Towards A Framework For Meaning Negotiation And Conflict Resolution In Ontology Authoring

 Rolf Grütter1 And C. Maria Keet2

1Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

2Department Of Computer Science, University Of Cape Town, South Africa

ICBO 2020

Introduction• Ontology authoring

• Reuse existing ones in part or in whole, where possible • Import IDO for schistosomiasis knowledge management [Cisse et al., 2019]

• Possible harmonization of disease ontologies [Haendel et al., 2018]

• Many reuses of the GO [GO, 2019]

• Aligning a domain ontology to a foundational ontology

• May result in conflicts • A class Infection in ontology O1 vs. property infected_by in O2 • Parthood declared as transitive in O1 and used in a cardinality constraint

in O1

• …

Problems

• How to deal with such conflicts?

• Currently ad hoc approaches as they come along in the development process

• No guidelines what to do in which situation

• No clear overview of what the choices are for a particular type of issue nor what the consequences of the choices are

• No systematic view on types of issues that may arise

• Mostly no documentation of the trade-off choices made

Toward a solution

• Novel approach for meaning negotiation in ontology development and (re)use and for conflict resolution

• Examine the possible principal sources of conflicts into library

• For each issue, a fixed set of feasible solution strategies

• Explanatory implications may be automatically generated

• Use case demonstration and evaluation: • Epizootic disease outbreak in Switzerland (avian influenza)

• Resolution on ‘appropriate’ mereotopological theory with OWL trade-offs and exceeding OWL 2 DL when combining ontologies

Solution sketch: sample scenario

The paper

• Definitions of meaning negotiation and conflict resolution

• Specification of a data structure: conflict set

• Principal sources are due to: • Ontological differences between theories and at axiom-level

• Different modelling styles

• Logic limitations

• Preliminary library of conflicts

• Details of conflicts and typical solution options

• Some assistance in the process

Sampling

• bla

Language profile violation

• Requirement: the COVID-relevant medical ontology for information systems should not exceed the OWL 2 EL profile (compatibility with OBO, SNOMED CT, scalability)

• CIDO ontology for COVID-19 [He et al., 2020] is not in OWL 2 EL • class expression with a universal quantifier on the right-hand side; a.o.:

• Requires tools to find violating axioms • OWL Classifier [https://github.com/muhummadPatel/OWL_Classifier]

‘Yale New Haven Hospital SARS-CoV-2 assay’ ‘EUA-authorized use at’.’FDA EUA-authorized organization’

Conflicting modeling styles illustration

• Requirement: align/integrate/merge the COVID-19 ontologies

• CIDO ontology + CODO ontology1 • codo:’laboratory testfinding’ = {positive, pending, negative} • cido:’COVID-19 diagnosis’ as a class, with three subclasses

[negative/positive/presumptive positive] COVID-19 diagnosis

• Issue: class vs. instance representations of the same idea

• Solution options: 1. Change CODO 2. Change CIDO 3. Outside option (e.g.: approach with attribute+values)

1https://bioportal.bioontology.org/ontologies/CODO

Use case

• Avian influenza in Switzerland in 2006

• O1: Administrative ontology with municipalities etc

 https://www.envidat.ch/dataset/icbo2020

• O2: Infections info with finds of infected birds, protection and surveillance zones

• Need to query which municipalities to include in which zones

• Therefore need to link up the two

Conflict set

• bla

Conclusions• First steps towards a framework dealing with the various types of

modeling conflicts through meaning negotiation and conflict resolution in a systematic way

• Specified the notions of meaning negotiation and conflict resolution

• First step towards conceiving a library of conflicts

• The notion of conflict set as a minimal data structure on which negotiation and resolution can operate.

• This approach was evaluated with a use case (epizootic disease outbreak)

References• Cisse PA, Camara G, Dembele JM, Lo M. An Ontological Model for the Annotation of Infectious

Disease Simulation Models. In: Bassioni G, Kebe CMF, Gueye A, Ndiaye A, editors. Innovations and Interdisciplinary Solutions for Underserved Areas. LNICST, vol. 296. Cham (Switzerland): Springer; 2019. p. 82–91.

• Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research 2019 Jan;47(D1):D330–8.

• Haendel MA, McMurry JA, Relevo R, Mungall CJ, Robinson PN, Chute CG. A Census of Disease Ontologies. Annual Review of Biomedical Data Science 2018;1:305–31.

• He Y, Yu H, Ong E, Wang Y, Liu Y, Huffman A, Huang H, Beverley J, Hur J, Yang X, Chen L, Omenn GS, Athey B, Smith B. CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis. Scientific Data 2020;7:181.

• Note: source attribution of the photos are in the presenter notes of the slide where they were used.

Thank you!

Questions?