DOI: 10.5281/zenodo.7908854
title: Taxonomies for Semantic Research Data Annotation

description: This dataset contains 35 of 39 taxonomies that were the result of a systematic review. The systematic review was conducted with the goal of identifying taxonomies suitable for semantically annotating research data. A special focus was set on research data from the hybrid societies domain.


The following taxonomies were identified as part of the systematic review:



	
		
			
			
Filename
			
			
			
Taxonomy Title
			
		
		
			
			
acm_ccs
			
			
			
ACM Computing Classification System [1]
			
		
		
			
			
amec
			
			
			
A Taxonomy of Evaluation Towards Standards [2]
			
		
		
			
			
bibo
			
			
			
A BIBO Ontology Extension for Evaluation of Scientific Research Results [3]
			
		
		
			
			
cdt
			
			
			
Cross-Device Taxonomy [4]
			
		
		
			
			
cso
			
			
			
Computer Science Ontology [5]
			
		
		
			
			
ddbm
			
			
			
What Makes a Data-driven Business Model? A Consolidated Taxonomy [6]
			
		
		
			
			
ddi_am
			
			
			
DDI Aggregation Method [7]
			
		
		
			
			
ddi_moc
			
			
			
DDI Mode of Collection [8]
			
		
		
			
			
n/a
			
			
			
DemoVoc [9]
			
		
		
			
			
discretization
			
			
			
Building a New Taxonomy for Data Discretization Techniques [10]
			
		
		
			
			
dp
			
			
			
Demopaedia [11]
			
		
		
			
			
dsg
			
			
			
Data Science Glossary [12]
			
		
		
			
			
ease
			
			
			
A Taxonomy of Evaluation Approaches in Software Engineering [13]
			
		
		
			
			
eco
			
			
			
Evidence & Conclusion Ontology [14]
			
		
		
			
			
edam
			
			
			
EDAM: The Bioscientific Data Analysis Ontology [15]
			
		
		
			
			
n/a
			
			
			
European Language Social Science Thesaurus [16]
			
		
		
			
			
et
			
			
			
Evaluation Thesaurus [17]
			
		
		
			
			
glos_hci
			
			
			
The Glossary of Human Computer Interaction [18]
			
		
		
			
			
n/a
			
			
			
Humanities and Social Science Electronic Thesaurus [19]
			
		
		
			
			
hcio
			
			
			
A Core Ontology on the Human-Computer Interaction Phenomenon [20]
			
		
		
			
			
hft
			
			
			
Human-Factors Taxonomy [21]
			
		
		
			
			
hri
			
			
			
A Taxonomy to Structure and Analyze Human–Robot Interaction [22]
			
		
		
			
			
iim
			
			
			
A Taxonomy of Interaction for Instructional Multimedia [23]
			
		
		
			
			
interrogation
			
			
			
A Taxonomy of Interrogation Methods [24]
			
		
		
			
			
iot
			
			
			
Design Vocabulary for Human–IoT Systems Communication [25]
			
		
		
			
			
kinect
			
			
			
Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors [26]
			
		
		
			
			
maco
			
			
			
Thesaurus Mass Communication [27]
			
		
		
			
			
n/a
			
			
			
Thesaurus Cognitive Psychology of Human Memory [28]
			
		
		
			
			
mixed_initiative
			
			
			
Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey [29]
			
		
		
			
			
qos_qoe
			
			
			
A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction [30]
			
		
		
			
			
ro
			
			
			
The Research Object Ontology [31]
			
		
		
			
			
senses_sensors
			
			
			
A Human-Centered Taxonomy of Interaction Modalities and Devices [32]
			
		
		
			
			
sipat
			
			
			
A Taxonomy of Spatial Interaction Patterns and Techniques [33]
			
		
		
			
			
social_errors
			
			
			
A Taxonomy of Social Errors in Human-Robot Interaction [34]
			
		
		
			
			
sosa
			
			
			
Semantic Sensor Network Ontology [35]
			
		
		
			
			
swo
			
			
			
The Software Ontology [36]
			
		
		
			
			
tadirah
			
			
			
Taxonomy of Digital Research Activities in the Humanities [37]
			
		
		
			
			
vrs
			
			
			
Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions  [38]
			
		
		
			
			
xdi
			
			
			
Cross-Device Interaction [39]
			
		
	




We converted the taxonomies into SKOS (Simple Knowledge Organisation System) representation. The following 4 taxonomies were not converted as they were already available in SKOS and were for this reason excluded from this dataset:


1) DemoVoc, cf. http://thesaurus.web.ined.fr/navigateur/
available at https://thesaurus.web.ined.fr/exports/demovoc/demovoc.rdf


2) European Language Social Science Thesaurus, cf. https://thesauri.cessda.eu/elsst/en/
available at https://zenodo.org/record/5506929


3) Humanities and Social Science Electronic Thesaurus, cf. https://hasset.ukdataservice.ac.uk/hasset/en/
available at https://zenodo.org/record/7568355


4) Thesaurus Cognitive Psychology of Human Memory, cf. https://www.loterre.fr/presentation/
available at https://skosmos.loterre.fr/P66/en/


 


References


[1]      “The 2012 ACM Computing Classification System,” ACM Digital Library, 2012. https://dl.acm.org/ccs (accessed May 08, 2023).


[2]      AMEC, “A Taxonomy of Evaluation Towards Standards.” Aug. 31, 2016. Accessed: May 08, 2023. [Online]. Available: https://amecorg.com/amecframework/home/supporting-material/taxonomy/


[3]      B. Dimić Surla, M. Segedinac, and D. Ivanović, “A BIBO ontology extension for evaluation of scientific research results,” in Proceedings of the Fifth Balkan Conference in Informatics, in BCI ’12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 275–278. doi: 10.1145/2371316.2371376.


[4]      F. Brudy et al., “Cross-Device Taxonomy: Survey, Opportunities and Challenges of Interactions Spanning Across Multiple Devices,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, in CHI ’19. New York, NY, USA: Association for Computing Machinery, Mai 2019, pp. 1–28. doi: 10.1145/3290605.3300792.


[5]      A. A. Salatino, T. Thanapalasingam, A. Mannocci, F. Osborne, and E. Motta, “The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas,” in Lecture Notes in Computer Science 1137, D. Vrandečić, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, and E. Simperl, Eds., Monterey, California, USA: Springer, Oct. 2018, pp. 187–205. Accessed: May 08, 2023. [Online]. Available: http://oro.open.ac.uk/55484/


[6]      M. Dehnert, A. Gleiss, and F. Reiss, “What makes a data-driven business model? A consolidated taxonomy,” presented at the European Conference on Information Systems, 2021.


[7]      DDI Alliance, “DDI Controlled Vocabulary for Aggregation Method,” 2014. https://ddialliance.org/Specification/DDI-CV/AggregationMethod_1.0.html (accessed May 08, 2023).


[8]      DDI Alliance, “DDI Controlled Vocabulary for Mode Of Collection,” 2015. https://ddialliance.org/Specification/DDI-CV/ModeOfCollection_2.0.html (accessed May 08, 2023).


[9]      INED - French Institute for Demographic Studies, “Thésaurus DemoVoc,” Feb. 26, 2020. https://thesaurus.web.ined.fr/navigateur/en/about (accessed May 08, 2023).


[10]    A. A. Bakar, Z. A. Othman, and N. L. M. Shuib, “Building a new taxonomy for data discretization techniques,” in 2009 2nd Conference on Data Mining and Optimization, Oct. 2009, pp. 132–140. doi: 10.1109/DMO.2009.5341896.


[11]    N. Brouard and C. Giudici, “Unified second edition of the Multilingual Demographic Dictionary (Demopaedia.org project),” presented at the 2017 International Population Conference, IUSSP, Oct. 2017. Accessed: May 08, 2023. [Online]. Available: https://iussp.confex.com/iussp/ipc2017/meetingapp.cgi/Paper/5713


[12]    DuCharme, Bob, “Data Science Glossary.” https://www.datascienceglossary.org/ (accessed May 08, 2023).


[13]    A. Chatzigeorgiou, T. Chaikalis, G. Paschalidou, N. Vesyropoulos, C. K. Georgiadis, and E. Stiakakis, “A Taxonomy of Evaluation Approaches in Software Engineering,” in Proceedings of the 7th Balkan Conference on Informatics Conference, in BCI ’15. New York, NY, USA: Association for Computing Machinery, Sep. 2015, pp. 1–8. doi: 10.1145/2801081.2801084.


[14]    M. C. Chibucos, D. A. Siegele, J. C. Hu, and M. Giglio, “The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations,” in The Gene Ontology Handbook, C. Dessimoz and N. Škunca, Eds., in Methods in Molecular Biology. New York, NY: Springer, 2017, pp. 245–259. doi: 10.1007/978-1-4939-3743-1_18.


[15]    M. Black et al., “EDAM: the bioscientific data analysis ontology,” F1000Research, vol. 11, Jan. 2021, doi: 10.7490/f1000research.1118900.1.


[16]    Council of European Social Science Data Archives (CESSDA), “European Language Social Science Thesaurus ELSST,” 2021. https://thesauri.cessda.eu/en/ (accessed May 08, 2023).


[17]    M. Scriven, Evaluation Thesaurus, 3rd Edition. Edgepress, 1981. Accessed: May 08, 2023. [Online]. Available: https://us.sagepub.com/en-us/nam/evaluation-thesaurus/book3562


[18]    Papantoniou, Bill et al., The Glossary of Human Computer Interaction. Interaction Design Foundation. Accessed: May 08, 2023. [Online]. Available: https://www.interaction-design.org/literature/book/the-glossary-of-human-computer-interaction


[19]    “UK Data Service Vocabularies: HASSET Thesaurus.” https://hasset.ukdataservice.ac.uk/hasset/en/ (accessed May 08, 2023).


[20]    S. D. Costa, M. P. Barcellos, R. de A. Falbo, T. Conte, and K. M. de Oliveira, “A core ontology on the Human–Computer Interaction phenomenon,” Data Knowl. Eng., vol. 138, p. 101977, Mar. 2022, doi: 10.1016/j.datak.2021.101977.


[21]    V. J. Gawron et al., “Human Factors Taxonomy,” Proc. Hum. Factors Soc. Annu. Meet., vol. 35, no. 18, pp. 1284–1287, Sep. 1991, doi: 10.1177/154193129103501807.


[22]    L. Onnasch and E. Roesler, “A Taxonomy to Structure and Analyze Human–Robot Interaction,” Int. J. Soc. Robot., vol. 13, no. 4, pp. 833–849, Jul. 2021, doi: 10.1007/s12369-020-00666-5.


[23]    R. A. Schwier, “A Taxonomy of Interaction for Instructional Multimedia.” Sep. 28, 1992. Accessed: May 09, 2023. [Online]. Available: https://eric.ed.gov/?id=ED352044


[24]    C. Kelly, J. Miller, A. Redlich, and S. Kleinman, “A Taxonomy of Interrogation Methods,” Psychol. Public Policy Law, vol. 19, p. 165, May 2013, doi: 10.1037/a0030310.


[25]    Y. Chuang, L.-L. Chen, and Y. Liu, “Design Vocabulary for Human-IoT Systems Communication,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal QC Canada: ACM, Apr. 2018, pp. 1–11. doi: 10.1145/3173574.3173848.


[26]    N. Díaz Rodríguez, R. Wikström, J. Lilius, M. P. Cuéllar, and M. Delgado Calvo Flores, “Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors,” in Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction, G. Urzaiz, S. F. Ochoa, J. Bravo, L. L. Chen, and J. Oliveira, Eds., in Lecture Notes in Computer Science, vol. 8276. Cham: Springer International Publishing, 2013, pp. 254–261. doi: 10.1007/978-3-319-03176-7_33.


[27]    “Thesaurus: mass communication - UNESCO Digital Library.” https://unesdoc.unesco.org/ark:/48223/pf0000015031 (accessed May 08, 2023).


[28]    Institute for Scientific and Technical Information, Thesaurus Cognitive Psychology of Human Memory, Version 2.0. 2021. Accessed: May 08, 2023. [Online]. Available: https://fairsharing.org/FAIRsharing.LcyXdU


[29]    S. Jiang and R. C. Arkin, “Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey,” in 2015 IEEE International Conference on Systems, Man, and Cybernetics, Oct. 2015, pp. 954–961. doi: 10.1109/SMC.2015.174.


[30]    S. Moller, K.-P. Engelbrecht, C. Kuhnel, I. Wechsung, and B. Weiss, “A taxonomy of quality of service and Quality of Experience of multimodal human-machine interaction,” in 2009 International Workshop on Quality of Multimedia Experience, Jul. 2009, pp. 7–12. doi: 10.1109/QOMEX.2009.5246986.


[31]    K. Belhajjame et al., “Using a suite of ontologies for preserving workflow-centric research objects,” J. Web Semant., vol. 32, pp. 16–42, May 2015, doi: 10.1016/j.websem.2015.01.003.


[32]    M. Augstein and T. Neumayr, “A Human-Centered Taxonomy of Interaction Modalities and Devices,” Interact. Comput., vol. 31, no. 1, pp. 27–58, Jan. 2019, doi: 10.1093/iwc/iwz003.


[33]    J. Jerald, “A Taxonomy of Spatial Interaction Patterns and Techniques,” IEEE Comput. Graph. Appl., vol. 38, no. 1, pp. 11–19, Jan. 2018, doi: 10.1109/MCG.2018.011461524.


[34]    L. Tian and S. Oviatt, “A Taxonomy of Social Errors in Human-Robot Interaction,” ACM Trans. Hum.-Robot Interact., vol. 10, no. 2, pp. 1–32, Jun. 2021, doi: 10.1145/3439720.


[35]    A. Haller, K. Janowicz, S. Cox, D. Phuoc, K. Taylor, and M. Lefrançois, Semantic Sensor Network Ontology. 2017.


[36]    J. Malone et al., “The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation,” J. Biomed. Semant., vol. 5, no. 1, p. 25, Jun. 2014, doi: 10.1186/2041-1480-5-25.


[37]    L. Borek, Q. Dombrowski, J. Perkins, and C. Schöch, “TaDiRAH: a Case Study in Pragmatic Classification,” Digit. Humanit. Q., vol. 010, no. 1, Feb. 2016.


[38]    M. A. Muhanna, “Virtual reality and the CAVE: Taxonomy, interaction challenges and research directions,” J. King Saud Univ. - Comput. Inf. Sci., vol. 27, no. 3, pp. 344–361, Jul. 2015, doi: 10.1016/j.jksuci.2014.03.023.


[39]    F. Scharf, C. Wolters, M. Herczeg, and J. Cassens, “Cross-Device Interaction: Definition, Taxonomy and Application,” presented at the AMBIENT 2013 : The Third International Conference on Ambient Computing, Applications, Services and Technologies, Porto, Portugal: IARIA, 2013, pp. 35–41. Accessed: May 08, 2023. [Online]. Available: https://www.imis.uni-luebeck.de/de/forschung/publikationen/6380