Analyzing User Interactions with Biomedical Ontologies
Biomedical ontologies are large: Several ontologies in the BioPortal repository contain thousands or even hundreds of thousands of entities. The development and maintenance of such large ontologies is difficult. To support ontology authors and repository developers in their work, it is crucial to improve our understanding of how these ontologies are explored, queried, reused, and used in downstream applications by biomedical researchers. We present an exploratory empirical analysis of user activities in the BioPortal ontology repository by analyzing BioPortal interaction logs across different access modes over several years. We investigate how users of BioPortal query and search for ontologies and their classes, how they explore the ontologies, and how they reuse classes from different ontologies. Additionally, through three real-world scenarios, we not only analyze the usage of ontologies for annotation tasks but also compare it to the browsing and querying behaviors of BioPortal users. For our investigation, we use several different visualization techniques. To inspect large amounts of interaction, reuse, and real-world usage data at a glance, we make use of and extend PolygOnto, a visualization method that has been successfully used to analyze reuse of ontologies in previous work. Our results show that exploration, query, reuse, and actual usage behaviors rarely align, suggesting that different users tend to explore, query and use different parts of an ontology. Finally, we highlight and discuss differences and commonalities among users of BioPortal.
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Analyzing User Interactions with Biomedical Ontologies
- 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 ⌠âŚ
- 7. Uses: Knowledge management, decision support, semantic search, data annotation, data integration, reasoning ⌠âŚ
- 15. BeneďŹts of analyzing user interacRons Ă⯠Ontology Engineers: v⯠IdenRfy exploraRon and querying paGerns v⯠Understand ontology usage and reuse v⯠Prune unwanted classes and relaRons Ă⯠Ontology Repository Maintainers: v⯠Categorize user behaviors v⯠Develop intelligent interfaces v⯠Provide targeted recommendaRons Ă⯠Biomedical Researchers: v⯠IdenRfy temporal research trends v⯠IdenRfy frequently accessed classes
- 18. NCBO API Usage APIRequestsperMonth 2013âO ct 2014âJan 2014âApr 2014âJul 2014âO ct 2015âJan 2015âApr 2015âJul 2015âO ct 2016âJan 2016âApr 2016âJul 2016âO ct 2M8M32M Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data NCBO Website Traffic OccurrencesperMonth 2009âJan 2010âJan 2011âJan 2012âJan 2013âJan 2014âJan 2015âJan 2016âJan 0100K200K Page Requests Unique IP Addresses BiOnIC datasets creaRon â˘âŻ Removing robot/invalid requests â˘âŻ Normalizing ontology idenRďŹers and class IRIs
- 19. Filtering Access Logs Filtering Ontologies CompuRng Class Counts CompuRng Sequences Anonymizing Data BiOnIC datasets creaRon â˘âŻ January 2015 version. â˘âŻ Ontologies should have classes that are reused by others OR reuse classes from other ontologies. â˘âŻ 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: â˘âŻ Access ANributes: o⯠Total IP Requests (WebUI/API) o⯠Unique IP Requests (WebUI/API) â˘âŻ Reuse ANributes: o⯠Number of ontologies reusing a class â˘âŻ Structural ANributes: o⯠Number of parent/child/sibling classes o⯠Depth from ontology root
- 31. 2aâ 1â 2bâ 3bâ 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 â˘âŻ IP addresses anonymized using unique SHA-224 hash-encoded user idenRďŹers generated from âuser_<Random String>_<Random_Integer>â. â˘âŻ e.g. 39fd4e6d569a034973g61bb392a694d4eabe1ef98c43ee68ca2fc86 â˘âŻ Absolute Time-stamps converted to relaRve Rme-stamps, with respect to ďŹrst interacRon with BioPortal repository. â˘âŻ e.g. 0, 2757, 2786, 3586, 3618, 3803, 3959, 4047, 5111 (s), âŚ
- 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 â˘âŻ WebUI Access: 5.4M class requests, 1M unique agents â˘âŻ API Access: 67.2M class requests, 205K unique agents â˘âŻ 255 biomedical ontologies
- 58. Do BioPortal WebUI exploraRon and API querying strategies correlate with each other? â˘âŻ Small proporRons of ontological content explored or queried for several ontologies, and minimal Spearman CorrelaRon and Jaccard Similarity observed between consumpRon strategies. â˘âŻ Varying consumpRon strategies (triangular polygons, blind querying) across the interfaces â these strategies are generally uniform across ontologies, with a few excepRons (ChEBI). â˘âŻ 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? â˘âŻ Minimal correlaRon and similariRes between exploraRon and querying strategies, and usage (reuse and data annotaRons) â˘âŻ Ontology reuse occurs from classes located in the higher layers of the ontological hierarchy for semanRc interoperability between ontologies . â˘âŻ 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. â⯠These classes are rarely explored or queried, we need beGer interfaces!
- 63. Novel research direcRons may be enabled through the BiOnIC and VisIOn resources â˘âŻ Categorize user browsing behaviors by incorporaRng the structural features of the ontology classes. â˘âŻ 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 ďŹltering, âŚ). â˘âŻ Develop advanced methods for ontology summarizaRon and modularizaRon, using BiOnIC datasets as features. âŚ