REX - A TOOL FOR DISCOVERING EVOLUTION TRENDS

Transcription

1 REX - A TOOL FOR DISCOVERING EVOLUTION TRENDS IN ONTOLOGY REGIONS VICTOR CHRISTEN, ANIKA GROSS, MICHAEL HARTUNG 18 TH JULY 2014, DILS, LISBOA 1

2 ONTOLOGY EVOLUTION Heavy usage of ontologies in the life sciences, e.g. annotations, semantic search, Ongoing research, new findings Ontology changes Continuous release of new versions Collaborative, distributed development of large ontologies Coordination by project leader Ontology developers with different fields of expertise 2

3 ONTOLOGY EVOLUTION Where are the changes located? Potential for future development? How has the work progressed? Are there (un)stable ontology regions? Unstable ontology regions Many modifications focus of recent development Impact of changes on ontology-based algorithms or applications redo analyses? Stable ontology regions Already completed? Low interest so far further changes necessary? 3

4 CURRENT STATE & CONTRIBUTIONS BioPortal, OBO Foundry, Provide life science ontologies with versions Diff computation (PromptDiff, COntoDiff, ) Web tools to analyze ontology evolution (GOChase, OnEX, ) No tool to analyze and compare evolution in different ontology parts Own previous work: Web application Discovering Region Evolving Evolution Regions in Explorer Life Science (REX) Ontologies. Determination (Hartung, Groß, of differently Kirsten, Rahm, changing DILS 2010) ontology regions Interactive Only applicable exploration offline, of no regions visualization w.r.t. their change intensity Comparative trend analysis Monitor long-term evolution for regions of interest Track the work or coordinate future development 4

5 AGENDA Region Discovery Method Trend Discovery REX Infrastructure Analysis workflows Conclusions and Outlook 5

6 REGION DISCOVERY METHOD Basic idea: Compute change intensities for regions based on changes between succeeding ontology versions O 1 O n using change cost model σ computeaggcosts(o 1,O 2,σ) computeaggcosts(o n-1,o n,σ) O 1 O 2 O n-1 O n Region Discovery 1) Compute changes between succeeding versions 2) Propagate change costs within the is-a hierarchy 3) Transfer aggregated costs from the first to the last version 4) Compute change intensities to discover differently evolving regions 6

7 ONTOLOGY CHANGES AND COSTS Concepts Relationships Attributes Change operation Change costs addc 1 delc 2 addr 0.5/0.5 delr 1.0/1.0 adda 0.5 dela 0.5 chgattvalue 0.5 Costs of an ontology concept c Local costs lc(c) Changes that directly affect c Aggregated costs ac(c) Changes on is-a descendants of c 1 5 c8 1 1 c2 c Cost model can be adapted! c5 c1 c6 c3 c7 7

8 COMPUTE AGGREGATED COSTS FOR TWO VERSIONS Algorithm: computeaggregatedcosts(o old, O new,σ) Input: Two ontology versions O old and O new, change costs σ Output: O new with computed aggregated costs (ac) 1. Detect changes: diff (O old, O new ) 2. Assign local costs to O old and O new 3. Aggregate costs in O old and in O new 4. Transfer costs O old O new 8

9 CHANGE DETECTION AND LOCAL COSTS Simple diff computation: based on accession numbers used in ontology elements addc, delc, addr, delr, Example Uniform change costs of 1 Relationship costs to target O old 1 c2 c1 c3 O new 1 c2 c1 1 c3 1 c4 c5 c6 c7 2 c8 c5 c6 c7 c9 1 lc(c) c 9

10 COST PROPAGATION AND AGGREGATION Propagation of local costs lc Aggregated costs ac(c) of concept c: Weighted sum of all children ac s plus own local costs lc(c) ac c = direct children c of c ac(c ) parents(c ) + lc(c) Example O new c x c8 c5 c c6 c3 c7 propagation of lc(c9) propagation of lc(c8) propagation of lc(c5) propagation of lc(c2) lc(c) ac(c) c

11 COST PROPAGATION AND AGGREGATION Propagation of local costs lc Aggregated costs ac(c) of concept c: Weighted sum of all children ac s plus own local costs lc(c) ac c = direct children c of c ac(c ) parents(c ) + lc(c) Example O new c x c Transfer aggregated c8 c5 costs O c6 c7 old O new Costs of del changes reflected in new(er) version(s) lc(c) ac(c) c Costs of equal concepts are summed up c3 propagation of lc(c9) propagation of lc(c8) propagation of lc(c5) propagation of lc(c2) 11

12 DISCOVERY ALGORITHM FOR MULTIPLE VERSIONS Reuse of computeaggregatedcosts algorithm Successive computation and transfer of aggregated costs Apply region discovery on latest version computeaggcosts(o 1,O 2,σ) computeaggcosts(o n-1,o n,σ) O 1 O 2 O n-1 O n Region Discovery with Measures 12

13 ONTOLOGY REGIONS AND MEASURES Ontology region OR Subgraph of an ontology consisting of a single root concept and all concepts in its is-a subgraph Region measures to quantify change intensity Absolute change costs 7 c2 7 c1 c3 Absolute / relative region size Average change costs c8 c5 c6 c7 region abs_costs rel_size avg_costs 1 c9 ac(c) c c1 7 8/8=1 7/8=0.875 c2 7 4/8=0.5 7/4=1.75 c3 0 3/8= /3=0 13

14 TREND DISCOVERY Trend discovery based on sliding windows Monitor region changes over long periods of time Ontology O, region of interest OR Time interval (t start, t end ) Sliding window of size ω Step width Δ Call region discovery algorithm within ω Collect change intensities for region of interest over time O 1 O 2 O 3 O 4 O 5 O 6 O 7 O 8 O n-2 O n-1 O n ω = 4, Δ=1 t start t end 14

15 AGENDA Region Discovery Method Trend Discovery REX Infrastructure Analysis workflows Conclusions and Outlook 15

16 INFRASTRUCTURE Client request response Communication Interface Function Calls Server Trend Analysis Service Region Algorithm Graph Data Service Quantitative Service SQL Knowledge Base OnEX Ontology versions from the life sciences 16

17 QUANTITATIVE CHANGE ANALYSIS added concepts deleted concepts 17

18 STRUCTURAL ANALYSIS Input Browser View Click node to navigate ontology root stable unstable Coloring schemes Interval based grouping Equal distribution between min/max avg_costs 18

19 STRUCTURAL ANALYSIS Input Browser View Selected concept Parent concept stable unstable Coloring schemes Interval based grouping Equal distribution between min/max avg_costs 19

20 STRUCTURAL ANALYSIS Input Table View Change History Filter / sort by accession number, name, avg_costs View individual change history of concepts 20

21 STRUCTURAL ANALYSIS Input Browser View stable unstable Coloring schemes Interval based grouping Equal distribution between min/max avg_costs 21

22 TREND ANALYSIS 22

23 CONCLUSIONS AND FUTURE WORK Explore evolution of life science ontologies to discover (un)stable regions and trends Monitor development of large ontologies (developers, project coordinators) Support ontology-based algorithms and analysis Support versioning of individual ontologies (upload of own versions) Include user-based adaptation of cost model Web service interface 23