggested terms,
...
Model Assisted Search ResultSearch Results enhanced by
Semantic Model
In this case, the user has
selected the preferred te...
Search refiners augment the
Semantic Model
The search results
page includes
refiners, populated
from document
metadata whi...
Auto Categorisation (or Classification)
Document for Categorisation
This slide shows how you
can apply the Semantic
Model to documents (in this
case a transcripti...
Entity Extraction
In this example a
document (taken from
Wikipedia) is not only
being categorised for
Subject (in this cas...
Fact Extraction
In this example
Semaphore is being
used to process legal
documents to
automatically extract
key pieces of
...
Model Creation and Management
(Taxonomies/Ontologies)
Model; High Level Concepts
Browsing the Semantic Model
Semaphore provides
a collaborative
environment for
managing semanti...
Concept Relationships (Collaboration Tool)
Term information
The Semaphore
workbench shows
how each term fits
into the mode...
Obtaining feedback
The Semaphore
Workbench also
allows collaboration:
subject matter experts
can contribute to the
quality...
The Value of a Semantic Solution
Our clients describe the value they derive in a number of ways, here are just three:
Cost...
SMARTLOGIC – EMEA & APAC
200 ALDERSGATE
LONDON, EC1A 4HD
TEL: +44 (0)203 176 4500
FAX: +44 (0)207 785 7014
SMARTLOGIC – AM...
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Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery”

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Lezing door Paul Gunstone, VOGIN-IP-lezing 2016, 3 maart 2016, Amsterdam

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Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery”

  1. 1. Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery” Zoeken & Vinden 3rd March 2016, Amsterdam Paul Gunstone, Sales Director
  2. 2. The Company & The Opportunity We believe there is huge business value locked away in content, because content contains the majority of the organization's intelligence. Organizations that unlock that value can outperform competitors in their market. Content augments the value that most organizations have already found in structured data - it is the untapped frontier of competitive advantage. To realize this value we know you must understand your content, the information and knowledge it contains, how it can be applied in the context of your operations and how it enhances the insights from structured data. The content must be described completely and consistently with metadata. We focus all our energy on creating value from unstructured content – something we call Content Intelligence Jeremy Bentley, CEO “
  3. 3. Some of Smartlogic’s customers
  4. 4. Search “Maturity” - why am I searching? Search Volume Search Value Document Search Subject Search “Euraka” Search €0 €1M? SEARCH REFINERS ENTITIES CLUSTERING RELATIONSHIPS FACT EXTRACTION
  5. 5. • Digital universe is growing dramatically • Most of this information is unstructured • Only a small fraction of the digital universe has been explored for analytical value • Valuable knowledge and relationships are hidden in this data The Challenges … Part 1 Relentless growth in content volumes ……
  6. 6. The Challenges …. Part 2 We don’t all speak the same language ……
  7. 7. The Challenges …. Part 3 The proliferation of systems within and beyond the “Hyper-Connected Enterprise” is creating a HUGE range of sources of ‘content’: • File shares • DMS • ECMS • ERP • HR (HCM) • Finance & Legal • Email • Knowledge Base • CRM • SFA • Twitter (etc.) • LIMS (eg) • Call Centre Logs Board & Meeting Minutes, Engineers’ Reports, H&S Audits (and Actions), Annual Appraisals, Processes & Procedures, Newsletters, Marketing & Product Materials, Maintenance & Repair Manuals, Contracts, Letters of Credit, Insurance Policies, Supply Chain Information, Strategy Documents, Business Plans, Annual Report, Regulatory Submissions, Management Reports, Performance Management Documents, Grievance Procedure Evidence ……..
  8. 8. Contextual metadata driven experience User interfaces to leverage the ontologies to deliver the richest experience for users when publishing, using and analyzing content Semaphore delivers these capabilities – enterprise scale Build and manage semantic models Simplify the ingestion, development or customization of ontologies Assisted and automated metadata enrichment Automatically describe all your content with rich metadata What is Semaphore?
  9. 9. Conceptual architecture
  10. 10. SharePoint 2013 integration
  11. 11. SharePoint Online integration
  12. 12. Solr native integration
  13. 13. MarkLogic integration
  14. 14. Generic CMS integration
  15. 15. A presenter’s nightmare … I will now demonstrating 3 components to show how each plays a part in making content discoverable.
  16. 16. Search and Publishing Enhancement
  17. 17. Executing a SOLR Search Executing a Solr Search The screen-shot shows the Semaphore SAF (Search Application Framework) front-end where the user is wanting to search NASA content for information on the “moon buggy”. The search box is prompting with suggestions from the model, but we’re going to ignore these to illustrate the benefits of using semaphore to enhance solr search
  18. 18. Standard Search ResultsStandard SOLR Search Results You can see there are over 2000 results. The standard SOLR method for joining two words is to use an ‘OR’; as a result you get the majority of results that mention “Moon” but are not about the “Moon Buggy”.
  19. 19. More sophisticated searching still doesn’t get better results A more knowledgeable user might search for the phrase “moon buggy” which should potentially return more relevant results, but may not return ALL the relevant results as there may be other ways to describe this item.
  20. 20. Standard Search ResultsOntology Driven Widgets provide “Did You Mean?” Each set of results includes some suggested terms, extracted from the Semantic Model using a process called “Concept Mapping”. The most common use for this is to provide a “Did You Mean” panel. The user can hover- over terms and see information such as a description and images surfaced from the Model. In this case, the user has selected the preferred-term of “Lunar Roving Vehicle” as the picture matches what they call the “Moon Buggy”.
  21. 21. Model Assisted Search ResultSearch Results enhanced by Semantic Model In this case, the user has selected the preferred term “Lunar Roving Vehicle” (either when prompted in the search box or via the “Did you mean” panel). The search engine is now returning the 59 results that were categorised as being relevant to the Lunar Roving Vehicle, using the rules built automatically from the Semantic Model, using as evidence the term, its acronyms (‘LRV’), its synonyms (such as ‘Moon Buggy’) and the context of the related missions (Apollos 15, 17 and 17). Results returned in this type of search will be more relevant, as the match is determined by a linguistic analysis of the content – not by a search algorithm.
  22. 22. Search refiners augment the Semantic Model The search results page includes refiners, populated from document metadata which can be obtained from the document itself, or by classification against the Semantic Model. These refiners can be used to supplement the Semantic Model, for example you could use an author refiner to identify experts on the subject that you are researching.
  23. 23. Auto Categorisation (or Classification)
  24. 24. Document for Categorisation This slide shows how you can apply the Semantic Model to documents (in this case a transcription of an Apollo crew de-brief) to automatically identify the areas of the model that are discussed in this document. These items are stored as various items of metadata, in this case when the document is uploaded to SharePoint, although Semaphore integrates with many other systems. Semaphore has also identified the type of document, and this can be used to drive additional workflow such as compliance etc. Lastly, the Model is interactive – document authors can browse the model for relevant terms, or use search-as-you-type.
  25. 25. Entity Extraction In this example a document (taken from Wikipedia) is not only being categorised for Subject (in this case topics from a civilian government taxonomy) but Semaphore is also extracting Organisations and People found in the document using Natural Language Processing, names that can be included as Metadata even though they aren’t part of the Semantic Model.
  26. 26. Fact Extraction In this example Semaphore is being used to process legal documents to automatically extract key pieces of information such as Party names, amounts, terms and conditions etc. Where these items can be extracted explicitly they can be stored as metadata properties; where they cannot be extracted explicitly, the clauses referring to these items can be stored for manual processing.
  27. 27. Model Creation and Management (Taxonomies/Ontologies)
  28. 28. Model; High Level Concepts Browsing the Semantic Model Semaphore provides a collaborative environment for managing semantic models, capitalising on subject matter experts within an organisation. This illustration shows the Semaphore Workbench being used to browse the NASA Model, the user can select to browse by top-level category, or can type a search, which will be matched to terms in the model.
  29. 29. Concept Relationships (Collaboration Tool) Term information The Semaphore workbench shows how each term fits into the model, including related terms, synonyms and term properties. All this information can be used in document categorization and in search enhancement as illustrated in this presentation.
  30. 30. Obtaining feedback The Semaphore Workbench also allows collaboration: subject matter experts can contribute to the quality of the Semantic Model by suggesting additional terms, synonyms and related terms.
  31. 31. The Value of a Semantic Solution Our clients describe the value they derive in a number of ways, here are just three: Cost Efficiency: One organisation, which has a very engineering/scientific workforce, indicates that it saves the equivalent of cUS$700 per employee per year due to the reduction in time taken to find the right content from across many content repositories. ($700/$45 (hourly salary) = 15.5 hours/year saved = 19 minutes/week saved). With over 10,000 employees the equivalent savings are huge. Cost Savings: Another organisation calculated the cost of classifying documents manually at US$3 per document (based on staff costs, office space, etc). With over 500,000 documents needing to be classified the Return on Investment was 10 fold – and would continue to increase as more documents are produced. They also cited the quality and consistency of auto-classification to be significantly better than human- classified content Risk Reduction: Financial Services companies that cannot prove compliance to a host of regulations are being fined millions of Euros/Pounds/Dollars. One reason they cannot prove compliance is that the evidence they need is lost or locked away in textual content, in a file-share or in a Content Repository, poorly classified. Our semantic solution makes the evidence readily available and provides consistency over time. Looking for the same evidence at a later date will still deliver the same results.
  32. 32. SMARTLOGIC – EMEA & APAC 200 ALDERSGATE LONDON, EC1A 4HD TEL: +44 (0)203 176 4500 FAX: +44 (0)207 785 7014 SMARTLOGIC – AMERICAS 111 N MARKET ST. SAN JOSE, CALIFORNIA, 95113 TEL: 408 213 9500 FAX: 408 572 5601 WWW.SMARTLOGIC.COM INFO@SMARTLOGIC.COM © 2016 SMARTLOGIC INC Questions … And Thank You! Paul Gunstone Sales Director paul.gunstone@smartlogic.com +44 7739 310343

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