Abstract
Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies’ occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies’ popularity, resulting in ineffective ranking scores. This motivated us to investigate – based on the problematic WoT case – whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies’ popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Extracted from the LOV SPARQL endpoint: https://lov.linkeddata.es/dataset/lov/sparql – accessed 03/2019.
- 2.
- 3.
- 4.
- 5.
Accessed 03/2019.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
Denoted by \(\texttt {<}\)http://sensormeasurement.appspot.com/m3#hasContext\(\texttt {>}\).
- 13.
Denoted by \(\texttt {<}\)http://sensormeasurement.appspot.com/m3#hasM2MDevice\(\texttt {>}\).
- 14.
- 15.
- 16.
Supplemental material: https://tinyurl.com/y64sa6le.
References
Andročec, D., Novak, M., Oreški, D.: Using semantic web for internet of things interoperability: a systematic review. Int. J. Semant. Web Inf. Syst. (IJSWIS) 14(4), 147–171 (2018). https://doi.org/10.4018/IJSWIS.2018100108
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010). https://doi.org/10.1016/j.comnet.2010.05.010
Bakerally, N., Boissier, O., Zimmermann, A.: Smart city artifacts web portal. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 172–177. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47602-5_34
Barnaghi, P., Wang, W., Henson, C., Taylor, K.: Semantics for the internet of things: early progress and back to the future. Int. J. Semant. Web Inf. Syst. (IJSWIS) 8(1), 1–21 (2012). https://doi.org/10.4018/jswis.2012010101
Burges, C.J.: From ranknet to lambdarank to lambdamart: an overview. Learning 11(23–581), 81 (2010)
Butt, A.S.: Ontology search: finding the right ontologies on the web. In: Proceedings of the 24th International Conference on World Wide Web, pp. 487–491. ACM (2015). https://doi.org/10.1145/2740908.2741753
Butt, A.S., Haller, A., Xie, L.: Ontology search: an empirical evaluation. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 130–147. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11915-1_9
Butt, A.S., Haller, A., Xie, L.: DWRank: learning concept ranking for ontology search. Semant. Web 7(4), 447–461 (2016). https://doi.org/10.3233/SW-150185
Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 621–630. ACM (2009). https://doi.org/10.1145/1645953.1646033
Espinoza-Arias, P., Poveda-Villalón, M., García-Castro, R., Corcho, O.: Ontological representation of smart city data: from devices to cities. Appl. Sci. 9(1), 32 (2019). https://doi.org/10.3390/app9010032
Fernández-López, M., Suárez-Figueroa, M.C., Gómez-Pérez, A.: Ontology development by reuse. In: Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A. (eds.) Ontology Engineering in a Networked World, pp. 147–170. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24794-1_7
Gyrard, A., Bonnet, C., Boudaoud, K., Serrano, M.: Lov4iot: a second life for ontology-based domain knowledge to build semantic web of things applications. In: IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 254–261. IEEE (2016). https://doi.org/10.1109/FiCloud.2016.44
Gyrard, A., Zimmermann, A., Sheth, A.: Building IoT-based applications for smart cities: how can ontology catalogs help? IEEE Internet Things J. 5(5), 3978–3990 (2018). https://doi.org/10.1109/JIOT.2018.2854278
Katsumi, M., Grüninger, M.: Choosing ontologies for reuse. Appl. Ontol. 12(3–4), 195–221 (2017). https://doi.org/10.3233/AO-160171
Kolbe, N., Kubler, S., Robert, J., Le Traon, Y., Zaslavsky, A.: Linked vocabulary recommendation tools for internet of things: a survey. ACM Comput. Surv. (CSUR) 51(6), 127 (2019). https://doi.org/10.1145/3284316
Kolchin, M., et al.: Ontologies for web of things: a pragmatic review. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2015. CCIS, vol. 518, pp. 102–116. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24543-0_8
Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3(3), 225–331 (2009). https://doi.org/10.1007/978-3-642-14267-3
Martínez-Romero, M., Jonquet, C., O’Connor, M.J., Graybeal, J., Pazos, A., Musen, M.A.: NCBO ontology recommender 2.0: an enhanced approach for biomedical ontology recommendation. J. Biomed. Semant. 8(1), 21 (2017). https://doi.org/10.1186/s13326-017-0128-y
McCandless, M., Hatcher, E., Gospodnetic, O.: Lucene in Action: Covers Apache Lucene 3.0. Manning Publications Co., Shelter Island (2010). ISBN 1933988177
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report 1999-66, Stanford InfoLab (1999)
Poveda Villalón, M., García Castro, R., Gómez-Pérez, A.: Building an ontology catalogue for smart cities, pp. 829–839. CRC Press (2014)
Robertson, S.E.: Overview of the Okapi projects. J. Doc. 53(1), 3–7 (1997). https://doi.org/10.1108/EUM0000000007186
Sabou, M., Lopez, V., Motta, E., Uren, V.: Ontology selection: ontology evaluation on the real semantic web. In: 4th International Workshop on Evaluation of Ontologies for the Web (2006)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988). https://doi.org/10.1016/0306-4573(88)90021-0
Schaible, J., Gottron, T., Scherp, A.: Survey on common strategies of vocabulary reuse in linked open data modeling. In: Presutti, V., et al. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_31
Schaible, J., Gottron, T., Scherp, A.: TermPicker: enabling the reuse of vocabulary terms by exploiting data from the linked open data cloud. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 101–117. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34129-3_7
Simperl, E.: Reusing ontologies on the semantic web: a feasibility study. Data Knowl. Eng. 68(10), 905–925 (2009). https://doi.org/10.1016/j.datak.2009.02.002
Stadtmüller, S., Harth, A., Grobelnik, M.: Accessing information about linked data vocabularies with vocab.cc. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, H.T. (eds.) Semantic Web and Web Science. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6880-6_34
Stavrakantonakis, I., Fensel, A., Fensel, D.: Linked open vocabulary ranking and terms discovery. In: Proceedings of the 12th International Conference on Semantic Systems, pp. 1–8. ACM (2016). https://doi.org/10.1145/2993318.2993338
Vandenbussche, P.Y., Atemezing, G.A., Poveda-Villalón, M., Vatant, B.: Linked open vocabularies (LOV): a gateway to reusable semantic vocabularies on the web. Semant. Web 8(3), 437–452 (2017). https://doi.org/10.3233/SW-160213
Wu, G., Li, J., Feng, L., Wang, K.: Identifying potentially important concepts and relations in an ontology. In: Sheth, A., et al. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 33–49. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88564-1_3
Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality assessment for linked data: a survey. Semant. Web 7(1), 63–93 (2016). https://doi.org/10.3233/SW-150175
Acknowledgements
The research leading to this publication is supported by the EU’s H2020 Research and Innovation program under grant agreement № 688203 – bIoTope.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kolbe, N., Kubler, S., Le Traon, Y. (2019). Popularity-Driven Ontology Ranking Using Qualitative Features. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-30793-6_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30792-9
Online ISBN: 978-3-030-30793-6
eBook Packages: Computer ScienceComputer Science (R0)