UKON 2016 Short Talks VIII

These are my notes for the eighth session of talks at the UK Ontology Network Meeting on 14 April, 2016.

Source https://lh3.googleusercontent.com/-Ql7RFDSgvxQ/AAAAAAAAAAI/AAAAAAAAAFA/pnoDTCze85Q/s120-c/photo.jpg 14 April 2016

A workflow of developing biological ontologies using a document-centric approach
Aisha Blfgeh, Catharien Hilkens, Phillip Lord

Ontologists know how to use domain-specific tools and applications to develop ontologies, while the biologist uses a completely different set of tools. Can we break through this wall and enable both groups to work together while still using the tools they like to use?

So, this brings us to the Ontologist and the Excel Spreadsheet. There are existing tools which transform an excel sheet once, but don’t allow further updates from that sheet. With this method (which will use Tawny OWL), the spreadsheet remains and becomes part of the ontology. It works almost the same way with a Word document, but here the ontology remains the master.

The PROHOW ontology: a semantic web of human tasks, instructions and activities
Paolo Pareti

A common-sense approach to describing human activities such as How to Make a Pancake.  They transformed existing instructions from wikiHow and snapGuide, extracting 200,000 procedures into a KB. What to do with this knowledge? Activity recognition is one thing (find out an ultimate goal from a few intermediate steps). They’ve been creating links between the different graphs. Often this allows the user of one activity to access extra information (a subgraph, for example) from another graph. Once this works well, you could implement a method to have a machine perform an activity for you (if it is a computer-based activity, e.g. Send an Email).

http://w3id.org/prohow

Combining Ontologies and Machine Learning to Capture Tacit Knowledge in Complex Decision Making
Yiannis Gatsoulis, Owais Mehmood, Vania Dimitrova, Anthony Cohn

This project has been created to help diagnose tunnels, as maintenance operations and the impact of a rail tunnel malfunction can be costly and catastrophic. Factors include tunnel age and external influences (traffic, weather). Tunnel diagnosis is a complex process for which there are few experts with a large amount of tacit knowledge.

PADTUN goal is a decision support system for the engineers. It is a highly complex knowledge set, and hard to describe. The system diagnosis possible tunnel illnesses based on a set of data. The current ontological model may not be enough. There are two key challenges: validation (inaccurate or missing rules, takes a long time to identify these problematic rule, and some rules are more reliable than others), and extension (identification of rules that cannot be articulated by the experts, addition of crucial aspects of the model).

They have survey data and contextual data to support maintenance planning and identify risk levels. Decision trees can be derived from this data to determine when they decide to close and repair the tunnel.

Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

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