Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery”
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Smartlogic provides semantic search and content intelligence solutions to unlock business value from unstructured content. Their software, Semaphore, uses natural language processing and machine learning to build ontologies and automatically annotate content with metadata, enabling more sophisticated search and discovery of hidden knowledge within large volumes of documents. Semaphore integrates with various systems and delivers benefits such as cost savings from more efficient content exploration, risk reduction through improved compliance, and competitive advantages from making better use of organizational intelligence in content.
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Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery”
- 1. Smartlogic, Semaphore and Semantically Enhanced Search – For “Discovery” Zoeken & Vinden 3rd March 2016, Amsterdam Paul Gunstone, Sales Director
- 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. Some of Smartlogic’s customers
- 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. • 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. The Challenges …. Part 2 We don’t all speak the same language ……
- 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. 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?
- 15. A presenter’s nightmare … I will now demonstrating 3 components to show how each plays a part in making content discoverable.
- 16. Search and Publishing Enhancement
- 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. 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. 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. 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. 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. 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. Auto Categorisation (or Classification)
- 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. 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. 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. Model Creation and Management (Taxonomies/Ontologies)
- 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. 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. 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. 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. 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
Editor's Notes
- Thank you … and I am delighted to be here and have the opportunity to talk about – and show – how the “new-ish” world of semantic technologies, combined with Information Science, can aid “Discovery”. I am humbled to be on a mainland European country and be delivering a presentation in my native tongue. However, of course, as an Englishman in Holland, it is impossible to converse in any other language – you are all too good! As I am speaking in English I do sometimes go a little too fast (as I get excited). If I do, please shout, wave or throw things and I will attempt to slow down. I am Paul Gunstone, the EMEA/APAC Sales Director for Smartlogic
- Smartlogic has been in this world of semantic technologies since 2006. We set out then to help organisations benefit from the business value locked away in their unstructured and semi-structured content. That is what we still do today, it is all we do. In order to be able to realise the business value in ‘content’ you have to know that the content is, what it is about, what entities and facts it contains. Our platform (Semaphore) uses a combination of a semantic domain model and NLP to read documents and surface the “aboutness”, the entities and the facts as a rich layer of metadata or as RDF triples. We don’t keep copies of any of the content, there is no duplication or ETL processes, we simply process it as it is being ingested into some form of content store or is being re-indexed.
- This is the last “marketing” slide but it illustrates that the company, whilst a UK company, has a global footprint and that the platform scales. It is worth noting that this slide shows use cases in Financial Services, Government or Public Sector, Media, Life Sciences, more general manufacturing and Retail, Professional Services, Heavy Engineering. Any industry, any organization that has large volumes of “content” will benefit from understanding what that content is and what information/data it contains. We call that Content Intelligence.
- I just wanted to spend a moment or two understand why people search for things and the value those searches deliver. You can see at the bottom left of the curve individuals are performing Document Searches … “I need the latest version of the holiday application form” This has been by far the greatest volume of searches conducted … but is being caught by Subject Searches …. “I want to understand more about competitor X, give me all of the documents we have.” So not just the latest competitor analysis document but perhaps news or a report on the CEO or pricing information or last deals won or lost against them. This clearly is likely to represent greater value to the enterprise if the search is fulfilled effectively Finally, a very very small part (at the moment) largely because it has been too difficult but Eureka Search … “I don’t know what I don’t know, I don’t know what I’m looking for, but show me entities in the content we have, show me relationships between those entities and let’s see if anything interesting emerges” This was the world of advanced analytics, OLAPs and cubes, where you had to define the fields you want to examine ahead of time and if you wanted to add or change a field you had to re-define the cube. With RDF Triples, Graph Databases (or Triple Stores) this is crashing into the world of Search.
- Why is it so difficult then? Of the many reasons I have picked three First is the inexorable growth in content (largely unstructured content). This will contain some very interesting “stuff”. It will also contain a vast amount of garbage … but unless you read it all, you won’t know what it does contain and you won’t be able to use the interesting “stuff”
- Second is that we don’t all talk about things in the same way. In this example – from the NASA domain model you can see that whilst I might talk about the Moon Buggy (and others might talk of the Geological Rover), the official NASA term is Lunar Roving Vehicle. Now this one is quite easy because it is famous and public but what about the issues in an Enterprise. I was working with a large pharma only a few years ago that didn’t have a consistent term for each Country in which they operated. Fortunately they have spent many many millions on three MDM programmes in the years since then …. And they still don’t have a consistent term for each Country in which they operate. But three MDM vendors are happy!
- My final example is just a reflection of the corporate world today where content is scattered across the inside of organisations in different systems, structured and organised in different ways. As organisations become part of bigger ecosystems the problem is compounded by the different ways that other companies do things. How do you refer to the components of a “switch rail” – in manufacturing, in engineering, in marketing, in maintenance. How do the third parties you have to use occasionally refer to them. How do you reflect changes in all of the documentation when it is “owned” by so many different people in different departments. How do you use the information you learned 5 years ago (and locked away in a closed project) in a problem that has occurred today. Food for thought …. So how does this Semantic thing help??
- Our Semantic platform helps by reading the content and surfacing that rich layer of metadata that I spoke of earlier. At the heart of it is a domain model (something that describes your universe
- My final example is just a reflection of the corporate world today where content is scattered across the inside of organisations in different systems, structured and organised in different ways. As organisations become part of bigger ecosystems the problem is compounded by the different ways that other companies do things. How do you refer to the components of a “switch rail” – in manufacturing, in engineering, in marketing, in maintenance. How do the third parties you have to use occasionally refer to them. How do you reflect changes in all of the documentation when it is “owned” by so many different people in different departments. How do you use the information you learned 5 years ago (and locked away in a closed project) in a problem that has occurred today. Food for thought …. So how does this Semantic thing help??