A discovery system for narrative query graphs: entity-interaction-aware document retrieval

Azad, H.K., Deepak, A.: Query expansion techniques for information retrieval: a survey. Inf. Process. Manag. 56(5), 1698–1735 (2019). https://doi.org/10.1016/j.ipm.2019.05.009 Betts, C., Power, J., Ammar, W.: GrapAL: connecting the dots in scientific literature. In: Proceedings of the 57th annual meeting of the association for computational linguistics: system demonstrations. association for computational linguistics, Florence, Italy, pp 147–152, (2019)https://doi.org/10.18653/v1/P19-3025 Chen, Q.: An object-oriented database system for efficient information retrieval applications. PhD thesis, (1992) http://hdl.handle.net/10919/27976 Croft, W., Parenty, T.J.: A comparison of a network structure and a database system used for document retrieval. Inf. Syst. 10(4), 377–390 (1985). https://doi.org/10.1016/0306-4379(85)90042-0 Croft, W.B., Wolf, R., Thompson, R.: A network organization used for document retrieval. In: proceedings of the 6th annual international acm sigir conference on research and development in information retrieval. association for computing machinery, New York, NY, USA, SIGIR ’83, p 178-188, (1983) https://doi.org/10.1145/511793.511820 Dietz, L., Kotov, A., Meij, E.: Utilizing knowledge graphs for text-centric information retrieval. In: The 41st international ACM SIGIR conference on research & development in information retrieval. Association for computing machinery, New York, NY, USA, SIGIR ’18, p 1387-1390, (2018) https://doi.org/10.1145/3209978.3210187 Dogan, R.I., Leaman, R., Lu, Z.: NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Inf. 47, 1–10 (2014). https://doi.org/10.1016/j.jbi.2013.12.006 Färber, M.: The microsoft academic knowledge graph: A linked data source with 8 billion triples of scholarly data. In: The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part II, Lecture Notes in Computer Science, vol 11779. Springer, pp 113–129, (2019) https://doi.org/10.1007/978-3-030-30796-7_8 France, R.K.: Effective, efficient retrieval in a network of digital information objects. PhD thesis, (2001) http://hdl.handle.net/10919/29754 Herskovic, J.R., Tanaka, L.Y., Hersh, W., et al.: A day in the life of pubmed: analysis of a typical day’s query log. J. Am. Med. Inf. Assoc. 14(2), 212–220 (2007). https://doi.org/10.1197/jamia.M2191 Jaradeh, M.Y., Oelen, A., Farfar, K.E., et al. Open research knowledge graph: Next generation infrastructure for semantic scholarly knowledge. In: proceedings of the 10th international conference on knowledge capture, K-CAP 2019, Marina Del Rey, CA, USA, November 19-21, 2019. ACM, pp 243–246, (2019) https://doi.org/10.1145/3360901.3364435 Kadry, A., Dietz, L.: open relation extraction for support passage retrieval: merit and open issues. In: proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. Association for computing machinery, New York, NY, USA, SIGIR ’17, p 1149-1152, (2017) https://doi.org/10.1145/3077136.3080744 Kolluru, K., Adlakha, V., Aggarwal, S., et al. OpenIE6: iterative grid labeling and coordination analysis for open information extraction. In: Proc. of the 2020 conf. on empirical methods in natural language processing (EMNLP). ACL, pp 3748–3761, (2020) https://doi.org/10.18653/v1/2020.emnlp-main.306 Kroll, H., Kalo, J.C., Nagel, D., et al.: Context-compatible information fusion for scientific knowledge graphs. In: Digital Libraries for Open Knowledge, pp. 33–47. Springer (2020) Kroll, H., Nagel, D., Balke, W.T.: Modeling Narrative Structures in Logical Overlays on Top of Knowledge Repositories. In: Dev, T. (ed.) Conceptual Modeling, pp. 250–260. Springer (2020) Kroll, H., Nagel, D., Kunz, M., et al. Demonstrating narrative bindings: linking discourses to knowledge repositories. In: fourth workshop on narrative extraction from texts, Text2Story@ECIR2021, CEUR Workshop Proceedings, vol 2860. CEUR-WS.org, pp 57–63, (2021a) http://ceur-ws.org/Vol-2860/paper7.pdf Kroll, H., Pirklbauer, J., Balke, W.: A toolbox for the nearly-unsupervised construction of digital library knowledge graphs. In: ACM/IEEE joint conference on digital libraries, JCDL 2021, Champaign, IL, USA, September 27-30, 2021. IEEE, pp 21–30, (2021b) https://doi.org/10.1109/JCDL52503.2021.00014 Kroll, H., Pirklbauer, J., Kalo, J., et al. Narrative query graphs for entity-interaction-aware document retrieval. In: Towards open and trustworthy digital societies—23rd international conference on Asia-pacific digital libraries, ICADL 2021, Virtual Event, December 1-3, 2021, Proceedings, Lecture Notes in Computer Science, vol 13133. Springer, pp 80–95, (2021c) https://doi.org/10.1007/978-3-030-91669-5_7 Kroll, H., Pirklbauer, J., Plötzky, F., et al. A library perspective on nearly-unsupervised information extraction workflows in digital libraries. In: proceedings of the 22nd ACM/IEEE joint conference on digital libraries. Association for computing machinery, New York, NY, USA, JCDL ’22, (2022a) https://doi.org/10.1145/3529372.3530924 Kroll, H., Plötzky, F., Pirklbauer, J., et al. What a Publication Tells You-Benefits of Narrative Information Access in Digital Libraries. In: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries. Association for Computing Machinery, New York, NY, USA, JCDL ’22, (2022b) https://doi.org/10.1145/3529372.3530928 Krötzsch, M., Rudolph, S.: Is your database system a semantic web reasoner? KI-Künstliche Intelligenz 30(2), 169–176 (2016). https://doi.org/10.1007/s13218-015-0412-x Langnickel, L., Baum, R., Darms, J., et al. COVID-19 preVIEW: semantic search to explore COVID-19 research preprints. In: public health and informatics. IOS Press, Amsterdam, the Netherlands, p 78–82, (2021a) https://doi.org/10.3233/SHTI210124 Langnickel, L., Darms, J., Baum, R., et al.: preVIEW: from a fast prototype towards a sustainable semantic search system for central access to COVID-19 preprints. J. EAHIL 17(3), 8–14 (2021) Leaman, R., Lu, Z.: TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics 32(18), 2839–2846 (2016). https://doi.org/10.1093/bioinformatics/btw343 Manning, C.D., Surdeanu, M., Bauer, J., et al. The stanford CoreNLP natural language processing toolkit. In: proceedings of the 52nd annual meeting of the association for computational linguistics, ACL 2014, June 22-27, 2014, Baltimore, MD, USA, system demonstrations. The association for computer linguistics, pp 55–60, (2014) https://doi.org/10.3115/v1/p14-5010 Manola, F., Miller, E., McBride, B., et al. RDF primer. W3C recommendation 10(1-107):6 (2004) Mendez, D., Gaulton, A., Bento, A.P., et al.: ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47(D1), D930–D940 (2018). https://doi.org/10.1093/nar/gky1075 Mohan, S., Fiorini, N., Kim, S., et al. A fast deep learning model for textual relevance in biomedical information retrieval. In: Proceedings of the 2018 world wide web conference. International world wide web conferences steering committee, Republic and Canton of Geneva, CHE, WWW ’18, p 77-86, (2018) https://doi.org/10.1145/3178876.3186049 Nguyen, D.B., Abujabal, A., Tran, N.K., et al.: Query-driven on-the-fly knowledge base construction. Proc. VLDB Endow 11(1), 66–79 (2017) Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. (2009). https://doi.org/10.1145/1567274.1567278 Priem, J., Piwowar, H., Orr, R.: Openalex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. (2022) https://doi.org/10.48550/ARXIV.2205.01833 Ratner, A., Bach, S.H., Ehrenberg, H.R., et al.: Snorkel: rapid training data creation with weak supervision. Proc. VLDB Endow 11(3), 269–282 (2017) Raviv, H., Kurland, O., Carmel, D.: Document retrieval using entity-based language models. In: Proceedings of the 39th international acm sigir conference on research and development in information retrieval. association for computing machinery, New York, NY, USA, SIGIR ’16, p 65-74, (2016) https://doi.org/10.1145/2911451.2911508 Shin, J., Wu, S., Wang, F., et al.: Incremental knowledge base construction using deepdive. Proc. VLDB Endow 8(11), 1310–1321 (2015) Spitz, A., Gertz, M.: Terms over LOAD: Leveraging named entities for cross-document extraction and summarization of events. In: proceedings of the 39th international acm sigir conference on research and development in information retrieval. Association for computing machinery, New York, NY, USA, SIGIR ’16, p 503-512, (2016) https://doi.org/10.1145/2911451.2911529 Vazirgiannis, M., Malliaros, F.D., Nikolentzos, G.: GraphRep: boosting text mining, NLP and information retrieval with graphs. In: proceedings of the 27th ACM international conference on information and knowledge management. Association for computing machinery, New York, NY, USA, CIKM ’18, p 2295-2296, (2018) https://doi.org/10.1145/3269206.3274273 Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014). https://doi.org/10.1145/2629489 Weaver, M.T.: Implementing an intelligent information retrieval system: the CODER system, version 1.0. Master’s thesis, (1988) http://hdl.handle.net/10919/44097 Wei, C.H., Kao, H.Y., Lu, Z.: PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res. 41(W1), W518–W522 (2013). https://doi.org/10.1093/nar/gkt441 Wei, C.H., Kao, H.Y., Lu, Z.: GNormPlus: an integrative approach for tagging genes, gene families, and protein domains. BioMed. Res. Int. 918, 710 (2015a). https://doi.org/10.1155/2015/918710 Wei, C.H., Peng, Y., Leaman, R., et al. Overview of the BioCreative V chemical disease relation (CDR) task. In: proceedings of the fifth biocreative challenge evaluation workshop (2015b) Wei, C.H., Allot, A., Leaman, R., et al.: PubTator central: automated concept annotation for biomedical full text articles. Nucleic Acids Res. 47(W1), W587–W593 (2019). https://doi.org/10.1093/nar/gkz389 Xiong, C., Power, R., Callan, J.: Explicit semantic ranking for academic search via knowledge graph embedding. In: proceedings of the 26th international conference on world wide web. international world wide web conferences steering committee, Republic and Canton of Geneva, CHE, WWW ’17, p 1271-1279, (2017) https://doi.org/10.1145/3038912.3052558 Zhang, Y., Chen, Q., Yang, Z., et al.: BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci. Data 6(1), 52 (2019). https://doi.org/10.1038/s41597-019-0055-0 Zhao, S., Su, C., Sboner, A., et al. GRAPHENE: a precise biomedical literature retrieval engine with graph augmented deep learning and external knowledge empowerment. In: proceedings of the 28th ACM international conference on information and knowledge management. Association for computing machinery, New York, NY, USA, CIKM ’19, p 149-158, (2019) https://doi.org/10.1145/3357384.3358038