TO BE EDITED Ontolog Summit 2017 Summary of Tools, Processes, Languages –sessions and tracks Feb-March 2017 Compiled by Dr. Ravi Sharma- May 2017 Focus -Relationships between AI and ontology ● Tracks focus on 3 specific relationships – Track A: Using Automation and ML to Extract Knowledge and Improve Ontologies (Learning →Ontology) – Track B: Using background knowledge to improve machine learning results (Ontology→Learning) – Track C: Using ontologies for logical reasoning (Ontology→Reasoning) Intro and Overview related presentations: Track A: Champion - Gary Berg Cross • Ontology engineering is an iterative and spotty (non-uniform progress in its activities and process). • Bottlenecks and obstructions in Onto-Eng. and Onto Dev. Ref: Oscar Corcho • Objective: How Machine Learning (ML) can - generate KB to help Develop Ontologies, - reduce noisy data to further quality of developed ontologies, harmonize ontologies from dependence on peculiarities of datasets used. • Tools Mentioned: OntoLT – Protégé Based for Extracting Concepts and Relationships in text searches. OntoLearn has been successfully experimented in several domains (art, tourism, economy and finance, web learning, interoperability). • Ontology Learning: about building domain ontologies automatic extraction of concepts and relationships. A Layer Cake of Ontological Primitives. Book Ref.- Paul Buitelaar, Philipp Cimiano & Bernardo Magnini (Editors). Also linguistic methods: by Ícaro Medeiros (2009). Concept Learning: Jens LEHMANN et.al. algorithms, decision trees, Operator factors, all used to reduce work in Ont. Eng. NELL: Never-Ending Language Learner (2014) - Semi-Supervised Bootstrap Learning to read, reason and extend ontology. Discourse Representation Theory (DRT), Semantic Technology Laboratory, Valentina Presutti et.al. using frame semantics and design patterns.