Top

International Journal on Digital Libraries

Published in:

19-07-2017

Retrieval by recommendation: using LOD technologies to improve digital library search

Authors: Lisa Wenige, Johannes Ruhland

Published in: International Journal on Digital Libraries | Issue 2-3/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper investigates how Linked Open Data (LOD) can be used for recommendations and information retrieval within digital libraries. While numerous studies on both research paper recommender systems and Linked Data-enabled recommender systems have been conducted, no previous attempt has been undertaken to explore opportunities of LOD in the context of search and discovery interfaces. We identify central advantages of Linked Open Data with regard to scientific search and propose two novel recommendation strategies, namely flexible similarity detection and constraint-based recommendations. These strategies take advantage of key characteristics of data that adheres to LOD principles. The viability of Linked Data recommendations was extensively evaluated within the scope of a web-based user experiment in the domain of economics. Findings indicate that the proposed methods are well suited to enhance established search functionalities and are thus offering novel ways of resource access. In addition to that, RDF triples from LOD repositories can complement local bibliographic records that are sparse or of poor quality.

previous article Investigating exploratory search activities based on the stratagem level in digital libraries

next article Extracting discourse elements and annotating scientific documents using the SciAnnotDoc model: a use case in gender documents

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

http://linkeddatacatalog.dws.informatik.uni-mannheim.de/state/.

https://www.econstor.eu/.

http://www.w3.org/2005/Incubator/lld.

https://datahub.io/de/dataset?q=lld.

http://linkeddatacatalog.dws.informatik.uni-mannheim.de/state.

https://www.econstor.eu/about.

http://lucene.apache.org/solr/.

Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User. Model. User-Adap. Inter. 22, 317–355 (2012). doi:10.1007/s11257-011-9115-7 CrossRef

Lam, S.K., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Müller, G. (ed.) Emerging trends in information and communication security, pp. 14–29. Springer, Berlin (2006). doi:10.1007/11766155_2 CrossRef

Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22, 5–53 (2004). doi:10.1145/963770.963772 CrossRef

Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to expect the unexpected. ACM Trans. Intell. Syst. Technol. (TIST) (2011). doi:10.1145/2559952

Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the 4th ACM Conference on Recommender Systems (RecSys ’10), pp. 257–260 (2010). doi:10.1145/1864708.1864761

Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web (WWW ’05), pp. 22–32 (2005). doi:10.1145/1060745.1060754

Adomavicius, G., Kwon, Y.: Toward more diverse recommendations: item re-ranking methods for recommender systems. In: Proceedings of the 19th Workshop on Information Technology and Systems (WITS ’09) (2009)

McNee, S.M., Riedl, J., Konstan, J.A. (2006) Being accurate is not enough : how accuracy metrics have hurt recommender systems. Ext. Abs. Human Factors Comput. Syst. doi:10.1145/1125451.1125659

Swearingen, K., Sinha, R.: Beyond algorithms : an HCI perspective on recommender systems. In: Proceedings of the ACM SIGIR 2001 Workshop on Recommender Systems (2001)

10.

Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11), pp. 157–164. doi:10.1145/2043932.2043962 (2011)

11.

Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is seeing believing?: how recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 585–592 (2003). doi:10.1145/642611.642713

12.

Beel, J., Gipp, B., Langer, S.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17, 305–338 (2016). doi:10.1007/s00799-015-0156-0 CrossRef

13.

Castells, P., Vargas, S., Wang, J.: Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In: International Workshop on Diversity in Document Retrieval (DDR 2011) at the 33rd European Conference on Information Retrieval (ECIR 2011) (2011)

14.

Beel, J., Breitinger, C., Langer, S., Lommatzsch, A., Gipp, B.: Towards reproducibility in recommender-systems research. User. Model. User-Adap. Inter. 26, 69–101 (2016). doi:10.1007/s11257-016-9174-x CrossRef

15.

Hersh, W., Turpin, A., Price, S., Chan, B., Kramer, D., Sacherek, L., Olson, D.: Do batch and user evaluations give the same results? In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’00), pp. 17–24 (2000). doi:10.1145/345508.345539

16.

Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)CrossRefMATH

17.

Ruthven, I., Lalmas, M., Van Rijsbergen, K.: Incorporating user search behavior into relevance feedback. J. Am. Soc. Inf. Sci. Technol. 54, 529–549 (2003)CrossRef

18.

Rui, Y., Huang, T.S.: A novel relevance feedback technique in image retrieval. In: Proceedings of the 7th ACM International Conference on Multimedia (Multimedia ’99), pp. 67–70 (1999). doi:10.1145/319878.319896

19.

Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 296–304 (1998)

20.

Neubert, J.: Bringing the ’thesaurus for economics’ on to the web of linked data. In: Proceedings of the Linked Data on the Web Workshop (LDOW ’2009) (2009)

21.

Kirk, R.E.: Experimental design-procedures for the behavioral sciences. Sage, Los Angeles (2013)CrossRefMATH

22.

Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinform 30, 740–742 (2014)CrossRef

23.

Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’06), pp. 19–26 (2006). doi:10.1145/1148170.1148177

24.

Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20, 422–446 (2002). doi:10.1145/582415.582418 CrossRef

25.

El Demerdash, O., Bergler, S., Kosseim, L., Langshaw, P.K.: Developing AMIE: an adaptive multimedia integrated environment. In: International Workshop on Adaptive Multimedia Retrieval, pp. 65–78 (2005). doi:10.1007/11670834_6

26.

Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. Semant. Serv., Interoper. Web Appli.: Emer. Concepts, pp. 205–227 (2009)

27.

Mönnich, M., Spiering, M.: Adding value to the library catalog by implementing a recommendation system. D-Lib Magazine 14 (2008)

28.

Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (WWW ’01), pp. 285–295 (2001). doi:10.1145/371920.372071

29.

Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005). doi:10.1109/TKDE.2005.99 CrossRef

30.

Yang, C., Wei, B., Wu, J., Zhang, Y., Zhang, L.: CARES: a ranking-oriented CADAL recommender system. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL ’09), pp. 203–212 (2009). doi:10.1145/1555400.1555432

31.

Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems (I-SEMANTICS ’12) (2012). doi:10.1145/2362499.2362501

32.

Wenige, L., Ruhland, J.: Flexible on-the-fly recommendations from linked open data repositories. In: Proceedings of the International Conference on Business Information Systems (BIS 2016), pp. 43–54 (2016). doi:10.1007/978-3-319-39426-8_4

33.

Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. ACM Comput. Surv. (CSUR) (2012). doi:10.1145/2071389.2071390 MATH

34.

Hajra, A., Latif, A., Tochtermann, K.: Retrieving and ranking scientific publications from linked open data repositories. In: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business (i-KNOW ’14) (2014). doi:10.1145/2637748.2638436

35.

Shekarpour, S., Höffner, K., Lehmann, J., Auer, S.: Keyword query expansion on linked data using linguistic and semantic features. In: Proceedings of the IEEE 7th International Conference on Semantic Computing (ICSC), pp. 191–197 (2013). doi:10.1109/ICSC.2013.41

36.

Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D.: Exploiting the web of data in model-based recommender systems. In: Proceedings of the 6th ACM Conference on Recommender Systems, pp. 253–256 (2012). doi:10.1145/2365952.2366007

37.

Ruotsalo, T., Haav, K., Stoyanov, A., Roche, S., Fani, E., Deliai, R., Mäkelä, E., Kauppinen, T., Hyvönen, E.: SMARTMUSEUM: a mobile recommender system for the web of data. Web Semant.: Sci., Serv. Agents World Wide Web 20, 50–67 (2013)CrossRef

38.

Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics (ACL ’94), pp. 133–138 (1994). doi:10.3115/981732.981751

39.

Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. Accessed 21 January 2017 arXiv preprint arXiv:cmp-lg/9511007. (1995)

40.

Kules, B., Capra, R.: Influence of training and stage of search on gaze behavior in a library catalog faceted search interface. J. Am. Soc. Inf. Sci. Technol. 63, 114–138 (2012). doi:10.1002/asi.21647 CrossRef

41.

Wakeling, S., Clough, P., Sen, B.: Investigating the potential impact of non-personalized recommendations in the OPAC: Amazon vs. WorldCat.org. In: Proceedings of the 5th Information Interaction in Context Symposium (IIiX ’14 ), pp. 96–105 (2014). doi:10.1145/2637002.2637015

42.

Ballard, T., Blaine, A.: User search-limiting behavior in online catalogs: comparing classic catalog use to search behavior in next-generation catalogs. New Lib. World 112, 261–273 (2011). doi:10.1108/03074801111136293 CrossRef

43.

Kules, B., Capra, R., Banta, M., Sierra, T.: What do exploratory searchers look at in a faceted search interface? In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL ’09), pp. 313–322 (2009). doi:10.1145/1555400.1555452

44.

Antell, K., Huang, J.: Subject searching success: transaction logs, patron perceptions, and implications for library instruction. Ref. Us. Serv. Q. 48, 68–76 (2008)

45.

Yang, S.Q., Wagner, K.: Evaluating and comparing discovery tools: how close are we towards next generation catalog? Lib. Hi Tech 28, 690–709 (2010)CrossRef

46.

Konstan, J.A., Kapoor, N., McNee, S.M., Butler, J.T.: Techlens: Exploring the use of recommenders to support users of digital libraries. CNI Fall Task Force Meeting Project Briefing. Accessed 21 January 2017 (2005)

47.

Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22, 54–88 (2004). doi:10.1145/963770.963773 CrossRef

48.

Porcel, C., Moreno, J.M., Herrera-Viedma, E.: A multi-disciplinar recommender system to advice research resources in university digital libraries. Exp. Syst. Appli. 36, 12520–12528 (2009). doi:10.1016/j.eswa.2009.04.038 CrossRef

49.

Vellino, A.: Recommending research articles using citation data. Lib. Hi Tech 33, 597–609 (2015). doi:10.1108/LHT-06-2015-0063 CrossRef

50.

Webster, J., Jung, S., Herlocker, J.: Collaborative filtering: a new approach to searching digital libraries. New Rev. Inform. Netw. 10, 177–191 (2004). doi:10.1080/13614570500054229 CrossRef

51.

Kabutoya, Y., Sumi, R., Iwata, T., Uchiyama, T., Uchiyama, T.A.: Topic model for recommending movies via linked open data. In: Proceedings of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 625–630 (2012). doi:10.1109/WI-IAT.2012.23

52.

Ostuni, V.C., Di Noia, T., Mirizzi, R., Romito, D., Di Sciascio, E.: Cinemappy: a context-aware mobile app for movie recommendations boosted by DBpedia. In: Proceedings of the 2012 International Conference on Semantic Technologies Meet Recommender Systems & Big Data (SeRSy ’12), pp. 37–48 (2012)

53.

Passant, A.: dbrec - music recommendations using dbpedia. In: Proceedings of the 9th International Semantic Web Conference, (ISWC 2010), pp. 209–224 (2010). doi:10.1007/978-3-642-17749-1_14

54.

Peska, L., Vojtas, P. (2013) Enhancing recommender system with linked open data. Flex. Query Answ. Syst. doi:10.1007/978-3-642-40769-7_42

55.

Celma, Ò., Serra, X.: FOAFing the music: bridging the semantic gap in music recommendation. In: Proceedings of the 5th International Semantic Web Conference (ISWC 2006), pp. 927-934 (2008). doi:10.1007/11926078_67

56.

Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. Web Mining: From Web to Semantic Web, pp. 57–76 (2004). doi:10.1007/978-3-540-30123-3_4

57.

Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13 ), pp. 85–92 (2013). doi:10.1145/2507157.2507172

58.

Heitmann, B., Hayes, C.: Using linked data to build open, collaborative recommender systems. AAAI Spring Symp.: Link. Data Meets Artif. Intelli., pp. 76–81 (2010)

59.

Ziegler, C.N.: Semantic web recommender systems. Current Trends Data. Technology-EDBT 2004 Workshops, pp. 78–89 (2005). doi:10.1007/978-3-540-30192-9_8

60.

Summers, E., Isaac, A., Redding, C., Krech, D.: LCSH, SKOS and linked data. In: Proceedings of the International Conference on Dublin Core and Metadata Applications, pp. 25–33 (2008)

61.

Tuominen, J., Frosterus, M., Viljanen, K., Hyvönen, E.: ONKI SKOS server for publishing and utilizing SKOS vocabularies and ontologies as services. In: Proceedings of the 6th European Semantic Web Conference (ESWC 2009), pp. 768–780 (2009). doi:10.1007/978-3-642-02121-3_56

62.

Neubert, J., Tochtermann, K.: Linked library data: offering a backbone for the semantic web. In: Lukose D, Ahmad AR, Suliman A (ed) Knowledge Technology, Springer, Berlin pp. 37–45. doi:10.1007/978-3-642-32826-8_4

63.

Caracciolo, C., Stellato, A., Rajbahndari, S., Morshed, A., Johannsen, G., Jaques, Y., Keizer, J.: Thesaurus maintenance, alignment and publication as linked data: the AGROVOC use case. Inter. J. Metadata, Seman. Ontolog. 7(1), 65–75 (2012). doi:10.1504/IJMSO.2012.048511 CrossRef

64.

Liao, I., Hsu, W., Cheng, M., Chen, L.: A library recommender system based on a personal ontology model and collaborative filtering technique for English collections. Electron. Lib. 28, 386–400 (2010). doi:10.1108/02640471011051972 CrossRef

65.

Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries (DL ’00), pp. 195–204 (2004). doi:10.1145/336597.336662

66.

Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in WordNet. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI ’04) (2004)

67.

Zapilko, B., Schaible, J., Mayr, P., Mathiak, B.: TheSoz: A SKOS representation of the thesaurus for the social sciences. Semant. Web 4(3), 257–263 (2013)

68.

Mayr, P., Petras, V.: Cross-concordances: terminology mapping and its effectiveness for information retrieval. In: Proceedings of the 74th IFLA World Library and Information Congress (2008)

Title: Retrieval by recommendation: using LOD technologies to improve digital library search
Authors: Lisa Wenige
Johannes Ruhland
Publication date: 19-07-2017
Publisher: Springer Berlin Heidelberg
Published in: International Journal on Digital Libraries / Issue 2-3/2018
Print ISSN: 1432-5012
Electronic ISSN: 1432-1300
DOI: https://doi.org/10.1007/s00799-017-0224-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2-3/2018

The references of references: a method to enrich humanities library catalogs with citation data

Scientific document summarization via citation contextualization and scientific discourse

The context of multiple in-text references and their signification

Identifying reference spans: topic modeling and word embeddings help IR

Introduction to the special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL)

Insights from CL-SciSumm 2016: the faceted scientific document summarization Shared Task

Premium Partner