Abstract
Given the evolution of publicly available Linked Data, crawling and preservation have become increasingly important challenges. Due to the scale of available data on the Web, efficient focused crawling approaches which are able to capture the relevant semantic neighborhood of seed entities are required. Here, determining relevant entities for a given set of seed entities is a crucial problem. While the weight of seeds within a seed list vary significantly with respect to the crawl intent, we argue that an adaptive crawler is required, which considers such characteristics when configuring the crawling and relevance detection approach. To address this problem, we introduce a crawling configuration, which considers seed list-specific features as part of its crawling and ranking algorithm. We evaluate it through extensive experiments in comparison to a number of baseline methods and crawling parameters. We demonstrate that, configurations which consider seed list features outperform the baselines and present further insights gained from our experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In this case we pooled the \(Top-500\) entities resulting from all the different configurations for each seed list.
References
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998)
Chakrabarti, S., Punera, K., Subramanyam, M.: Accelerated focused crawling through online relevance feedback. In: Proceedings of the 11th International Conference on World Wide Web, WWW, pp. 148–159. ACM, New York (2002)
Chakrabarti, S., Van den Berg, M., Dom, B.: Focused crawling: a new approach to topic-specific web resource discovery. Comput. Netw. 31(11), 1623–1640 (1999)
De Bra, P., Houben, G.-J., Kornatzky, Y., Post, R.: Information retrieval in distributed hypertexts. In: RIAO, pp. 481–493 (1994)
Diligenti, M., Coetzee, F., Lawrence, S., Giles, C.L., Gori, M., et al.: Focused crawling using context graphs. In: VLDB, pp. 527–534 (2000)
Fetahu, B., Gadiraju, U., Dietze, S.: Crawl me maybe: iterative linked dataset preservation. In: Proceedings of the 13th International Semantic Web Conference (ISWC) Posters & Demonstrations Track, pp. 433–436 (2014)
Fetahu, B., Gadiraju, U., Dietze, S.: Improving entity retrieval on structured data. In: Proceedings of the 14th International Semantic Web Conference. Springer (2015)
Gadiraju, U., Demartini, G., Kawase, R., Dietze, S.: Human beyond the machine: challenges and opportunities of microtask crowdsourcing. IEEE Intell. Syst. 30(4), 81–85 (2015)
Gadiraju, U., Kawase, R., Dietze, S., Demartini, G.: Understanding malicious behaviour in crowdsourcing platforms: the case of online surveys. In: Proceedings of CHI 2015 (2015)
Isele, R., Umbrich, J., Bizer, C., Harth, A.: Ldspider: an open-source crawling framework for the web of linked data. In 9th International Semantic Web Conference, ISWC. Citeseer (2010)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
McCallumzy, A., Nigamy, K., Renniey, J., Seymorey, K.: Building domain-specific search engines with machine learning techniques (1999)
Meusel, R., Mika, P., Blanco, R.: Focused crawling for structured data. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, CIKM, pp. 1039–1048 (2014)
Pereira Nunes, B., Dietze, S., Casanova, M.A., Kawase, R., Fetahu, B., Nejdl, W.: Combining a co-occurrence-based and a semantic measure for entity linking. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 548–562. Springer, Heidelberg (2013)
Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) WWW, pp. 771–780. ACM (2010)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Tang, T.T., Hawking, D., Craswell, N., Griffiths, K.: Focused crawling for both topical relevance and quality of medical information. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 147–154. ACM (2005)
Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 319–326. ACM (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yu, R., Gadiraju, U., Fetahu, B., Dietze, S. (2015). Adaptive Focused Crawling of Linked Data. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-26190-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26189-8
Online ISBN: 978-3-319-26190-4
eBook Packages: Computer ScienceComputer Science (R0)