UKON 2016 Short Talks II

These are my notes for the second morning 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

Dialogues for finding correspondences between partially disclosed Ontologies
Terry Payne and Valentina Tamma

and-

Dialogue based meaning negotiation
Gabrielle Santos, Valentina Tamma, Terry Payne, Floriana Grasso

Different systems have different ontologies, so you need to align them. Many approaches exist. There are 3 problems: different alignment systems produce different solutions; you may not want to align all of your ontology; part of that ontology might be commercially sensitive and therefore not want to expose or disclose it.

You can use 2 agents to negotiate possible alignments. Agents selectively identify what mappings should be disclosed if the agents are knowledge rich. If not knowledge rich, then the agents need to start exchanging segments of the ontology.

They have started to develop a formal inquiry dialogue that allows two agents to exchange knowledge about known mappings. If you’re looking at many different alignments, you many have many one-to-many mappings. Which mapping should be selected? Could it be resolved through objections within the dialogue? Through the dialogue each agent extends their ontology by including correspondences.

They’ve used a cognitive approach to reaching consensus over possible correspondences. Agents identify possible concepts that may be ontologically equivalent in their respective ontologies. Each then seeks further evidence. One agent asks the other agent if it can align an entity. Start with a lexical match. The second phase will then ask for evidence to support the correspondence, e.g. what are the structural similarities? The CID dialogue has been empirically evaluated using OAEI datasets.

The Cardiovascular Disease Ontolgoy (CVDO)
Mercedes Arguello Casteleiro, Julie Klein, Robert Stevens

CVDKB content: 34 publications, including human and mouse. How can we connect the biological information, e.g. HGNC, UniProt, MGI, ChEBI, miRBase? From this they have 86792 mouse proteins, 172121 proteins from human, and many metabolites and miRNAs. The CVDO reuses some ontologies, such as OBI, and parts of other ontologies including SO, PRO, GO, CL, UBERON, PATO…

There is a CVDO application which allows SPARQL queries over the OWL. They’re looking at including SOLR and elasticsearch to make searching fast, via the conversion of OWL to JSON-LD. Go to http://cvdkb.cs.man.ac.uk to try it out. The idea is to hide the ontological complexity from the end user.

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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