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Semantics as the Basis of Advanced Cognitive Computing


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This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.

To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.

Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.

There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.

A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.

This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.

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Semantics as the Basis of Advanced Cognitive Computing

  1. 1. Andreas Blumauer CEO & Managing Partner Semantic Web Company / PoolParty Semantic Suite Semantics as the Basis of Advanced Cognitive Computing (with a focus on Cognitive Search & Analytics) 1
  2. 2. Introduction 2 Semantic Web Company founder & CEO of Andreas Blumauer developer and vendor of 2004 founded 6.0 current Version active at based on Vienna located part of Enterprise Knowledge Graphs manages standard for part of enriches >200serves customers editor of Taxonomies is about Ontologies standard for graduates
  3. 3. A quick question at the beginning Will Artificial Intelligence make Subject Matter Experts obsolete? 3 Imagine you want to build an application that helps to identify wine and cheese pairings. Which performs best? Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both?
  4. 4. Another question at the beginning Will Artificial Intelligence make Subject Matter Experts obsolete? 4 Imagine you want to build an application that helps to identify patients and treatments pairings. Which will you prefer? Applications solely based on machine learning, those ones which are based on doctors' knowledge only, or a combination of both?
  5. 5. A key assumption of this talk Effectiveness of cognitive systems is limited by the machines’ current inability to explain their decisions and actions to human users. 5 From: David Gunning Explainable Artificial Intelligence (XAI)
  6. 6. Another key assumption of this talk People do not search for documents only, they seek facts about things and smaller chunks of information. Machines shall help to create links across data silos to give answers to questions. 6 Converging AI Technologies
  7. 7. What makes a system a cognitive system? ▸ Adaptive. Cognitive systems learn through their interactions with data and humans. They may resolve ambiguity and tolerate unpredictability. ▸ Contextual. Cognitive systems are capable of extracting relevant information from big and diverse data sets for users in their (work) context. ▸ Iterative. Systems may interact easily with users so they can define their needs comfortably, even if a problem statement is ambiguous or incomplete. They may also interact with other processors, devices, and cloud services, as well as with people. See also: 7
  8. 8. How Semantic Computing and Machine Learning complement each other 8 Structured Data Machine Learning Cognitive Applications
  9. 9. How Semantic Computing and Machine Learning complement each other 9 Unstructured Data Structured Data Machine Learning Cognitive Applications
  10. 10. How Semantic Computing and Machine Learning complement each other 10 Unstructured Data Structured Data Knowledge Graphs Machine Learning Cognitive Applications
  11. 11. Four-layered Information Architecture 11
  12. 12. Towards a Digital Twin Proposal for a Cognitive Computing Platform Architecture 12 Unstructured Data Structured Data Knowledge Graphs Machine Learning Semantic Layer IoT & Cognitive Applications
  13. 13. Digital Twin A digital twin continuously learns and updates itself from multiple sources to represent their near real-time status, working condition or position. This learning system learns ▸ from itself using sensor data that conveys various aspects of its operating condition ▸ from human experts, such as engineers with deep and relevant industry domain knowledge ▸ from other similar machines, and ▸ from the larger systems and environment in which it may be a part of. From: Wikipedia 13
  14. 14. Use Case #1 The Wine & Cheese Recommender 14
  15. 15. How to overcome the knowledge acquisition bottleneck? 15 Knowledge Domain Knowledge Modellers Knowledge Model semantic gap Domain Experts
  16. 16. The idea Build a Graph-based Recommender Systems and use Semantic Web Standards 16 Dry Medium-bodied High acidity Weingut Weinrieder Grüner Veltliner Alte Reben is characterized by Nutmeg Full-bodied Warm finish Tobacco is characterized by Nagelkaas Cumin Clove Hard cheese Higher fat ? is characterized by matches matches does not match
  17. 17. The result A scalable and configurable application based on an enterprise semantic platform: PoolParty GraphSearch 17
  18. 18. Use Case #2 Make use of Shadow Concepts 18
  19. 19. Bionics How does nature go around similar learning bottlenecks? 19 Bla bla bla bla. Bla bla bla bla The stove is on. The stove is hot! Ontological model → reasoningTaxonomical model → is-a abstractions Bla stove bla bla. Bla bla bla hot Switched on devices are dangerous devices. Switched on devices are dangerous, only if the operating temperature is above 100 degrees and the automatic shutdown mechanism is broken. The stove is on. The stove is hot! Statistical model/cooccurences → is related The stove is on. The stove is hot! Bla bla bla bla Bla bla bla bla.
  20. 20. Graphs + Machine Learning PoolParty as a supervised learning system 20 Content Manager Integrator Taxonomist/ Ontologist Thesaurus Server Extractor PowerTagging uses API is user of is user of is basis of is basis of Index annotates enriches Corpus Learning/ Semantic Analysis CMS extends is basis of analyzes uses API proposes extensions
  21. 21. Co-occurence model 21 Reference Corpus - Websites - PDF, Word, … - Abstracts from DBpedia - RSS Feeds Term 8 Term 3 Term 7 Term 8 Term 6 Term 9 Term 5 Term 10 - Relevant terms and phrases - Relevancy of terms - co-occurence between terms and terms Term 1 Term 4 Term 2
  22. 22. Shadow Concepts Use co-occurences between concepts and terms to extract ‘shadow concepts’ 22 This site is a 15th-century Inca site located 2,430 metres above sea level. It is located in Cusco, Peru. It is situated on a mountain ridge above the Sacred Valley through which the Urubamba River flows. Most archaeologists believe that it was built as an estate for the Inca emperor Pachacuti. Often mistakenly referred to as the "Lost City of the Incas", it is the most familiar icon of Inca civilization. The Incas built the estate around 1450, but abandoned it a century later at the time of the Spanish Conquest. Inca site Machu Picchu Cusco Inca empire Inca emperor Peru Spanish Conquest Sacred Valley Chankas Lost City Pachacuti In addition to explicitly used concepts and terms, Machu Picchu is extracted from the article as a Shadow Concept. As a prerequisite, one has to provide and analyze a representative text corpus first. Example:
  23. 23. Use Shadow Concepts to improve Recommender Systems 23 Mini Countryman And it’s probably more of a crossover than ever, with the design to match, Being a Mini, the Countryman is clearly meant to be the driver’s car among small crossovers. The suspension is sophisticated, and there are lots of chassis options (a stiffer sports setup, variable damping, the electronically controlled ALL4 all-wheel-drive). But it’s also the crossover for people who’ve bags of cash to blow on personalisation and luxury. There’s been a lot of effort on ramping up the cabin quality, but then the outgoing Countryman was a sad let-down in that department. On the outside, plastic wheel-arch extensions, with eyebrow creases in the metalwork above, as well as roof bars and sill protectors all add to the visual crossover-ness. This remains the only Mini with angular rather than oval headlamps, and there’s a load of visual posturing going on in the lower face. There are eight versions at launch, and they’re exactly what you’d expect. It’s Cooper or Cooper S, each fuelled by petrol or diesel, each of them with front drive or ALL4. Oh and an eight-speed auto, too, if you count that as a separate choice. The Cooper petrol is a three-cylinder, the rest fours. You get extra kit as standard versus the old car, including navigation, Bluetooth, emergency call and park sensors. Upgrades include a bigger touch-screen nav with high-definition traffic, various posher seats, a HUD, and driver aids. Oh and a cushion thingy that folds out from the boot so you can sit on the rear bumper without getting your clothes mucky. In June 2017 a Cooper E will launch, which has the Cooper three-cylinder petrol driving the front wheels, and an electric motor for the rears, with a capacity to do a claimed 25 miles of gentle all-electric running. So it has the performance of a Cooper S ALL4 with the tax-busting advantages of a plug-in hybrid. And you wouldn’t use any fuel if you commuted a short distance. The platform is BMW’s contemporary transverse-engined hardware, in the bigger of its two sizes. That means it shares a lot with the BMW X1. The 4WD system is more sophisticated than the previous Countryman’s. The proportion of drive to the rear is computed by a controller that takes into account parameters including grip, steering angle and throttle position, as well as whether you’ve got the sports mode and sports traction systems selected.
  24. 24. Use Case #3 Improving Classification Algorithms 24
  25. 25. Use Semantic Knowledge Models to improve Document Classifiers 25 Prof. Farhad Ameri Engineering Informatics works at researches We observed 10-20% improvement in precision of the classification process as a result of using a semantic thesaurus. Classification Algorithms improves uses Supplier classification framework develops A Thesaurus-guided Text Analytics Technique for Capability-based Classification of Manufacturing Suppliers CIE/SEIKM Best Paper 2017 publishes
  26. 26. Infoneer’s Manufacturer Classification Framework (MCF) 26
  27. 27. Use Case #4 A Perfect Wedding based on AI 27
  28. 28. How knowledge acquisition methods play together? 28 Natural languages Taxonomies Schemas/Ontologies Statisticalmodels Computational Linguists Taxonomists DataScientists Ontologists
  29. 29. Why ‘The Knot’ uses Machine Learning 29 ▸ Vendor similarity ▸ Vendor matching ▸ Image similarity ▸ Reverse image search ▸ Image tag generator (auto-classification) ▸ Recommendations ▸ Card sort user response analysis ▸ Style predictor ▸ XO Group runs ‘The Knot’ since 1996 ▸ NYSE: XOXO (S&P 600 Component) ▸ 1.5 million active members ▸ The Knot has helped marry 25 million couples ▸ Partnering with 250,000 wedding vendors ▸ Millions of vendor reviews
  30. 30. The Learning Curve 30
  31. 31. ▸ To understand ▹ Content aboutness in a defined framework ▹ Data relationships and context within a unified organizational model ▹ Connections across disparate datasets ▸ To increase precision ▹ Hierarchical or other mapped relationships allow for recommending similar content when exact matches not found ▹ Granularity allows for more specific recommendations ▹ Consistency across structure results more precise analysis and predictions Source: Suzanne Carroll, Data Science Product Director at XO Group Why Data Scientists need Semantic Models 31
  32. 32. Thank you for your interest! Andreas Blumauer CEO, Semantic Web Company ▸ Mail ▸ Company ▸ LinkedIn ▸ Twitter ▸ Blog author/andreasblumauer 32 © Semantic Web Company - and