{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:00:34Z","timestamp":1740099634472,"version":"3.37.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030445836"},{"type":"electronic","value":"9783030445843"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-44584-3_39","type":"book-chapter","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T23:04:42Z","timestamp":1587510282000},"page":"496-508","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Orometric Methods in Bounded Metric Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1579-1151","authenticated-orcid":false,"given":"Maximilian","family":"Stubbemann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4918-6374","authenticated-orcid":false,"given":"Tom","family":"Hanika","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0570-7908","authenticated-orcid":false,"given":"Gerd","family":"Stumme","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,22]]},"reference":[{"key":"39_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/978-3-540-30115-8_7","volume-title":"Machine Learning: ECML 2004","author":"R Akbani","year":"2004","unstructured":"Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39\u201350. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30115-8_7"},{"issue":"1","key":"39_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2010.11.002","volume":"499","author":"M Barth\u00e9lemy","year":"2011","unstructured":"Barth\u00e9lemy, M.: Spatial networks. Phys. Rep. 499(1), 1\u2013101 (2011)","journal-title":"Phys. Rep."},{"key":"39_CR3","unstructured":"Bentley, J.L.: A survey of techniques for fixed radius near neighbor searching. Technical report, SLAC, SCIDOC, Stanford, CA, USA (1975). SLAC-R-0186, SLAC-0186"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Burgard, W., Roth, D. (eds.) Proceedings of the 25th Conference on Artificial Intelligence, pp. 301\u2013306. AAAI Press, Palo Alto (2011)","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"39_CR5","doi-asserted-by":"crossref","unstructured":"Catherine, R., Cohen, W.: Personalized recommendations using knowledge graphs: a probabilistic logic programming approach. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys, pp. 325\u2013332. ACM, New York (2016)","DOI":"10.1145\/2959100.2959131"},{"key":"39_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1007\/11556114_27","volume-title":"Spatial Information Theory","author":"L \u010comi\u0107","year":"2005","unstructured":"\u010comi\u0107, L., De Floriani, L., Papaleo, L.: Morse-smale decompositions for modeling terrain knowledge. In: Cohn, A.G., Mark, D.M. (eds.) COSIT 2005. LNCS, vol. 3693, pp. 426\u2013444. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11556114_27"},{"issue":"3","key":"39_CR7","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"39_CR8","unstructured":"Fry, S.: Defining and sizing-up mountains. Summit, pp. 16\u201321, January-February 1987"},{"key":"39_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/978-3-030-21462-3_21","volume-title":"Formal Concept Analysis","author":"T Hanika","year":"2019","unstructured":"Hanika, T., Marx, M., Stumme, G.: Discovering implicational knowledge in wikidata. In: Cristea, D., Le Ber, F., Sertkaya, B. (eds.) ICFCA 2019. LNCS (LNAI), vol. 11511, pp. 315\u2013323. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21462-3_21"},{"issue":"9","key":"39_CR10","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263\u20131284 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"39_CR11","volume-title":"The Finest Peaks-Prominence and Other Mountain Measures","author":"A Helman","year":"2005","unstructured":"Helman, A.: The Finest Peaks-Prominence and Other Mountain Measures. Trafford, Victoria (2005)"},{"issue":"46","key":"39_CR12","doi-asserted-by":"publisher","first-page":"16569","DOI":"10.1073\/pnas.0507655102","volume":"102","author":"JE Hirsch","year":"2005","unstructured":"Hirsch, J.E.: An index to quantify an individual\u2019s scientific research output. Proc. Nat. Acad. Sci. 102(46), 16569\u201316572 (2005)","journal-title":"Proc. Nat. Acad. Sci."},{"issue":"1","key":"39_CR13","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1068\/b306","volume":"31","author":"B Jiang","year":"2004","unstructured":"Jiang, B., Claramunt, C.: Topological analysis of urban street networks. Environ. Plan. B: Plan. Des. 31(1), 151\u2013162 (2004)","journal-title":"Environ. Plan. B: Plan. Des."},{"issue":"6","key":"39_CR14","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1177\/0309133317738163","volume":"41","author":"A Kirmse","year":"2017","unstructured":"Kirmse, A., de Ferranti, J.: Calculating the prominence and isolation of every mountain in the world. Prog. Phys. Geogr.: Earth Environ. 41(6), 788\u2013802 (2017)","journal-title":"Prog. Phys. Geogr.: Earth Environ."},{"issue":"2","key":"39_CR15","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3233\/SW-140134","volume":"6","author":"J Lehmann","year":"2015","unstructured":"Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web 6(2), 167\u2013195 (2015)","journal-title":"Semant. Web"},{"issue":"2","key":"39_CR16","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1080\/00330124.2018.1531039","volume":"71","author":"GD Nelson","year":"2019","unstructured":"Nelson, G.D., McKeon, R.: Peaks of people: using topographic prominence as a method for determining the ranked significance of population centers. Prof. Geogr. 71(2), 342\u2013354 (2019)","journal-title":"Prof. Geogr."},{"issue":"2","key":"39_CR17","doi-asserted-by":"publisher","first-page":"21:1","DOI":"10.1145\/2926718","volume":"8","author":"S Oramas","year":"2016","unstructured":"Oramas, S., Ostuni, V.C., Noia, T.D., Serra, X., Sciascio, E.D.: Sound and music recommendation with knowledge graphs. ACM Trans. Intell. Syst. Technol. 8(2), 21:1\u201321:21 (2016)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Palumbo, E., Rizzo, G., Troncy, R.: Entity2rec: learning user-item relatedness from knowledge graphs for top-n item recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 32\u201336. ACM (2017)","DOI":"10.1145\/3109859.3109889"},{"key":"39_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-319-98192-5_22","volume-title":"The Semantic Web: ESWC 2018 Satellite Events","author":"E Palumbo","year":"2018","unstructured":"Palumbo, E., Rizzo, G., Troncy, R., Baralis, E., Osella, M., Ferro, E.: Knowledge graph embeddings with node2vec for item recommendation. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 117\u2013120. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98192-5_22"},{"key":"39_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. JMLR 12, 2825\u20132830 (2011)","journal-title":"JMLR"},{"key":"39_CR21","series-title":"Oxford Studies in Probability","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198506263.001.0001","volume-title":"Random Geometric Graphs","author":"M Penrose","year":"2003","unstructured":"Penrose, M.: Random Geometric Graphs. Oxford Studies in Probability, vol. 5. Oxford University Press, Oxford (2003)"},{"key":"39_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1007\/978-3-030-03667-6_24","volume-title":"Knowledge Engineering and Knowledge Management","author":"A Schmidt","year":"2018","unstructured":"Schmidt, A., Stumme, G.: Prominence and dominance in networks. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds.) EKAW 2018. LNCS (LNAI), vol. 11313, pp. 370\u2013385. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03667-6_24"},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Torres, R.N., Fraternali, P., Milani, F., Frajberg, D.: A deep learning model for identifying mountain summits in digital elevation model data. In: First IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2018, Laguna Hills, CA, USA, 26\u201328 September 2018, pp. 212\u2013217. IEEE Computer Society (2018)","DOI":"10.1109\/AIKE.2018.00049"},{"key":"39_CR24","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrande\u010di\u0107","year":"2014","unstructured":"Vrande\u010di\u0107, D., Kr\u00f6tzsch, M.: Wikidata: a free collaborative knowledge base. Commun. ACM 57, 78\u201385 (2014)","journal-title":"Commun. ACM"},{"key":"39_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the 28th Conference on Artificial Intelligence, pp. 1112\u20131119. AAAI Press (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"39_CR26","volume-title":"Six Degrees: The Science of a Connected Age","author":"DJ Watts","year":"2003","unstructured":"Watts, D.J.: Six Degrees: The Science of a Connected Age. W. W. Norton, New York (2003)"}],"container-title":["Lecture Notes in Computer Science","Advances in Intelligent Data Analysis XVIII"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-44584-3_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T23:39:22Z","timestamp":1666395562000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-44584-3_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030445836","9783030445843"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-44584-3_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligent Data Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Konstanz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ida2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ida2020.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"114","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}