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Session Track B Session 2
Duration 1.5 hour
Date/Time Apr 12 2017 18:30 GMT
9:30am PDT/12:30pm EDT
5:30pm BST/6:30pm CEST
Convener TBD

Ontology Summit 2017 Track B Session 2     (2)

Meeting ID 768423137     (4)

Please use the chatroom above. Do not use the video teleconference chat, which is only for communicating with the moderator.     (7)

When you use the Video Conference URL above, you will be given the choice of using the computer audio or using your own telephone. Some attendees had difficulties when using the computer audio choice. If this happens to you, please leave the meeting and reenter it using the telephone choice with access code 768423137.     (8)


Contents

Abstract     (9)

Context: Background knowledge and ontologies can be used to improve machine learning model selection, processing, data preparation and results.     (9A)

Perception: Machine Learning requires knowledge of the domain being examined to define relevant hypotheses, infer missing information in data, choose relevant features, etc.     (9B)

Motivation: Discuss the kinds of ontologies needed for ML, and their "ground rules" and requirements.     (9C)

Agenda     (10)

  • Introduction     (10A)
    • In the first Track B session on March 15, there were two presentations on combining ontologies with machine learning and natural language processing technologies in order to improve results. In the first case, ontologies were combined with ML to improve decision support. The benefits included improving the quality of decisions, making decisions more understandable, and adapting the decision making processes in response to changing conditions. Regarding combining ontologies with NLP processing, this was in support of digital forensics and situational awareness. Concept extraction from natural language text was improved by using an ontology to isolate the meanings/semantics of the concepts and provide “artificial intuition” into the text.     (10A1)
    • In the second Track B session, we want to continue discussing the use of ontologies to improve machine learning understandability and natural language processing. We have presentations on Machine-Based Machine Learning (MBML), discussing how to get meaningful output from existing machine learning techniques, and on the use of FIBO and corporate taxonomies to extract and integrate information from data warehouses, operational stores and natural language communications.     (10A2)
  • Speakers     (10B)
    • Courtney Falk, Infinite Machines     (10B1)
      • Title: The Meaning-Based Machine Learning Project     (10B1A)
      • Abstract: Meaning-based machine learning (MBML) is a project to investigate how to get meaningful output from existing machine learning techniques. MBML builds from the Ontological Semantics Technology (OST) where natural language resources map to an ontology. An application of MBML to detecting phishing emails provides some initial experimental results. Finally, future research directions are explored.     (10B1B)
    • Bryan Bell, Expert System     (10B2)
      • Title: Leveraging FIBO with Semantic Analysis to Perform On-Boarding, Know-Your-Customer (KYC) and Customer-Due-Diligence (CDD)     (10B2A)
      • Abstract: The Financial Industry Business Ontology (FIBO) provides a common ontology and taxonomy for financial instruments, legal entities, and related knowledge. It provides regulatory and compliance value by ensuring that a common language can be used for data harmonization and reporting purposes. This session discusses using the formal structure of FIBO and other corporate taxonomies on unstructured information in data warehouses, operational stores and natural language communications (such as news articles, research reports, customer interactions, emails, and product descriptions), in order to create new value and aid in onboarding new customers, establishing a dependable know-your-customer process and complete on-going customer due diligence processes. Financial Industry Business Ontology (FIBO) provides the common language for bridging interoperability gaps and organizing content in a consistent way.     (10B2C)
    • Tatiana Erekhinskaya, Lymba Corporation     (10B3)
      • Title: Converting Text into FIBO-Aligned Semantic Triples     (10B3A)
      • Abstract: Ontologies are playing a major role in federating multiple sources of structured data within enterprises. However, the unstructured documents remain mostly untouched or require manual labor to be included into consolidated knowledge management process. At Lymba, we are developing a knowledge extraction tool that automatically identifies instances of concepts/classes and relations between them in the text. The extraction is driven by a semantic model or ontology. For example, using FIBO terminology, the system recognizes time/duration constraints in contracts, money values, and their meaning - transaction value, penalty, fee, etc. and links them to the parties in the contract. The extracted knowledge is represented in a form of semantic triples, which can be persisted in an RDF storage to allow integration with other sources, inference, and querying. One more useful add-on is natural language querying capability, when a query like “Find clauses with time constraints for payor” is automatically converted into semantic triples, and then into SPARQL. This talk will give the audience an overview of Lymba’s knowledge extraction pipeline, as well as our knowledge representation framework. Semantic parsing and triple-based representation provide a bridge between semantic technologies and NLP, leveraging inference techniques and existing ontologies. We will show how Lymba’s Semantic Calculus framework allows easy customization of the solution to different domains.     (10B3C)
  • Discussion     (10C)

Attendees     (11)

Proceedings     (12)

Resources     (13)

Previous Meetings     (14)