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	<updated>2026-07-27T14:43:13Z</updated>
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		<title>imported&gt;Ravisharma: Created page with &quot;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 betwe...&quot;</title>
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		<updated>2017-05-07T23:40:01Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;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 betwe...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;TO BE EDITED&lt;br /&gt;
Ontolog Summit 2017 &lt;br /&gt;
Summary of Tools, Processes, Languages –sessions and tracks Feb-March 2017&lt;br /&gt;
Compiled by Dr. Ravi Sharma- May 2017&lt;br /&gt;
Focus -Relationships between AI and ontology &lt;br /&gt;
● Tracks focus on 3 specific relationships &lt;br /&gt;
– Track A: Using Automation and ML to Extract Knowledge and Improve Ontologies (Learning →Ontology) &lt;br /&gt;
– Track B: Using background knowledge to improve machine learning results (Ontology→Learning) &lt;br /&gt;
– Track C: Using ontologies for logical reasoning (Ontology→Reasoning)&lt;br /&gt;
Intro and Overview related presentations:&lt;br /&gt;
Track A: Champion - Gary Berg Cross&lt;br /&gt;
•	Ontology engineering is an iterative and spotty (non-uniform progress in its activities and process).&lt;br /&gt;
•	Bottlenecks and obstructions in Onto-Eng. and Onto Dev. Ref: Oscar Corcho&lt;br /&gt;
•	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.&lt;br /&gt;
•	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).&lt;br /&gt;
•	Ontology Learning: about building domain ontologies automatic extraction of concepts and relationships. A Layer Cake of Ontological Primitives. Book Ref.- Paul Buitelaar, Philipp Cimiano &amp;amp; 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.&lt;/div&gt;</summary>
		<author><name>imported&gt;Ravisharma</name></author>
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