Abstract
In this chapter, we will analyze a number of essential knowledge engineering activities that, for technical or principled reasons, can hardly be optimally executed through automatic processing approaches, thus remaining heavily reliant on human intervention. Human computation methods can be applied to this field in order to overcome these limitations in terms of accuracy, while still being able to fully take advantage of the scalability and performance of machine-driven capabilities. For each activity, we will explain how this symbiosis can be achieved by giving a short overview of the state of the art and several examples of systems and applications such as games-with-a-purpose, microtask crowdsourcing projects, and community-driven collaborative initiatives that showcase the benefits of the general idea.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Especially in tasks like data annotation or data quality assessment, which involve defining and encoding the meaning of the resources published on the Web or resolving semantic conflicts such as data ambiguity or inconsistency.
- 2.
A similar process model applies to the creation, management and use of instance data. Management and pre-development activities cover the entire scope of the knowledge-engineering exercise. Development, post-development and support activities are equally relevant to both schema and data, though there might be differences in their actual realization. For example, instance data is typically lifted from existing sources into the newly created ontological schema, while a greater share of activities at the ontology level are carried out manually.
- 3.
Exceptions include highly contextualized systems, which require extensive training and/or background knowledge. In these cases, the manual efforts shifts from the creation and maintenance of the knowledge base to the generation of training data sets and background corpora.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
WordNet: http://wordnet.princeton.edu/, Freebase: http://www.freebase.com/
- 10.
- 11.
- 12.
- 13.
- 14.
- 15.
Amazon Mechanical Turk: http://mturk.com, CrowdFlower: http://crowdflower.com
- 16.
- 17.
- 18.
- 19.
- 20.
Wikipedia extractors. http://wiki.dbpedia.org/DeveloperDocumentation/Extractor
- 21.
- 22.
References
Acosta M, Simperl E, Flöck F, Norton B (2012) A sparql engine for crowdsourcing query processing using microtasks – technical report. Institute AIFB, KIT, Karlsruhe
Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43
Bloehdorn S, Petridis K, Saathoff C, Simou N, Tzouvaras V, Avrithis Y, Handschuh S, Kompatsiaris Y, Staab S, Strintzis MG (2005) Semantic annotation of images and videos for multimedia analysis. LNCS. Springer The Semantic Web: Research and Applications, volume 3532 of Lecture Notes in Computer Science, pp 592–607. Springer Berlin Heidel-berg
Bouquet P, Scefor S, Serafini L, Zanobini S (2006) Booststrapping semantics on the web: meaning elicitation from schemas. In: 15th international conference on ACM, New York, NY, USA, http://doi.acm.org/10.1145/1135777.1135851, pp 505–512
Buitelaar P, Cimiano P (2008) Ontology learning and population: bridging the gap between text and knowledge. IOS-Press, AmsterdamCelino, Irene, Simone Contessa, Marta Corubolo, Daniele Dell’Aglio, Emanuele Della Valle, Stefano Fumeo, Thorsten Krüger, and Thorsten Krüger. “UrbanMatch-linking and improving Smart Cities Data.” In LDOW. 2012
Demartini G, Difallah DE, Cudré-Mauroux P (2012) Zencrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: Proceedings of the 21st international conference on world wide web, WWW’12, Lyon. ACM, New York, pp 469–478
Dimitrov M, Simov A, Momtchev V, Konstantinov M (2007) Wsmo studio – a semantic web services modelling environment for wsmo (system description). In: European semantic web conference (ESWC 2007), Innsbruck
Eckert K, Niepert M, Niemann C, Buckner C, Allen C, Stuckenschmidt H (2010) Crowdsourcing the assembly of concept hierarchies. In: Proceedings of the 10th annual joint conference on digital libraries, JCDL ’10, Gold Coast. ACM, New York, pp 139–148
Euzenat J, Shvaiko P (2007) Ontology matching. Springer, Berlin/New York
Euzenat J, Mocan A, Scharffe F (2007) Ontology alignments. Volume 6 of semantic web and beyond. Springer, p 350
Falconer SM, Storey M-A (2007) A cognitive support framework for ontology mapping. In: Asian semantic web conference (ASWC 2007), Busan
Fensel D (2001) Ontologies: a silver bullet for knowledge management and electronic commerce. Springer, Berlin/New York
Gómez-Pérez A, Fernández-Lopéz M, Corcho O (2004) Ontological engineering – with examples form the areas of knowledge management, e-Commerce and the semantic web. Advanced information and knowledge processing. Springer
Guarino N (1998) Formal ontology and information systems. In: Proceedings of the 1st international conference on formal ontologies in information systems FOIS1998, Amsterdam/Washington. IOS-Press, pp 3–15
Kerrigan M, Mocan A, Tanler M, Fensel D (2007) The web service modeling toolkit – an integrated development environment for semantic web services (system description). In: European semantic web conference (ESWC 2007), Innsbruck
Kerrigan M, Mocan A, Simperl E, Fensel D (2008) Modeling semantic web services with the web service modeling toolkit. Technical report, Semantic Technology Institute (STI)
Kuo Y-L, Lee J-C, Chiang K-Y, Wang R, Shen E, Chan C-W, Hsu J-Y (2009) Community-based game design: experiments on social games for commonsense data collection. In: International conference on knowledge discovery and data mining, HCOMP’09, Paris. ACM, New York, pp 15–22
Neches R, Fikes RE, Finin T, Gruber TR, Senator T, Swartout WR (1991) Enabling technology for knowledge sharing. AI Mag 12(3):35–56
Niepert M, Buckner C, Allen C (2007) A dynamic ontology for a dynamic reference work. In: Proceedings of the 7th ACM/IEEE-CS joint conference on digital libraries, JCDL ’07, Vancouver. ACM, New York, pp 288–297
Noy N, Musen M (2001) Anchor-prompt: using non-logical context for semantic matching. In: IJCAI workshop on ontologies and information sharing, Seattle, pp 63–70
Noy NF, Musen M (2003) The prompt suite: interactive tools for ontology merging and mapping. Int J Hum Comput Stud 59(6):983–1024
Reeve L, Han H (2005) Survey of semantic annotation platforms. ACM Press, New York, pp 1634–1638
Sarasua C, Simperl E, Noy NF (2012) Crowdmap: crowdsourcing ontology alignment with microtasks. In: International Semantic Web Conference (1)’12, Boston. pp 525–541
Schreiber G, Akkermans H, Anjewierden A, de Hoog R, Shadbolt N, Van de Velde W, Wielinga B (1999) Knowledge engineering and management: the CommonKADS methodology. MIT, Cambridge http://proton.semanticweb.org
SEKT Consortium. Proton ontology
Simperl E, Wölger S, Norton B, Thaler S, Bürger T (2012) Combining human and computational intelligence: the case of data interlinking tools. Int J Metadata Semant Ontol 7.2 (2012):77–92
Simperl E, Cuel R, Stein M (2013) Incentive-Centric semantic web application engineering. Synthesis lectures on the semantic web: theory and technology. Morgan & Claypool, San Rafael
Siorpaes K, Hepp M (2008) Ontogame: weaving the semantic web by online games The Semantic Web: Research and Applications, volume 5021 of Lecture Notes in Computer Science, pp 751–766. Springer Berlin Heidelberg, 2008
Siorpaes K, Simperl E (2010) Human intelligence in the process of semantic content creation. World Wide Web J 13(1):33–59
Studer R, Benjamins VR, Fensel D (1998) Knowledge engineering principles and methods. Data Knowl Eng 25(1/2):161–197
Uren V, Cimiano P, Iria J, Handschuh S, Vargas-Vera M, Motta E, Ciravegna F (2006) Semantic annotation for knowledge management: requirements and a survey of the state of the art. Web Semant Sci Serv Agents World Wide Web 4(1):14–28
Völker J, Vrandecic D, Sure Y, Hotho A (2007) Learning disjointness. In: Proceedings of the 4th european semantic web conference ESWC 2012, Innsbruck, pp 175–189
Wölger S, Siorpaes K, Bürger T, Simperl E, Thaler S, Hofer C (2011) Interlinking data – approaches and tools. Technical report, STI Innsbruck, University of InnsbruckYen-ling Kuo et al. (2009): In Proceedings of the ACM SIGKDD Workshop on Human Computation (HCOMP ‘09)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Simperl, E., Acosta, M., Flöck, F. (2013). Knowledge Engineering via Human Computation. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_13
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8806-4_13
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8805-7
Online ISBN: 978-1-4614-8806-4
eBook Packages: Computer ScienceComputer Science (R0)