UKON 2016 Short Talks I

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

Source 14 April 2016

Integrating literature mining and curation for ontology-driven knowledge discovery
George Demetriou, Warren Read, Noel Ruddock, Martyn Fletcher, Goran Nenadic, Tom Jackson, Robert Stevens, Jerry Winter

It is hard to keep up with the volume and complexity of data throughout its life cycle (search for content, collect it, read and analyse it, convert it into formal representations, integrate knowledge into computational models, use it to produce explanations, predictions or innovations). Therefore they have BioHub, which stores information on feedstocks, chemicals, plants, organisms, chemical transformation, and properties. The task is to extract, organise and integrate knowledge into models of chemical engineering. An example question: “Which chemicals come from which feedstocks?”

Where does the human come in for the curation task, and where the machine? DARPA Big Mechanism: a big project to compare based on text evidence from literature. In the study, they found humans are good for finding interactions and bad for grounding. Manchines were bad for interactions and good for grounding. A hybrid method had the best result.

In the BioHub Curation Pipeline, there are different types of annotation, with human and machine curation.

Integrating Concept Dynamism into Logitudinal Analysis of Electronic Health Records
Chris Smith and Alex Newsham

Policies that determine the data captured in EHRs is subject to change over time for a variety of reasons, including updated clinical practice, improved tests, and the introduction or cessation of PH initiatives. EHRs may capture different clinical concepts or use different representations. Longitudinal analysis of EHRs aims to identify patterns in health and healthcare over time to inform the design of interventions. The analysis predicated on the ability to robustly identify specific clinical concepts.

A set of policies determine recording by clinicians. These policies define a set of quality indicators. Updates are provided every 3 months, and need to be taken into account, and the changes need to be recorded.

Dynamism in presence and representation of clinical concepts in policies needs to be integrated into the longitudinal analysis of EHRs. This will improve accuracy with which patients, interventions and outcomes can be characterised over time.
INSPIRE: An Ontological Approach to Augment Careers Guidance
Mirko Michele Dimartino, Vania Dimitrova, Alexandra Poulovassilis

Build an intelligent tool to inspire career paths. They want to build the tool on top of semantic web technologies. A GUI Tool would interface with the user and with a SPARQL endpoint. Other SPARQL endpoints are attached to RDFS data from LinkedIn and L4All (and others). They are joined up and integrated, and then the user queries the system through federated querying. The integration of the data happens with an ontology-based rewriting for integration.

They have one ontology to describe LinkedIn. The user is asked to create a profile, then can explore the next career step or explore a long-term career goal. The user can select time intervals, and the response is matched against those intervals.

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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