BrightTALK - Semantic AI
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Bringing Machine Learning and Knowledge Graphs Together Six Core Aspects of Semantic AI: - Hybrid Approach - Data Quality - Data as a Service - Structured Data Meets Text - No Black-box - Towards Self-optimizing Machines
![1) Class Discovery: Find the categories of objects
(population segments), events, and behaviors in your
data. + Learn the rules that constrain the class
boundaries (that uniquely distinguish them).
2) Correlation (Predictive and Prescriptive Power)
Discovery: Finding trends, patterns, dependencies in
data, which reveal the governing principles or behavioral
patterns (the object’s “DNA”).
3) Novelty (Surprise!) Discovery:
Finding new, rare, one-in-a-[million / billion / trillion]
objects, events, or behaviors.
4) Association (or Link) Discovery: (Graph and Network
Analytics) – Find the unusual (interesting) co-occurring
Make your data smarter with Machine Learning =
= generate semantic tags that describe discoveries
10
(Graphic by S. G. Djorgovski, Caltech)](https://image.slidesharecdn.com/brighttalksemanticai-180523122111/85/BrightTALK-Semantic-AI-10-320.jpg)
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BrightTALK - Semantic AI
- 1. Principal Data Scientist Booz Allen Hamilton http://www.boozallen.com/datascience Kirk Borne @KirkDBorne Semantic AI: Smart Data for Smarter Discovery & Actions
- 2. Six Core Aspects of Semantic AI https://bit.ly/2Kxw8H5 •Hybrid Approach •Data Quality •Data as a Service •Structured Data Meets Text •No Black-box •Towards Self-optimizing Machines
- 3. Ever since we first explored our world… http://www.livescience.com/27663-seven-seas.html 3
- 4. …We have asked questions about everything around us. https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/ 4
- 5. So, we have collected evidence (data) to answer our questions, which leads to more questions, which leads to more data collection, which leads to more questions, which leads to… BIG DATA! 5 https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair
- 6. So, we have collected evidence (data) to answer our questions, which leads to more questions, which leads to more data collection, which leads to more questions, which leads to… BIG DATA! y ~ 2 * x (linear growth) y ~ 2 ^ x (exponential growth) 6 https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair y ~ x! ≈ x ^ x → Combinatorial Growth! (all possible interconnections, linkages, and interactions) 3+1 V’s of Big Data: Volume = most annoying V Velocity = most challenging V Variety = most rich V for discovery Value = the most important V
- 7. “All the World is a Graph” – Shakespeare? (Graphic by Cray, for Cray Graph Engine CGE) 7 http://www.cray.com/products/analytics/cray-graph-engine
- 8. Semantic, Meaning-filled Data: • Ontologies (formal) • Taxonomies (class hierarchies) • Folksonomies (informal) • Tagging / Annotation – Automated (Machine Learning) – Crowdsourced – “Breadcrumbs” (user trails) Broad, Enriched Data: • Linked Data (RDF) – All of those combinations! • Graph Databases • Machine Learning • Cognitive Analytics • Context • The 360o view Making Sense of the World with Smart Data The Human Connectome Project: mapping and linking the major pathways in the brain. http://www.humanconnectomeproject.org/ 8
- 9. Semantic AI in the Internet of Things (IoT): Internet of Everything https://www.nsf.gov/news/news_images.jsp?cntn_id=122028 9 The Internet of Things (IoT) will be an interconnected network of Sensors and Dynamic Data-Driven Application Systems (dddas.org) => Leading to a Combinatorial Explosive Growth of Smart Data! IoT will power an “Internet of Context” – empowering smarter actionable intelligence from contextual data everywhere!
- 10. 1) Class Discovery: Find the categories of objects (population segments), events, and behaviors in your data. + Learn the rules that constrain the class boundaries (that uniquely distinguish them). 2) Correlation (Predictive and Prescriptive Power) Discovery: Finding trends, patterns, dependencies in data, which reveal the governing principles or behavioral patterns (the object’s “DNA”). 3) Novelty (Surprise!) Discovery: Finding new, rare, one-in-a-[million / billion / trillion] objects, events, or behaviors. 4) Association (or Link) Discovery: (Graph and Network Analytics) – Find the unusual (interesting) co-occurring Make your data smarter with Machine Learning = = generate semantic tags that describe discoveries 10 (Graphic by S. G. Djorgovski, Caltech)
- 11. SEMANTIC AI USE CASE IN ENVIRONMENTAL SCIENCE: From Data to Information to Knowledge to Understanding 11
- 12. Semantic AI tags new discoveries for search, re-use, & building the knowledge graph! 12 SEMANTIC AI USE CASE IN ENVIRONMENTAL SCIENCE:
- 13. Semantic AI creates a Smarter Data Narrative • It is best when we understand our data’s context and meaning… • … the Semantics! This is based on Ontologies. • My students memorized the definition of an Ontology… –“is_a formal, explicit specification of a shared conceptualization.” from Tom Gruber (Stanford) • Semantic “facts” can be expressed in a database as RDF triples: {subject, predicate, object} = {noun, verb, noun} 13
- 14. Get Smart (Data)! • Collect, Create, Connect smart data across your repositories. • Build Actionable Knowledge with Semantic AI, not databases! … then Explore and Exploit Your Knowledge Graph. 14http://ghostednotes.com/category/semantic-web Chapters Indexes Covers Tablesof Contents https://www.quora.com/What-is-the-main-goal-of-semantic-web Query your data for Patterns & Knowledge (Action)(Discovery)
- 15. Andreas Blumauer CEO & Managing Partner Semantic Web Company / PoolParty Semantic Suite Semantic AI Bringing Machine Learning, NLP and Knowledge Graphs together
- 16. Agenda 16 Semantic AI ▸ A Quick Introduction to the Semantic Web ▹ Semantic Web in Use ▹ Reasoning ▹ The Linked Data Lifecycle ▸ Six Core Aspects of Semantic AI ▹ Data Quality ▹ Data as a Service ▹ No black-box ▹ Hybrid approach ▹ Structured data meets text ▹ Towards self optimizing machines
- 17. A Quick Introduction To the Semantic Web Benefiting from Knowledge Graphs and Semantic Web Standards 17
- 18. The Semantic Web A standards-based graph of knowledge graphs 18 Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. http://lod- cloud.net/
- 19. Semantic Web in Use Knowledge Graphs to support Search and Q&A engines 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 19 Who is the inventor of the World Wide Web?
- 20. Reasoning Knowledge Graphs & Knowledge Extraction 20 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: Support complex Q&A: distance between Which cities located in the Commonwealth of Nations have a population of Commonwealt h of Nations Internation al Organisatio n is part of is a
- 21. The Linked Data Life Cycle Creating Semantic Data along the Data Life Cycle 21 Auer, S. et al. (2012). Managing the life-cycle of linked data with the LOD2 stack.In International semantic Web conference (pp. 1-16). Springer, Berlin, Heidelberg. https://link.springer.com/content/pdf/10.1007/978-3-642-35173-0_1.pdf
- 22. Six Core Aspects of Semantic AI #SemanticAI: Bringing Machine Learning, NLP and Knowledge Graphs together 22
- 23. Six Core Aspects of Semantic AI 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. 5. Structured data meets text: Most machine learning algorithms work well either with text or with structured data. 6. Towards self optimizing machines: Machine learning can help to extend knowledge graphs, and in return, knowledge graphs can help to improve ML algorithms. https://www.datasciencecentral.com/profiles/blogs/six-core-aspects-of-semantic-ai 23
- 24. 1. Data Quality Benchmarking the PoolParty Semantic Classifier 24 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 ▸ Improvement of 5.2% (F1 score) compared to traditional (term-based) SVM
- 25. 1. Data Quality PoolParty Semantic Classifier in a Nutshell 25 PoolParty Semantic Classifier combines machine learning algorithms (SVM, Deep Learning, Naive Bayes, etc.) with Semantic Knowledge Graphs.
- 26. 2. Data as a Service 26 Structured Data Machine Learning Cognitive Applications
- 27. 2. Data as a Service 27 Unstructured Data Structured Data Machine Learning Cognitive Applications
- 28. 2. Data as a Service 28 Unstructured Data Structured Data Knowledge Graphs Machine Learning Cognitive Applications
- 29. 2. Data as a Service Knowledge Graphs as a Data Model for Machine Learning 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 29 “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).”
- 30. 3. No Black Box Infrastructure to overcome information asymmetries between the developers of AI systems and other stakeholders 30
- 31. 3. No Black Box Explainable AI Classifiers based on ML algorithms such as Deep Learning perform better when training data is semantically enhanced. Additional features are derived from a controlled vocabulary, which also make the used features more transparent to the Data Scientist. 31
- 32. 4. Hybrid Approach 32 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 ● ….
- 33. 5. Structured Data meets Text 33 Purchase History Social Media Recommender Personal Assistant Prediction Customer Retention Classification Intent Detection
- 34. Examples for use cases based on (Semantic) AI 34
- 35. 6. Towards self optimizing machines 35 ▸ Semantic AI is the next-generation Artificial Intelligence ▸ Machine learning can help to extend knowledge graphs (e.g., through ‘corpus-based ontology learning’ or through graph mapping based on ‘spreading activation’), and in return, knowledge graphs can help to improve ML algorithms (e.g., through ‘distant supervision’). ▸ This integrated approach ultimately leads to systems that work like self optimizing machines after an initial setup phase, while being transparent to the underlying knowledge models. ▸ Graph Convolutional Networks (in progress) promise new insights Mike Bergman: Knowledge-based Artificial Intelligence (2014) http://www.mkbergman.com/1816/knowledge-based-artificial- intelligence/
- 36. ▸ 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 36
- 37. Next steps ▸ Mail: [email protected] ▸ LinkedIn: https://www.linkedin.com/in/andreasblumauer ▸ Download: White Paper ‘Introducing Semantic AI’ ▸ Visit: SEMANTiCS Conference ▸ E-Learn: PoolParty Academy 37 © Semantic Web Company - http://www.semantic-web.com and http://www.poolparty.biz/