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Semantic AI

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Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.

Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.

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Semantic AI

  1. 1. Andreas Blumauer CEO & Managing Partner Semantic Web Company / PoolParty Semantic Suite Semantic AI Bringing Machine Learning, NLP and Knowledge Graphs together
  2. 2. Introduction Semantic Web Company (SWC) ▸ Founded in 2004, based in Vienna ▸ Privately held ▸ 50 FTE ▸ Software Engineers & Consultants for NLP, Semantics and Machine learning ▸ Developer & Vendor of PoolParty Semantic Suite ▸ Participating in projects with €2.5 million funding for R&D ▸ ~30% revenue growth/year ▸ SWC named to KMWorld’s ‘100 Companies That Matter in Knowledge Management’ in 2016, 2017 and 2018 ▸ www.semantic-web.com 2 PoolParty Semantic Suite ▸ Most complete Semantic Middleware on the Global Market ▸ Semantic AI: Fusion of Knowledge Graphs, NLP, and Machine Learning ▸ W3C standards compliant ▸ First release in 2009 ▸ Current version 7.0 ▸ On-premises or cloud-based ▸ Over 200 installations world-wide ▸ Named as Sample Vendor in Gartner’s Hype Cycle for AI 2018 ▸ KMWorld listed PoolParty as Trend-Setting Product 2015, 2016, 2017, and 2018 ▸ www.poolparty.biz
  3. 3. Agenda 3 Semantic AI ▸ Introduction to Semantic AI ▹ Current status of Artificial Intelligence ▹ Machine Learning, NLP and Knowledge Graphs coming together ▹ Six core aspects of Semantic AI ▸ Use Cases
  4. 4. SEMANTIC ARTIFICIAL INTELLIGENCE #SemanticAI: Bringing Machine Learning, NLP and Knowledge Graphs together 4
  5. 5. Predictions and ground truths about AI Herbert A. Simon (1965) ▸ AI pioneer Herbert A. Simon (Carnegie Mellon University) had predicted in 1965 that "machines will be capable, within twenty years, of doing any work a man can do" Doug Lenat (1998) ▸ “Those of us who lack shared knowledge and experience often don't understand much of what they say to each other. In the same way, today's computers, which don't share even the common-sense knowledge we all draw on in our everyday speech and writing, can't comprehend most of our speech or texts.” 5 HAL 9000 (1968) KITT (1982) Siri (2011)
  6. 6. AI suffers from a lack of common sense, but ... 6 Google Assistant (2018)
  7. 7. … is talented in solving isolated problems based on isolated data sets 7 Monte Carlo TS Deep Learning Deep Learning Genetic Algorithms Neuronal networks Case based reasoning Face recognition Game AI Fraud detection
  8. 8. Gartner Hype Cycle for Artificial Intelligence, 2018 “The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.” 8
  9. 9. What makes someone an intelligent being? Assessment of the current status of Artificial Intelligence Level Example Questions (6) Create How to convert an inefficient AI system architecture to a more efficient one by replacing your choice of components? How would you improve …? Can you formulate a theory for …? Can you predict the outcome if …? (5) Evaluate Which kinds of knowledge models are best for machine learning, and why? What is your opinion of …? How would you prioritize …? What would you use to support the view …? (4) Analyse How does a graph database and a semantic knowledge model work together? How is ... related to ...? What is the function of ...? What conclusions can you draw ...? (3) Apply How can taxonomies be used to enhance machine learning? Why is … significant? How is … an example of …? What elements would you use to change …? (2) Understand What is the difference between an ontology and a taxonomy? What is the difference between …? What is the main idea of …? Which statements support …? (1) Remember Who is the inventor of the World Wide Web? Who is …? Where is …? Why did …? 9 Bloom’s Taxonomy: Classify cognitive processes
  10. 10. Remember 10 Perth Australia Perth is one of the most isolated major cities in the world, with a population of 2,022,044 living in Greater Perth. Australia is a member of the OECD, United Nations, G20, ANZUS, and the World Trade Organisation. Country City is a is a is located in Avoid illogical answers:distance between Commonwealth of Nations International Organisation is part of is a Support complex Q&A: Which cities located in the Commonwealth of Nations have a population of more than 2 mio. people? “Knowledge graphs silently accrue ‘smart data’ — i.e., data that can be easily read and ‘understood’ by AI systems.” Gartner Hype Cycle for Artificial Intelligence, 2018
  11. 11. Remember Knowledge Graphs & Knowledge Extraction Knowledge Graphs (KG) can cover general knowledge (often also called cross-domain or encyclopedic knowledge), or provide knowledge about special domains such as biomedicine. In most cases KGs are based on Semantic Web standards, and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work. Examples: ▸ DBpedia ▸ Google Knowledge Graph ▸ YAGO ▸ OpenCyc ▸ Wikidata 11 Who is the inventor of the World Wide Web?
  12. 12. “Understand” Google Featured Snippets based on Sentence Compression Algorithms To train Google’s artificial Q&A brain, the company uses old news stories, where machines start to see how headlines serve as short summaries of the longer articles that follow. But for now, the company still needs its team of PhD linguists. Spanning about 100 PhD linguists across the globe, the Pygmalion team produces “the gold data,” while the news stories are the “silver.” The silver data is still useful, because there’s so much of it. But the gold data is essential. WIRED article 12 What is the difference between an ontology and a taxonomy?
  13. 13. “Create” Example for DL-based “creativity” Aiva’s compositions still require human input with regards to orchestration and musical production. In fact, Aiva’s creators envisage a future where man and machine will collaborate to fulfill their creative potential, rather than replace one another. http://www.aiva.ai/ 13 After having listened to a large amount of music and learned its own models of music theory, Aiva composes its very own sheet music. These partitions are then played by professional artists on real instruments in a recording studio, achieving the best sound quality possible.
  14. 14. AI Methods - An overview 14 Artificial Intelligence (AI) Artificial Neural Network (ANN) Symbolic AI (GOFAI*) Sub-Symbolic AI Statistical AI Knowledge graphs & reasoning Natural Language Processing (NLP) Machine Learning * Good old-fashioned AI Word Embedding (Word2Vec) Deep Learning (DNN) Natural Language Understanding Entity Recognition & Linking Knowledge Extraction Semantic enhanced Text Classification
  15. 15. Observations from the AI Market Lack of AI Governance ▸ Companies have concerns about validity, explainability and unintended bias of AI. Lack of AI Strategy ▸ Many organizations are currently undertaking POCs from a large pool of AI vendors only for tactical benefits. Low Data Quality & Data Silos ▸ 80% of the work of data scientists is acquiring and preparing data. A demon that can drive up that 80% and often makes initiatives impossible are data silos. Danger of Vendor Lock-in ▸ Use of black-boxes instead of hybrid middleware approaches connecting internal training assets to third-party machine-learning solutions. Lack of Knowledge ▸ Only 1 in 10 enterprises feel they have a competent approach to mining data, which ultimately hampers AI efforts. ▸ A shortage of AI skills and risk managers' lack of familiarity with the technology increase the risk. ▸ Gartner (March 2018): “Clarify Strategy and Tactics for Artificial Intelligence by Separating Training and Machine Learning” by Anthony Mullen, Magnus Revang, Erick Brethenoux ▸ Harvard Business Review (2016): “Breaking Down Data Silos” by Edd Wilder-James ▸ Gartner (April 2018): “CIOs Can Manage the Risks of AI Investments” by Jorge Lopez, Paul E. Proctor 15
  16. 16. Six Core Aspects of Semantic AI > Read more 16 1. Data Quality Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. 2. Data as a Service Linked data based on W3C Standards can serve as an enterprise-wide data platform and helps to provide training data for machine learning in a more cost-efficient way. 3. No black-box Semantic AI ultimately leads to AI governance that works on three layers: technically, ethically, and on the legal layer. 4. Hybrid approach Semantic AI is the combination of methods derived from symbolic AI and statistical AI. It is not only focused on process automation, but also on intelligence augmentation. 5. Structured data meets text Most machine learning algorithms work well either with text or with structured data. Semantic AI is based on entity-centric data models. 6. Towards self optimizing machines ML can help to extend knowledge graphs, and in return, knowledge graphs can help to improve ML algorithms.
  17. 17. Data Quality Training data is semantically enriched with help from semantic knowledge models 17 PoolParty Semantic Classifier combines machine learning algorithms (SVM, Deep Learning, Naive Bayes, etc.) with Semantic Knowledge Graphs.
  18. 18. Benchmarking the PoolParty Semantic Classifier Improvement of 5.2% compared to traditional (term-based) SVM 18 Features used Classifier F1 (5-fold) Variance Terms LinearSVC 0.83175 0.0008 Concepts from REEGLE + Shadow Concepts LinearSVC 0.84451 0.0011 Concepts from REEGLE LinearSVC 0.84647 0.0009 Terms + Concepts from REEGLE + Shadow Concepts LinearSVC 0.87474 0.0009 Reegle thesaurus A comprehensive SKOS taxonomy for the clean energy sector (http://data.reeep.org/thesaurus/guide) ● 3,420 concepts ● 7,280 labels (English version) ● 9,183 relations (broader/narrower + related) Document Training Set 1,800 documents in 7 classes Renewable Energy, District Heating Systems, Cogeneration, Energy Efficiency, Energy (general), Climate Protection, Rural Electrification
  19. 19. Data as a Service Proposal for a Cognitive Computing Platform Architecture 19 Unstructured Data Structured Data Knowledge Graphs Machine Learning Semantic Layer Cognitive Applications
  20. 20. No black-box Explainable AI (XAI) Explaining an image classification prediction made by Google’s Inception network, highlighting positive pixels. The top 3 classes predicted are “Electric Guitar” (p = 0.32), “Acoustic guitar” (p = 0.24) and “Labrador” (p = 0.21) From: Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). ACM. 20 Explainable AI as an AI whose decision-making mechanism for a specific problem can be understood by humans who have expertise in making decisions for that specific problem. Explainable AI has been used for years in AI that are based on transparent methods. These include Expert Systems or Symbolic Reasoning Systems - anything that is considered GOFAI (Good Old-Fashioned AI) methods)
  21. 21. No black-box Explainable AI (XAI) Explaining a text classification prediction made by PoolParty Semantic Suite, highlighting positive concepts and terms. 21
  22. 22. Hybrid Approach 22 Artificial Intelligence ANN Symbolic AISub-Symbolic AI Statistical AI KR & reasoning NLP Machine Learning Word Embedding Deep Learning Natural Language Understanding Entity Recognition & Linking Knowledge Extraction Semantic enhanced Text Classification In Semantic AI, various methods from Symbolic AI are combined with machine learning methods, and/or neuronal networks. Examples: ● Semantic enrichment of text corpora to enhance word embeddings ● Extraction of semantic features from text to improve ML-based classification tasks ● Combine ML-based with Graph-based entity extraction ● Knowledge Graphs as a Data Model for Machine Learning ● ….
  23. 23. Structured data meets text Knowledge Graphs as a Data Model for Machine Learning These transformations can result in loss of information and introduce bias. To solve this problem, we require machine learning methods to consume knowledge in a data model more suited to represent this heterogeneous knowledge. We argue that knowledge graphs are that data model. Three examples for the benefits of using knowledge graphs: ▸ they allow for true end-to-end-learning, ▸ they simplify the integration of heterogeneous data sources and data harmonization, ▸ they provide a natural way to seamlessly integrate different forms of background knowledge. Wilcke X, Bloem P, De Boer V. The Knowledge Graph as the Default Data Model for Machine Learning. Data Science. 2017 Oct 17;1-19. Available from, DOI: 10.3233/DS-170007 23 Traditionally, when faced with heterogeneous knowledge in a machine learning context, data scientists preprocess the data and engineer feature vectors so they can be used as input for learning algorithms (e.g., for classification).
  24. 24. Towards self optimizing machines The Singularity is just around the corner ;-) Mike Bergman (2014): Knowledge-based Artificial Intelligence 24
  25. 25. USE CASES Bringing Machine Learning, NLP and Knowledge Graphs together 25
  26. 26. The LinkedIn Economic Graph 26 The LinkedIn Economic Graph is a digital representation of the global economy based on ▸ 560 million members, ▸ 50 thousand skills, ▸ 20 million companies, ▸ 15 million open jobs, and ▸ 60 thousand schools. https://economicgraph.linkedin.com/ “LinkedIn has a vast quantity of data. While much of the data is structured—graph nodes and edges, normalized fields in database records—a great deal of it is simply natural language text. Attaching structure and meaning to this text is essential to LinkedIn’s overall mission of connecting its members to opportunity.”
  27. 27. 27 13485 skills / competences 2942 occupations For more information go to: https://ec.europa.eu/ esco/portal/home EventAdvisor helps to 'putting out feelers' to find the right people and interesting content
  28. 28. Deep Text Analytics Combining ML-based and Graph-based text mining with rules Bain Capital is a venture capital company based in Boston, MA. Since inception it has invested in hundreds of companies. In 2018, Bain had $75b AUM. Graph-based Entity Extraction ML-based Entity Extraction Regular Expression-based Extraction SHACL-based Rules Engine Give me all paragraphs in documents talking about American Private Equity firms with AUM higher than $20b 28
  29. 29. ▸ Data Quality & Date Governance ▹ Content aboutness in a defined framework ▹ Data relationships and context within a unified organizational model ▹ Connections across disparate datasets ▸ Improved Machine Learning ▹ 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 29
  30. 30. Examples for enterprise use cases based on (Semantic) AI 30
  31. 31. One question at the end Will Artificial Intelligence make Subject Matter Experts obsolete? 31 Imagine you want to build an application that helps to identify patients and treatments pairings. Which will you prefer? 1. Solely based on machine learning, 2. based on doctors' knowledge only, 3. or a combination of both?
  32. 32. Thank you for your interest! Andreas Blumauer CEO, Semantic Web Company ▸ Mail andreas.blumauer@semantic-web.com ▸ Company https://www.semantic-web.com ▸ LinkedIn https://www.linkedin.com/in/andreasblumauer ▸ Twitter https://twitter.com/semwebcompany 32 © Semantic Web Company - http://www.semantic-web.com and http://www.poolparty.biz/

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