Analyzing User Interactions with Biomedical Ontologies: A Visual Perspective
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The document presents a talk on analyzing user interactions with biomedical ontologies through a catalog called 'bionic' and a visual application that helps explore bioportal usage. It discusses the methodologies for data collection and analysis, user behaviors, and the impact of querying strategies on ontology usage. The findings highlight minimal correlations between exploration strategies and ontology reuse, emphasizing the need for better interfaces and advanced methods for summarizing and modularizing ontologies.
Analyzing User Interactions with Biomedical Ontologies: A Visual Perspective
- 1. Analyzing User Interactions with Biomedical Ontologies: A Visual Perspective Tuesday Talk, 14th November 2017 M A U L I K KA M D A R , S I M O N WA L K , TA N I A T U D O R A C H E , MA R K MU S E N Stanford Center for Biomedical Informa:cs Research maulikrk@stanford.edu
- 6. Uses: Knowledge management, decision support, semantic search, data annotation, data integration, reasoning … …
- 7. Uses: Knowledge management, decision support, semantic search, data annotation, data integration, reasoning … …
- 15. Benefits of analyzing user interacRons Ø Ontology Engineers: v IdenRfy exploraRon and querying paGerns v Understand ontology usage and reuse v Prune unwanted classes and relaRons Ø Ontology Repository Maintainers: v Categorize user behaviors v Develop intelligent interfaces v Provide targeted recommendaRons Ø Biomedical Researchers: v IdenRfy temporal research trends v IdenRfy frequently accessed classes
- 18. NCBO API Usage APIRequestsperMonth 2013−O ct 2014−Jan 2014−Apr 2014−Jul 2014−O ct 2015−Jan 2015−Apr 2015−Jul 2015−O ct 2016−Jan 2016−Apr 2016−Jul 2016−O ct 2M8M32M Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data NCBO Website Traffic OccurrencesperMonth 2009−Jan 2010−Jan 2011−Jan 2012−Jan 2013−Jan 2014−Jan 2015−Jan 2016−Jan 0100K200K Page Requests Unique IP Addresses BiOnIC datasets creaRon • Removing robot/invalid requests • Normalizing ontology idenRfiers and class IRIs
- 19. Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data BiOnIC datasets creaRon • January 2015 version. • Ontologies should have classes that are reused by others OR reuse classes from other ontologies. • Ontologies should have minimum of 10 unique users via WebUI and API
- 20. Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data BiOnIC datasets creaRon Class Sta:s:cs Datasets For each class in each ontology: • Access ANributes: o Total IP Requests (WebUI/API) o Unique IP Requests (WebUI/API) • Reuse ANributes: o Number of ontologies reusing a class • Structural ANributes: o Number of parent/child/sibling classes o Depth from ontology root
- 31. 2a’ 1’ 2b’ 3b’ 2a 3a 4a 3a 1 Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data BiOnIC datasets creaRon User Interac:on Sequences Datasets Ontology 1 Ontology 2
- 32. Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data BiOnIC datasets creaRon Anonymiza:on Steps • IP addresses anonymized using unique SHA-224 hash-encoded user idenRfiers generated from “user_<Random String>_<Random_Integer>”. • e.g. 39fd4e6d569a034973g61bb392a694d4eabe1ef98c43ee68ca2fc86 • Absolute Time-stamps converted to relaRve Rme-stamps, with respect to first interacRon with BioPortal repository. • e.g. 0, 2757, 2786, 3586, 3618, 3803, 3959, 4047, 5111 (s), …
- 33. BiOnIC schema to model staRsRcs and sequences data countStat bionic:CountStat bionic:ReuseCount - reuseType - reusingOntologies bionic:RequestCount - accessType - year - totalUsers - uniqueUsers prov:Agent bionic:Sequence - accessType - totalTime - uniqueClasses bionic:SeqEn:ty - rela6veTimestamp bionic:Ontology - skos:prefLabel - totalClasses - maxDepth owl:Class - skos:prefLabel skos:Collec:on skos:Concept begin end nextEn6ty class class requests skos:member bionic:SeqDataset - accessType bionic:StatDataset dcat:Dataset sequence classInfo ontology ontology bionic:ClassInfo - siblings - directParents - directChildren - classDepth class subClassOf ontology SKOS, PROV and DCAT standards are reused in the BiOnIC schema.
- 49. CharacterisRcs of the BiOnIC Catalog • WebUI Access: 5.4M class requests, 1M unique agents • API Access: 67.2M class requests, 205K unique agents • 255 biomedical ontologies
- 58. Do BioPortal WebUI exploraRon and API querying strategies correlate with each other? • Small proporRons of ontological content explored or queried for several ontologies, and minimal Spearman CorrelaRon and Jaccard Similarity observed between consumpRon strategies. • Varying consumpRon strategies (triangular polygons, blind querying) across the interfaces – these strategies are generally uniform across ontologies, with a few excepRons (ChEBI). • Classes in the lower layers of the ontological hierarchy are rarely browsed or queried using the WebUI or API.
- 62. Do BioPortal WebUI exploraRon and API querying strategies inform ontology usage? • Minimal correlaRon and similariRes between exploraRon and querying strategies, and usage (reuse and data annotaRons) • Ontology reuse occurs from classes located in the higher layers of the ontological hierarchy for semanRc interoperability between ontologies . • However, these classes are very abstract and not very relevant for data annotaRon or data integraRon, where classes in the lower layers of hierarchy are highly used. – These classes are rarely explored or queried, we need beGer interfaces!
- 63. Novel research direcRons may be enabled through the BiOnIC and VisIOn resources • Categorize user browsing behaviors by incorporaRng the structural features of the ontology classes. • Develop personalized user interfaces for ontology navigaRon, with predicRons of the next class that a user is likely to access, or targeted recommendaRons based on user type (recurrent neural networks, collaboraRve filtering, …). • Develop advanced methods for ontology summarizaRon and modularizaRon, using BiOnIC datasets as features. …