{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T11:10:18Z","timestamp":1721473818306},"reference-count":77,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Modern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant reduction of the number of (hyper)edges, in regard to the number of nodes, while nearly capturing the same amount of information. Moreover, such a higher-order data structure, presents the ability to capture richer types of relations, including<jats:italic>n<\/jats:italic>ary connections such as synonymy, or subsumption. We present the hypergraph-of-entity as the next step in the graph-of-entity model, where we explore a ranking approach based on biased random walks. We evaluate the approaches using a subset of the INEX 2009 Wikipedia Collection. While performance is still below the state of the art, we were, in part, able to achieve a MAP score similar to TF-IDF and greatly improve indexing efficiency over the graph-of-entity.<\/jats:p>","DOI":"10.1515\/comp-2019-0006","type":"journal-article","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T09:03:31Z","timestamp":1561712611000},"page":"103-127","source":"Crossref","is-referenced-by-count":7,"title":["Hypergraph-of-entity"],"prefix":"10.1515","volume":"9","author":[{"given":"Jos\u00e9","family":"Devezas","sequence":"first","affiliation":[{"name":"INESC TEC and Faculty of Engineering , University of Porto , Portugal Porto"}]},{"given":"S\u00e9rgio","family":"Nunes","sequence":"additional","affiliation":[{"name":"INESC TEC and Faculty of Engineering , University of Porto , Portugal Porto"}]}],"member":"374","published-online":{"date-parts":[[2019,6,6]]},"reference":[{"key":"2022042707443478592_j_comp-2019-0006_ref_001_w2aab3b7b5b1b6b1ab1ab1Aa","unstructured":"[1] Gomes F., Devezas J., Figueira \u00c1., Temporal visualization of a multidimensional network of news clips, In: Advances in Information Systems and Technologies, Springer, 2013, 157\u201316610.1007\/978-3-642-36981-0_15"},{"key":"2022042707443478592_j_comp-2019-0006_ref_002_w2aab3b7b5b1b6b1ab1ab2Aa","doi-asserted-by":"crossref","unstructured":"[2] Belkin N. J., Croft W. B., Information filtering and information retrieval: Two sides of the same coin?, In: Communications of the ACM, 1992, 35(12), 29\u20133810.1145\/138859.138861","DOI":"10.1145\/138859.138861"},{"key":"2022042707443478592_j_comp-2019-0006_ref_003_w2aab3b7b5b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] Bautin M., Skiena S., Concordance-based entity-oriented search, In: IEEE\/WIC\/ACM Conference on Web Intelligence (WI\u201907), 2007, 2\u2013510.1109\/WI.2007.84","DOI":"10.1109\/WI.2007.84"},{"key":"2022042707443478592_j_comp-2019-0006_ref_004_w2aab3b7b5b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] Blanco R., Lioma C., Graph-based term weighting for information retrieval, In: Information Retrieval, 2012, 15(1), 54\u20139210.1007\/s10791-011-9172-x","DOI":"10.1007\/s10791-011-9172-x"},{"key":"2022042707443478592_j_comp-2019-0006_ref_005_w2aab3b7b5b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] Rousseau F., Vazirgiannis M., Graph-of-word and TW-IDF: New approach to ad hoc IR, In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, ACM, 2013, 59\u20136810.1145\/2505515.2505671","DOI":"10.1145\/2505515.2505671"},{"key":"2022042707443478592_j_comp-2019-0006_ref_006_w2aab3b7b5b1b6b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[6] Bu J., Tan S., Chen C., Wang C., Wu H., Zhang L., He X., Music recommendation by unified hypergraph: Combining social media information and music content, In: Proceedings of the 18th International Conference on Multimedia, Firenze, Italy, October 25-29, 2010, 391\u201340010.1145\/1873951.1874005","DOI":"10.1145\/1873951.1874005"},{"key":"2022042707443478592_j_comp-2019-0006_ref_007_w2aab3b7b5b1b6b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"[7] Xiong C., Callan J., Liu T., Word-entity duet representations for document ranking, In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017, 763\u201377210.1145\/3077136.3080768","DOI":"10.1145\/3077136.3080768"},{"key":"2022042707443478592_j_comp-2019-0006_ref_008_w2aab3b7b5b1b6b1ab1ab8Aa","doi-asserted-by":"crossref","unstructured":"[8] Bast H., Buchhold B., Haussmann E., Semantic search on text and knowledge bases, In: Foundations and Trends\u00ae in Information Retrieval, 2016, 10(2-3), 119\u201327110.1561\/1500000032","DOI":"10.1561\/1500000032"},{"key":"2022042707443478592_j_comp-2019-0006_ref_009_w2aab3b7b5b1b6b1ab1ab9Aa","unstructured":"[9] Schenkel R., Suchanek F. M., Kasneci G., YAWN: A semantically annotated Wikipedia XML corpus, In: Datenbanksysteme in Business, Technologie und Web (BTW 2007), 12. Fachtagung des GIFachbereichs \u201cDatenbanken und Informationssysteme\u201d (DBIS), Proceedings, 7.-9. M\u00e4rz 2007, Aachen, Germany, 2007, 277\u2013291"},{"key":"2022042707443478592_j_comp-2019-0006_ref_010_w2aab3b7b5b1b6b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] Luhn H. P., A statistical approach to mechanized encoding and searching of literary information, In: IBM Journal of Research and Development, 1957, 1(4), 309\u201331710.1147\/rd.14.0309","DOI":"10.1147\/rd.14.0309"},{"key":"2022042707443478592_j_comp-2019-0006_ref_011_w2aab3b7b5b1b6b1ab1ac11Aa","doi-asserted-by":"crossref","unstructured":"[11] Sparck Jones K., A statistical interpretation of term specificity and its application in retrieval, In: Journal of Documentation, 1972, 28(1), 11\u20132110.1108\/eb026526","DOI":"10.1108\/eb026526"},{"key":"2022042707443478592_j_comp-2019-0006_ref_012_w2aab3b7b5b1b6b1ab1ac12Aa","doi-asserted-by":"crossref","unstructured":"[12] Robertson S. E., Walker S., Jones S., Hancock-Beaulieu M., Gat-ford M., Okapi at TREC-3, In: Proceedings of The Third Text Retrieval Conference, TREC 1994, Gaithersburg, Maryland, USA, November 2-4, 1994, 109\u2013126","DOI":"10.6028\/NIST.SP.500-225.city"},{"key":"2022042707443478592_j_comp-2019-0006_ref_013_w2aab3b7b5b1b6b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] Ponte J. M., Croft W. B., A language modeling approach to information retrieval, In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, August 24-28 1998, 275\u201328110.1145\/290941.291008","DOI":"10.1145\/290941.291008"},{"key":"2022042707443478592_j_comp-2019-0006_ref_014_w2aab3b7b5b1b6b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] Amati G., van Rijsbergen C. J., Probabilistic models of information retrieval based on measuring the divergence from randomness, In: ACM Transactions on Information Systems, 2002, 20(4), 357\u201338910.1145\/582415.582416","DOI":"10.1145\/582415.582416"},{"key":"2022042707443478592_j_comp-2019-0006_ref_015_w2aab3b7b5b1b6b1ab1ac15Aa","doi-asserted-by":"crossref","unstructured":"[15] Kraaij W., Westerveld T., Hiemstra D., The importance of prior probabilities for entry page search, In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, August 11-15, 2002, 27\u20133410.1145\/564376.564383","DOI":"10.1145\/564376.564383"},{"key":"2022042707443478592_j_comp-2019-0006_ref_016_w2aab3b7b5b1b6b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[16] Westerveld T., KraaijW., Hiemstra D., Retrieving web pages using content, links, URLs and anchors, In: Proceedings of The Tenth Text Retrieval Conference, TREC 2001, Gaithersburg, Maryland, USA, November 13-16, 2001","DOI":"10.6028\/NIST.SP.500-250.tno\/utwente"},{"key":"2022042707443478592_j_comp-2019-0006_ref_017_w2aab3b7b5b1b6b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] Brin S., Page L., The anatomy of a large-scale hypertextual web search engine, In: Computer Networks, 1998, 30(1-7), 107\u201311710.1016\/S0169-7552(98)00110-X","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"2022042707443478592_j_comp-2019-0006_ref_018_w2aab3b7b5b1b6b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"[18] Badache I., Boughanem M., A priori relevance based on quality and diversity of social signals, In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9-13, 2015, 731\u201373410.1145\/2766462.2767807","DOI":"10.1145\/2766462.2767807"},{"key":"2022042707443478592_j_comp-2019-0006_ref_019_w2aab3b7b5b1b6b1ab1ac19Aa","doi-asserted-by":"crossref","unstructured":"[19] Badache I., Boughanem M., Emotional social signals for search ranking, In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7-11, 2017, 1053\u2013105610.1145\/3077136.3080718","DOI":"10.1145\/3077136.3080718"},{"key":"2022042707443478592_j_comp-2019-0006_ref_020_w2aab3b7b5b1b6b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[20] Macdonald C., Ounis I., Voting for candidates: Adapting data fusion techniques for an expert search task, In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Arlington, Virginia, USA, November 6-11, 2006, 387\u201339610.1145\/1183614.1183671","DOI":"10.1145\/1183614.1183671"},{"key":"2022042707443478592_j_comp-2019-0006_ref_021_w2aab3b7b5b1b6b1ab1ac21Aa","doi-asserted-by":"crossref","unstructured":"[21] Fang Y., Si L., Related entity finding by unified probabilistic models, In: World Wide Web, 2015, 18(3), 521\u201354310.1007\/s11280-013-0267-8","DOI":"10.1007\/s11280-013-0267-8"},{"key":"2022042707443478592_j_comp-2019-0006_ref_022_w2aab3b7b5b1b6b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] Davison B. D., Topical locality in the web, In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece, July 24-28, 2000, 272\u201327910.1145\/345508.345597","DOI":"10.1145\/345508.345597"},{"key":"2022042707443478592_j_comp-2019-0006_ref_023_w2aab3b7b5b1b6b1ab1ac23Aa","doi-asserted-by":"crossref","unstructured":"[23] Raiber F., Kurland O., Exploring the cluster hypothesis, and cluster-based retrieval, over the web, In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Maui, HI, USA, October 29 - November 02, 2012, 2507\u2013251010.1145\/2396761.2398678","DOI":"10.1145\/2396761.2398678"},{"key":"2022042707443478592_j_comp-2019-0006_ref_024_w2aab3b7b5b1b6b1ab1ac24Aa","unstructured":"[24] Hogan A., Harth A., Decker S., ReConRank: A scalable ranking method for semantic web data with context, In: Proceedings of Second International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2006), in conjunction with International Semantic Web Conference (ISWC 2006), 2006"},{"key":"2022042707443478592_j_comp-2019-0006_ref_025_w2aab3b7b5b1b6b1ab1ac25Aa","unstructured":"[25] Balmin A., Hristidis V., Papakonstantinou Y., ObjectRank: Authority-based keyword search in databases, In: (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, August 31 - September 3, 2004, 564\u201357510.1016\/B978-012088469-8.50051-6"},{"key":"2022042707443478592_j_comp-2019-0006_ref_026_w2aab3b7b5b1b6b1ab1ac26Aa","doi-asserted-by":"crossref","unstructured":"[26] Nie Z., Zhang Y., Wen J., Ma W., Object-level ranking: Bringing order to web objects, In: Proceedings of the 14th International Conference on World Wide Web, Chiba, Japan, May 10-14, 2005, 567\u201357410.1145\/1060745.1060828","DOI":"10.1145\/1060745.1060828"},{"key":"2022042707443478592_j_comp-2019-0006_ref_027_w2aab3b7b5b1b6b1ab1ac27Aa","doi-asserted-by":"crossref","unstructured":"[27] Chakrabarti S., Dynamic personalized PageRank in entity-relation graphs, In: Proceedings of the 16th International Conference on World Wide Web, Banff, Alberta, Canada, May 8-12, 2007, 571\u201358010.1145\/1242572.1242650","DOI":"10.1145\/1242572.1242650"},{"key":"2022042707443478592_j_comp-2019-0006_ref_028_w2aab3b7b5b1b6b1ab1ac28Aa","unstructured":"[28] Delbru R., Toupikov N., Catasta M., Tummarello G., Decker S., Hierarchical link analysis for ranking web data, In: The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30 \u2013 June 3, 2010, Proceedings, Part II, 2010, 225\u201323910.1007\/978-3-642-13489-0_16"},{"key":"2022042707443478592_j_comp-2019-0006_ref_029_w2aab3b7b5b1b6b1ab1ac29Aa","doi-asserted-by":"crossref","unstructured":"[29] 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, Pisa, Italy, July 17-21, 2016, 65\u20137410.1145\/2911451.2911508","DOI":"10.1145\/2911451.2911508"},{"key":"2022042707443478592_j_comp-2019-0006_ref_030_w2aab3b7b5b1b6b1ab1ac30Aa","unstructured":"[30] Neumayer R., Balog K., N\u0159rv\u013ag K., On the modeling of entities for ad-hoc entity search in the web of data, In: Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012, Proceedings, 2012, 133\u201314510.1007\/978-3-642-28997-2_12"},{"key":"2022042707443478592_j_comp-2019-0006_ref_031_w2aab3b7b5b1b6b1ab1ac31Aa","unstructured":"[31] Lin B., Rosa K. D., Shah R., Agarwal N., LADS: Rapid development of a learning-to-rank based related entity finding system using open advancement, In: The First International Workshop on Entity-Oriented Search (EOS), 2011"},{"key":"2022042707443478592_j_comp-2019-0006_ref_032_w2aab3b7b5b1b6b1ab1ac32Aa","doi-asserted-by":"crossref","unstructured":"[32] Schuhmacher M., Dietz L., Ponzetto S. P., Ranking entities for web queries through text and knowledge, In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, VIC, Australia, October 19-23, 2015, 1461\u2013147010.1145\/2806416.2806480","DOI":"10.1145\/2806416.2806480"},{"key":"2022042707443478592_j_comp-2019-0006_ref_033_w2aab3b7b5b1b6b1ab1ac33Aa","doi-asserted-by":"crossref","unstructured":"[33] Chen J., Xiong C., Callan J., An empirical study of learning to rank for entity search, In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, July 17-21, 2016, 737\u201374010.1145\/2911451.2914725","DOI":"10.1145\/2911451.2914725"},{"key":"2022042707443478592_j_comp-2019-0006_ref_034_w2aab3b7b5b1b6b1ab1ac34Aa","doi-asserted-by":"crossref","unstructured":"[34] Tonon A., Demartini G., Cudr\u00e9-Mauroux P., Combining inverted indices and structured search for ad-hoc object retrieval, In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, OR, USA, August 12-16, 2012, 125\u201313410.1145\/2348283.2348304","DOI":"10.1145\/2348283.2348304"},{"key":"2022042707443478592_j_comp-2019-0006_ref_035_w2aab3b7b5b1b6b1ab1ac35Aa","doi-asserted-by":"crossref","unstructured":"[35] Cao L., Guo J., Cheng X., Bipartite graph based entity ranking for related entity finding, In: Proceedings of the 2011 IEEE\/WIC\/ACM International Conference on Web Intelligence, Campus Scientifique de la Doua, Lyon, France, August 22-27, 2011, 2011, 130\u201313710.1109\/WI-IAT.2011.60","DOI":"10.1109\/WI-IAT.2011.60"},{"key":"2022042707443478592_j_comp-2019-0006_ref_036_w2aab3b7b5b1b6b1ab1ac36Aa","doi-asserted-by":"crossref","unstructured":"[36] Raviv H., Kurland O., Carmel D., The cluster hypothesis for entity oriented search, in: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013, 841\u201384410.1145\/2484028.2484128","DOI":"10.1145\/2484028.2484128"},{"key":"2022042707443478592_j_comp-2019-0006_ref_037_w2aab3b7b5b1b6b1ab1ac37Aa","unstructured":"[37] Bron M., Balog K., de Rijke M., Example based entity search in the web of data, In: Advances in Information Retrieval \u2013 35th European Conference on Information Retrieval, ECIR 2013, Moscow, Russia, March 24-27, 2013, Proceedings, 2013, 392\u201340310.1007\/978-3-642-36973-5_33"},{"key":"2022042707443478592_j_comp-2019-0006_ref_038_w2aab3b7b5b1b6b1ab1ac38Aa","doi-asserted-by":"crossref","unstructured":"[38] Pound J., Mika P., Zaragoza H., Ad-hoc object retrieval in the web of data, In: Proceedings of the 19th International Conference on World Wide Web, ACM, 2010, 771\u201378010.1145\/1772690.1772769","DOI":"10.1145\/1772690.1772769"},{"key":"2022042707443478592_j_comp-2019-0006_ref_039_w2aab3b7b5b1b6b1ab1ac39Aa","unstructured":"[39] Devezas J., Coelho F., Nunes S., Ribeiro C., Music Discovery: Exploiting TF-IDF to boost results in the long tail of the tags distribution, 2013"},{"key":"2022042707443478592_j_comp-2019-0006_ref_040_w2aab3b7b5b1b6b1ab1ac40Aa","unstructured":"[40] Arvola P., Geva S., Kamps J., Schenkel R., Trotman A., Vainio J., Overview of the INEX 2010 ad hoc track, In: Comparative Evaluation of Focused Retrieval - 9th International Workshop of the Inititative for the Evaluation of XML Retrieval, INEX 2010, Vugh, The Netherlands, December 13-15, 2010, Revised Selected Papers, 2010, 1\u20133210.1007\/978-3-642-23577-1_1"},{"key":"2022042707443478592_j_comp-2019-0006_ref_041_w2aab3b7b5b1b6b1ab1ac41Aa","unstructured":"[41] Demartini G., Iofciu T., de Vries A. P., Overview of the INEX 2009 entity ranking track, In: Focused Retrieval and Evaluation, 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Brisbane, Australia, December 7-9, 2009, Revised and Selected Papers, 2009, 254\u201326410.1007\/978-3-642-14556-8_26"},{"key":"2022042707443478592_j_comp-2019-0006_ref_042_w2aab3b7b5b1b6b1ab1ac42Aa","doi-asserted-by":"crossref","unstructured":"[42] Clarke C. L. A., Craswell N., Soboroff I., Overview of the TREC 2009 web track, In: Proceedings of The Eighteenth Text Retrieval Conference, TREC 2009, Gaithersburg,Maryland, USA, November 17-20, 2009","DOI":"10.6028\/NIST.SP.500-278.web-overview"},{"key":"2022042707443478592_j_comp-2019-0006_ref_043_w2aab3b7b5b1b6b1ab1ac43Aa","doi-asserted-by":"crossref","unstructured":"[43] Balog K., Serdyukov P., de Vries A. P., Overview of the TREC 2011 entity track, In: Proceedings of The Twentieth Text REtrieval Conference, TREC 2011, Gaithersburg, Maryland, USA, November 15-18, 2011","DOI":"10.6028\/NIST.SP.500-296.entity-overview"},{"key":"2022042707443478592_j_comp-2019-0006_ref_044_w2aab3b7b5b1b6b1ab1ac44Aa","unstructured":"[44] Campinas S., Ceccarelli D., Perry T. E., Delbru R., Balog K., Tummarello G., The Sindice-2011 dataset for entity-oriented search in the web of data, In: The First International Workshop on Entity-Oriented Search (EOS), 2011, 26\u201332"},{"key":"2022042707443478592_j_comp-2019-0006_ref_045_w2aab3b7b5b1b6b1ab1ac45Aa","doi-asserted-by":"crossref","unstructured":"[45] Dkaki T., Mothe J., Truong Q. D., Passage retrieval using graph vertices comparison, In: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, Shanghai, China, December 16-18, 2007, 71\u20137610.1109\/SITIS.2007.82","DOI":"10.1109\/SITIS.2007.82"},{"key":"2022042707443478592_j_comp-2019-0006_ref_046_w2aab3b7b5b1b6b1ab1ac46Aa","unstructured":"[46] Page L., Brin S., Motwani R., Winograd T., The PageRank citation ranking: Bringing order to the web, Technical report, Stanford InfoLab, 1999"},{"key":"2022042707443478592_j_comp-2019-0006_ref_047_w2aab3b7b5b1b6b1ab1ac47Aa","doi-asserted-by":"crossref","unstructured":"[47] Khurana U., Deshpande A., Efficient snapshot retrieval over historical graph data, In: Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), Brisbane, Australia, April 8-12, 2013, 997\u2013100810.1109\/ICDE.2013.6544892","DOI":"10.1109\/ICDE.2013.6544892"},{"key":"2022042707443478592_j_comp-2019-0006_ref_048_w2aab3b7b5b1b6b1ab1ac48Aa","unstructured":"[48] Martins B., Silva M. J., A Graph-Ranking Algorithm for Geo-Referencing Documents, In: Proceedings of the Fifth IEEE International Conference on Data Mining, Houston, Texas, USA, 27-30 November, 2005, 741\u2013744"},{"key":"2022042707443478592_j_comp-2019-0006_ref_049_w2aab3b7b5b1b6b1ab1ac49Aa","doi-asserted-by":"crossref","unstructured":"[49] Zhu Y., Yan E., Song I., A natural language interface to a graph-based bibliographic information retrieval system, In: Data & Knowledge Engineering, 2017, 111, 73\u20138910.1016\/j.datak.2017.06.006","DOI":"10.1016\/j.datak.2017.06.006"},{"key":"2022042707443478592_j_comp-2019-0006_ref_050_w2aab3b7b5b1b6b1ab1ac50Aa","unstructured":"[50] Blanco R., Mika P., Vigna S., Effective and efficient entity search in RDF data, In: The Semantic Web \u2013 ISWC2011 \u2013 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I, 2011, 83\u20139710.1007\/978-3-642-25073-6_6"},{"key":"2022042707443478592_j_comp-2019-0006_ref_051_w2aab3b7b5b1b6b1ab1ac51Aa","doi-asserted-by":"crossref","unstructured":"[51] Bendersky M., Croft W. B., Modeling higher-order term dependencies in information retrieval using query hypergraphs, In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, Oregon, USA, 2012, 941\u201395010.1145\/2348283.2348408","DOI":"10.1145\/2348283.2348408"},{"key":"2022042707443478592_j_comp-2019-0006_ref_052_w2aab3b7b5b1b6b1ab1ac52Aa","doi-asserted-by":"crossref","unstructured":"[52] Xiong S., Ji D., Query-focused multi-document summarization using hypergraph-based ranking, In: Information Processing & Management, 2016, 52(4), 670\u201368110.1016\/j.ipm.2015.12.012","DOI":"10.1016\/j.ipm.2015.12.012"},{"key":"2022042707443478592_j_comp-2019-0006_ref_053_w2aab3b7b5b1b6b1ab1ac53Aa","unstructured":"[53] Haentjens Dekker R., Birnbaum D. J., It\u2019s more than just overlap: Text As Graph, In: Proceedings of Balisage: The Markup Conference 2017, 19, 2017"},{"key":"2022042707443478592_j_comp-2019-0006_ref_054_w2aab3b7b5b1b6b1ab1ac54Aa","unstructured":"[54] Cattuto C., Schmitz C., Baldassarri A., Servedio V. D. P., Loreto V., Hotho A., Grahl M., Stumme G., Network properties of folk-sonomies, In: AI Communications, 2007, 20(4), 245\u2013262"},{"key":"2022042707443478592_j_comp-2019-0006_ref_055_w2aab3b7b5b1b6b1ab1ac55Aa","doi-asserted-by":"crossref","unstructured":"[55] Seidman S. B., Structures induced by collections of subsets: A hypergraph approach, In: Mathematical Social Sciences, 1981, 1(4), 381\u201339610.1016\/0165-4896(81)90016-0","DOI":"10.1016\/0165-4896(81)90016-0"},{"key":"2022042707443478592_j_comp-2019-0006_ref_056_w2aab3b7b5b1b6b1ab1ac56Aa","doi-asserted-by":"crossref","unstructured":"[56] Tan S., Bu J., Chen C., Xu B., Wang C., He X., Using rich social media information for music recommendation via hypergraph model, In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) \u2013 Special Section on ACM Multimedia 2010 Best Paper Candidates, and Issue on Social Media, 2011, 7S(1), 2210.1145\/2037676.2037679","DOI":"10.1145\/2037676.2037679"},{"key":"2022042707443478592_j_comp-2019-0006_ref_057_w2aab3b7b5b1b6b1ab1ac57Aa","unstructured":"[57] McFee B., Lanckriet G. R. G., Hypergraph models of playlist dialects, In: Proceedings of the 13th International Society for Music Information Retrieval Conference, Mosteiro S.Bento Da Vit\u00f3ria, Porto, Portugal, October 8-12, 2012, 343\u2013348"},{"key":"2022042707443478592_j_comp-2019-0006_ref_058_w2aab3b7b5b1b6b1ab1ac58Aa","doi-asserted-by":"crossref","unstructured":"[58] Theodoridis A., Kotropoulos C., Panagakis Y., Music recommendation using hypergraphs and group sparsity, In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, May 26-31, 2013, 56\u20136010.1109\/ICASSP.2013.6637608","DOI":"10.1109\/ICASSP.2013.6637608"},{"key":"2022042707443478592_j_comp-2019-0006_ref_059_w2aab3b7b5b1b6b1ab1ac59Aa","unstructured":"[59] von Neumann J., The computer and the brain, Yale University Press, 2012"},{"key":"2022042707443478592_j_comp-2019-0006_ref_060_w2aab3b7b5b1b6b1ab1ac60Aa","unstructured":"[60] Sporns O., Networks of the brain, MIT press, 201010.7551\/mitpress\/8476.001.0001"},{"key":"2022042707443478592_j_comp-2019-0006_ref_061_w2aab3b7b5b1b6b1ab1ac61Aa","doi-asserted-by":"crossref","unstructured":"[61] Davison E. N., Schlesinger K. J., Bassett D. S., Lynall M.-E., Miller M. B., Grafton S. T., Carlson J. M., Brain network adaptability across task states, In: PLOS Computational Biology, 2015, 11(1), 1\u20131410.1371\/journal.pcbi.1004029428734725569227","DOI":"10.1371\/journal.pcbi.1004029"},{"key":"2022042707443478592_j_comp-2019-0006_ref_062_w2aab3b7b5b1b6b1ab1ac62Aa","doi-asserted-by":"crossref","unstructured":"[62] Jie B., Wee C.-Y., Shen D., Zhang D., Hyper-connectivity of functional networks for brain disease diagnosis, In: Medical Image Analysis, 2016, 32, 84\u20131002706062110.1016\/j.media.2016.03.003533348827060621","DOI":"10.1016\/j.media.2016.03.003"},{"key":"2022042707443478592_j_comp-2019-0006_ref_063_w2aab3b7b5b1b6b1ab1ac63Aa","doi-asserted-by":"crossref","unstructured":"[63] Gu S., Yang M., Medaglia J. D., Gur R. C., Gur R. E., Satterthwaite T. D., Bassett D. S., Functional hypergraph uncovers novel covariant structures over neurodevelopment, In: Human Brain Mapping, 2017, 38(8), 3823\u201338352849353610.1002\/hbm.23631632363728493536","DOI":"10.1002\/hbm.23631"},{"key":"2022042707443478592_j_comp-2019-0006_ref_064_w2aab3b7b5b1b6b1ab1ac64Aa","doi-asserted-by":"crossref","unstructured":"[64] Zhang B. T., Random hypergraph models of learning and memory in biomolecular networks: Shorter-term adaptability vs. longer term persistency, In: 2007 IEEE Symposium on Foundations of Computational Intelligence, 2007, 344\u2013349","DOI":"10.1109\/FOCI.2007.371494"},{"key":"2022042707443478592_j_comp-2019-0006_ref_065_w2aab3b7b5b1b6b1ab1ac65Aa","doi-asserted-by":"crossref","unstructured":"[65] Goertzel B., Patterns, hypergraphs and embodied general intelligence, In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006, 451\u201345810.1109\/IJCNN.2006.246716","DOI":"10.1109\/IJCNN.2006.246716"},{"key":"2022042707443478592_j_comp-2019-0006_ref_066_w2aab3b7b5b1b6b1ab1ac66Aa","unstructured":"[66] Bellaachia A., Al-Dhelaan M., Random walks in hypergraph, In: Proceedings of the 2013 International Conference on Applied Mathematics and Computational Methods, Venice Italy, 2013, 187\u2013194"},{"key":"2022042707443478592_j_comp-2019-0006_ref_067_w2aab3b7b5b1b6b1ab1ac67Aa","doi-asserted-by":"crossref","unstructured":"[67] Devezas J., Lopes C. T., Nunes S., FEUP at TREC 2017 OpenSearch track: Graph-based models for entity-oriented search, In: The Twenty-Sixth Text REtrieval Conference Proceedings (TREC 2017), Gaithersburg, MD, USA, 2017","DOI":"10.6028\/NIST.SP.500-324.open-FEUP"},{"key":"2022042707443478592_j_comp-2019-0006_ref_068_w2aab3b7b5b1b6b1ab1ac68Aa","unstructured":"[68] Devezas J., Nunes S., Graph-based entity-oriented search: imitating the human process of seeking and cross referencing information, In: ERCIM News, Special Issue: Digital Humanities, 2017, 111, 13\u201314"},{"key":"2022042707443478592_j_comp-2019-0006_ref_069_w2aab3b7b5b1b6b1ab1ac69Aa","unstructured":"[69] Mikolov T., Sutskever I., Chen K., Corrado G. S., Dean J., Distributed representations of words and phrases and their compositionality, In: Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2013, 2, 3111\u20133119"},{"key":"2022042707443478592_j_comp-2019-0006_ref_070_w2aab3b7b5b1b6b1ab1ac70Aa","doi-asserted-by":"crossref","unstructured":"[70] Robertson S., Understanding inverse document frequency: On theoretical arguments for IDF, In: Journal of Documentation, 2004, 60(5), 503\u201352010.1108\/00220410410560582","DOI":"10.1108\/00220410410560582"},{"key":"2022042707443478592_j_comp-2019-0006_ref_071_w2aab3b7b5b1b6b1ab1ac71Aa","doi-asserted-by":"crossref","unstructured":"[71] Alhelbawy A., Gaizauskas R., Graph ranking for collective named entity disambiguation, In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014, 2, 75\u20138010.3115\/v1\/P14-2013","DOI":"10.3115\/v1\/P14-2013"},{"key":"2022042707443478592_j_comp-2019-0006_ref_072_w2aab3b7b5b1b6b1ab1ac72Aa","unstructured":"[72] Hoffart J., Yosef M. A., Bordino I., F\u00fcrstenau H., Pinkal M., Spaniol M., Taneva B., Thater S., Weikum G., Robust disambiguation of named entities in text, In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011, 782\u2013792"},{"key":"2022042707443478592_j_comp-2019-0006_ref_073_w2aab3b7b5b1b6b1ab1ac73Aa","doi-asserted-by":"crossref","unstructured":"[73] Moro A., Raganato A., Navigli R., Entity linking meets word sense disambiguation: A unified approach, In: Transactions of the Association for Computational Linguistics, 2014, 2, 231\u201324410.1162\/tacl_a_00179","DOI":"10.1162\/tacl_a_00179"},{"key":"2022042707443478592_j_comp-2019-0006_ref_074_w2aab3b7b5b1b6b1ab1ac74Aa","unstructured":"[74] Geva S., Kamps J., Lehtonen M., Schenkel R., Thom J. A., Trotman A., Overview of the INEX 2009 ad hoc track, In: Focused Retrieval and Evaluation, 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009, Brisbane, Australia, December 7-9, 2009, Revised and Selected Papers, 2009, 4\u20132510.1007\/978-3-642-14556-8_4"},{"key":"2022042707443478592_j_comp-2019-0006_ref_075_w2aab3b7b5b1b6b1ab1ac75Aa","doi-asserted-by":"crossref","unstructured":"[75] Coelho F., Ribeiro C., Automatic illustration with cross-media retrieval in large-scale collections, In: 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI), 2011, 25\u20133010.1109\/CBMI.2011.5972515","DOI":"10.1109\/CBMI.2011.5972515"},{"key":"2022042707443478592_j_comp-2019-0006_ref_076_w2aab3b7b5b1b6b1ab1ac76Aa","doi-asserted-by":"crossref","unstructured":"[76] Liu J., Pasupat P.,Wang Y., Cyphers S., Glass J., Query understanding enhanced by hierarchical parsing structures, In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 2013, 72\u20137710.1109\/ASRU.2013.6707708","DOI":"10.1109\/ASRU.2013.6707708"},{"key":"2022042707443478592_j_comp-2019-0006_ref_077_w2aab3b7b5b1b6b1ab1ac77Aa","doi-asserted-by":"crossref","unstructured":"[77] Fogaras D., R\u00e1cz B., Csalog\u00e1ny K., Sarl\u00f3s T., Towards scaling fully personalized PageRank: Algorithms, lower bounds, and experiments, In: Internet Mathematics, 2011, 2(3), 333\u201335810.1080\/15427951.2005.10129104","DOI":"10.1080\/15427951.2005.10129104"}],"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/comp\/9\/1\/article-p103.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2019-0006\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2019-0006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T10:33:52Z","timestamp":1721471632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2019-0006\/html"}},"subtitle":["A unified representation model for the retrieval of text and knowledge"],"short-title":[],"issued":{"date-parts":[[2019,1,1]]},"references-count":77,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,9,26]]},"published-print":{"date-parts":[[2019,1,1]]}},"alternative-id":["10.1515\/comp-2019-0006"],"URL":"https:\/\/doi.org\/10.1515\/comp-2019-0006","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,1]]}}}