Abstract
Designing keyword-based access paths is a common practice in digital libraries. They are easy to use and accepted by users and come with moderate costs for content providers. However, users usually have to break down the search into pieces if they search for stories of interest that are more complex than searching for a few keywords. After searching for every piece one by one, information must then be reassembled manually. In previous work we recommended narrative information access, i.e., users can precisely state their information needs as graph patterns called narratives. Then a system takes a narrative and searches for evidence for each of its parts. If the whole query, i.e., every part, can be bound against data, the narrative is considered plausible and, thus, the query is answered. But is it as easy as that? In this work we perform case studies to analyze the process of making a given narrative plausible. Therefore, we summarize conceptual problems and challenges to face. Moreover, we contribute a set of dimensions that must be considered when realizing narrative information access in digital libraries.
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References
Plastics and the Environment. https://www.genevaenvironmentnetwork.org/resources/updates/plastics-and-the-environment/. Accessed May 2022
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
Azzarello, M.Y., Vleet, E.S.V.: Marine birds and plastic pollution. Mar. Ecol. Prog. Ser. 37(2/3), 295–303 (1987). http://www.jstor.org/stable/24824704
Bank, W.: Forest area (% of land area). https://data.worldbank.org/indicator/AG.LND.FRST.ZS. Accessed 25 May 2022
Blakeskee, S.: The CRAAP test. LOEX Quart. 31, 4(2004). https://commons.emich.edu/loexquarterly/vol31/iss3/4
Blatz, J., et al.: Confidence estimation for machine translation. In: COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics, pp. 315–321. COLING, Geneva, Switzerland, 23–27 August 2004. https://aclanthology.org/C04-1046
Carroll, J.J., Bizer, C., Hayes, P.J., Stickler, P.: Named graphs. J. Web Semant. 3(4), 247–267 (2005). https://doi.org/10.1016/j.websem.2005.09.001
Chapman, A., et al.: Dataset search: a survey. VLDB J. 29(1), 251–272 (2019). https://doi.org/10.1007/s00778-019-00564-x
Clark, J.R., et al.: Marine microplastic debris: a targeted plan for understanding and quantifying interactions with marine life. Front. Ecol. Environ. 14(6), 317–324 (2016). http://www.jstor.org/stable/44001167
Elbassuoni, S., Blanco, R.: Keyword search over RDF graphs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 237–242. CIKM 2011. Association for Computing Machinery, New York, NY, USA (2011). https://doi.org/10.1145/2063576.2063615
Fu, Y., Schneider, J.: Towards knowledge maintenance in scientific digital libraries with the keystone framework. In: Huang, R., Wu, D., Marchionini, G., He, D., Cunningham, S.J., Hansen, P. (eds.) JCDL 2020: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, Virtual Event, China, 1–5 August 2020, pp. 217–226. ACM (2020). https://doi.org/10.1145/3383583.3398514
Gandrabur, S., Foster, G.F., Lapalme, G.: Confidence estimation for NLP applications. ACM Trans. Speech Lang. Process. 3(3), 1–29 (2006). https://doi.org/10.1145/1177055.1177057
Group, W.W.: PROV-overview. an overview of the PROV family of documents (2013). https://www.w3.org/TR/prov-overview/
Guo, Z., Schlichtkrull, M., Vlachos, A.: A survey on automated fact-checking. Trans. Assoc. Comp Linguist. (TACL) 10, 178–206 (2022)
Han, S., Zou, L., Yu, J.X., Zhao, D.: Keyword search on RDF graphs - a query graph assembly approach. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 227–236. CIKM 2017. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3132847.3132957
Haslhofer, B., Isaac, A.: data.europeana.eu: the Europeana linked open data pilot. In: Baker, T., Hillmann, D.I., Isaac, A. (eds.) Proceedings of the 2011 International Conference on Dublin Core and Metadata Applications, DC 2011, The Hague, The Netherlands, 21–23 September 2011, pp. 94–104. Dublin Core Metadata Initiative (2011). http://dcpapers.dublincore.org/pubs/article/view/3625
Jambeck, J.R., et al.: Plastic waste inputs from land into the ocean. Science 347(6223), 768–771 (2015). https://doi.org/10.1126/science.1260352
Jaradeh, M.Y., et al.: Open research knowledge graph: Next generation infrastructure for semantic scholarly knowledge. In: Kejriwal, M., Szekely, P.A., Troncy, R. (eds.) Proceedings of the 10th International Conference on Knowledge Capture, K-CAP 2019, Marina Del Rey, CA, USA, 19–21 November 2019, pp. 243–246. ACM (2019). https://doi.org/10.1145/3360901.3364435
Jin, Q., et al.: Biomedical question answering: a survey of approaches and challenges. ACM Comput. Surv. 55(2), 1–36 (2022). https://doi.org/10.1145/3490238
Khot, T., Sabharwal, A., Clark, P.: Answering complex questions using open information extraction. In: Barzilay, R., Kan, M. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July – 4 August, Volume 2: Short Papers, pp. 311–316. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-2049
Kilicoglu, H., Shin, D., Fiszman, M., Rosemblat, G., Rindflesch, T.C.: SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics 28(23), 3158–3160 (2012). https://doi.org/10.1093/bioinformatics/bts591
Kroll, H., Kalo, J.-C., Nagel, D., Mennicke, S., Balke, W.-T.: Context-compatible information fusion for scientific knowledge graphs. In: Hall, M., Merčun, T., Risse, T., Duchateau, F. (eds.) TPDL 2020. LNCS, vol. 12246, pp. 33–47. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-54956-5_3
Kroll, H., Nagel, D., Balke, W.-T.: Modeling narrative structures in logical overlays on top of knowledge repositories. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 250–260. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_18
Kroll, H., Nagel, D., Kunz, M., Balke, W.: Demonstrating narrative bindings: linking discourses to knowledge repositories. In: Campos, R., Jorge, A.M., Jatowt, A., Bhatia, S., Finlayson, M.A. (eds.) Proceedings of Text2Story - Fourth Workshop on Narrative Extraction From Texts held in conjunction with the 43rd European Conference on Information Retrieval (ECIR 2021), Lucca, Italy, 1 April 2021 (online event due to COVID-19 outbreak). CEUR Workshop Proceedings, vol. 2860, pp. 57–63. CEUR-WS.org (2021). http://ceur-ws.org/Vol-2860/paper7.pdf
Kroll, H., Pirklbauer, J., Kalo, J.-C., Kunz, M., Ruthmann, J., Balke, W.-T.: Narrative query graphs for entity-interaction-aware document retrieval. In: Ke, H.-R., Lee, C.S., Sugiyama, K. (eds.) ICADL 2021. LNCS, vol. 13133, pp. 80–95. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91669-5_7
Kroll, H., Plötzky, F., Pirklbauer, J., Balke, W.: What a publication tells you - benefits of narrative information access in digital libraries. CoRR (Accepted to JCDL2022) abs/2205.00718 (2022). https://doi.org/10.48550/arXiv.2205.00718
Lebo, T., Sahoo, S., McGuinness, D.: PROV-O: The PROV Ontology (2013). https://www.w3.org/TR/prov-o/
NASA: Global Temperature | Vital Signs - NASA Climate Change. https://climate.nasa.gov/evidence/. Accessed 25 May 2022
Nickerson, R.S.: Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2(2), 175–220 (1998). https://doi.org/10.1037/1089-2680.2.2.175
Rhodes, C.J.: Plastic pollution and potential solutions. Sci. Prog. 101, 207–260 (2018)
Ritchie, H., Roser, M.: Forests and deforestation. Our World in Data (2021). https://ourworldindata.org/forests-and-deforestation
Uscinski, J.E., Butler, R.W.: The epistemology of fact checking. Crit. Rev. 25(2), 162–180 (2013)
Weikum, G., Dong, X.L., Razniewski, S., Suchanek, F.M.: Machine knowledge: creation and curation of comprehensive knowledge bases. Found. Trends Databases 10(2–4), 108–490 (2021). https://doi.org/10.1561/1900000064
Williams, A., Rangel-Buitrago, N.: Marine litter: solutions for a major environmental problem. J. Coast. Res. 35(3), 648–663 (2019). https://doi.org/10.2112/JCOASTRES-D-18-00096.1
Zhao, C., Xiong, C., Qian, X., Boyd-Graber, J.L.: Complex factoid question answering with a free-text knowledge graph. In: Huang, Y., King, I., Liu, T., van Steen, M. (eds.) WWW 2020: The Web Conference 2020, Taipei, Taiwan, 20–24 April 2020, pp. 1205–1216. ACM/IW3C2 (2020). https://doi.org/10.1145/3366423.3380197
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Kroll, H., Mainzer, N., Balke, WT. (2022). On Dimensions of Plausibility for Narrative Information Access to Digital Libraries. In: Silvello, G., et al. Linking Theory and Practice of Digital Libraries. TPDL 2022. Lecture Notes in Computer Science, vol 13541. Springer, Cham. https://doi.org/10.1007/978-3-031-16802-4_43
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