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2018 Ontology Summit: Ontologies in Context

September 12th, 2017, by Tim Finin, posted in events, KR, Ontologies, Semantic Web

2018 Ontology Summit: Ontologies in Context

The OntologySummit is an annual series of online and in-person events that involves the ontology community and communities related to each year’s topic. The topic chosen for the 2018 Ontology Summit will be Ontologies in Context, which the summit describes as follows.

“In general, a context is defined to be the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed. Some examples of synonyms include circumstances, conditions, factors, state of affairs, situation, background, scene, setting, and frame of reference. There are many meanings of “context” in general, and also for ontologies in particular. The summit this year will survey these meanings and identify the research problems that must be solved so that contexts can succeed in achieving the full understanding and assessment of an ontology.”

Each year’s Summit comprises of a series of both online and face-to-face events that span about three months. These include a vigorous three-month online discourse on the theme, and online panel discussions, research activities which will culminate in a two-day face-to-face workshop and symposium.

Over the next two months, there will be a sequence of weekly online meetings to discuss, plan and develop the 2018 topic. The summit itself will start in January with weekly online sessions of invited speakers. Visit the the 2018 Ontology Summit site for more information and to see how you can participate in the planning sessions.

A hands-on introduction to TensorFlow and machine learning, 10am 3/28

March 18th, 2017, by Tim Finin, posted in events, Machine Learning, meetings


A Hands-on Introduction to TensorFlow and Machine Learning

Abhay Kashyap, UMBC ebiquity Lab

10:00-11:00am Tuesday, 28 March 2017, ITE346 ITE325b

As many of you know, TensorFlow is an open source machine learning library by Google which simplifies building and training deep neural networks that can take advantage of computers with GPUs. In this meeting, I will introduce some basic concepts of TensorFlow and machine learning in general. This will be a hands on tutorial where we will sit and code up some basic examples in TensorfFow. Specifically, we will use TensorFlow to implement linear regression, softmax classifiers and feed forward neural networks (MLP). You can find the Python notebooks here. If time permits, we will go over the implementation of the popular word2vec algorithm and introduce LSTMs to build language models.

What you need to know: Python and the basics of linear algebra and matrix operations. While it helps to know basics of machine learning, no prior knowledge will be assumed and there will be a gentle high level introduction to the algorithms we will implement.

What you need to bring: A laptop that has Python and pip installed. Having virtual environments set up on your computer is also a plus. (Warning: Windows-only users might be publicly shamed)

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