{"id":1217,"date":"2022-05-05T14:16:18","date_gmt":"2022-05-05T06:16:18","guid":{"rendered":"https:\/\/iswc2022.semanticweb.org\/?page_id=1217"},"modified":"2022-09-30T12:05:22","modified_gmt":"2022-09-30T04:05:22","slug":"challenges","status":"publish","type":"page","link":"https:\/\/iswc2022.semanticweb.org\/index.php\/challenges\/","title":{"rendered":"Challenges"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1217\" class=\"elementor elementor-1217\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ca82374 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ca82374\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-77056b8\" data-id=\"77056b8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fc24ca8 elementor-widget elementor-widget-heading\" data-id=\"fc24ca8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.17.0 - 08-11-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Semantic Web Challenges<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb6c326 elementor-widget elementor-widget-spacer\" data-id=\"bb6c326\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.17.0 - 08-11-2023 *\/\n.elementor-column .elementor-spacer-inner{height:var(--spacer-size)}.e-con{--container-widget-width:100%}.e-con-inner>.elementor-widget-spacer,.e-con>.elementor-widget-spacer{width:var(--container-widget-width,var(--spacer-size));--align-self:var(--container-widget-align-self,initial);--flex-shrink:0}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container,.e-con>.elementor-widget-spacer>.elementor-widget-container{height:100%;width:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer{height:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner{height:var(--container-widget-height,var(--spacer-size))}.e-con-inner>.elementor-widget-spacer.elementor-widget-empty,.e-con>.elementor-widget-spacer.elementor-widget-empty{position:relative;min-height:22px;min-width:22px}.e-con-inner>.elementor-widget-spacer.elementor-widget-empty .elementor-widget-empty-icon,.e-con>.elementor-widget-spacer.elementor-widget-empty .elementor-widget-empty-icon{position:absolute;top:0;bottom:0;left:0;right:0;margin:auto;padding:0;width:22px;height:22px}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-76a80da elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"76a80da\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-14169df\" data-id=\"14169df\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-342e10a elementor-widget elementor-widget-heading\" data-id=\"342e10a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Knowledge Base Construction from Pre-trained Language Models (LM-KBC)<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/iswc2022.semanticweb.org\/wp-content\/uploads\/2022\/05\/new.png\" alt=\" \" height=\"20\" width=\"40\"><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-455a68c elementor-widget elementor-widget-text-editor\" data-id=\"455a68c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.17.0 - 08-11-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<b>Organizer<\/b>: Sneha Singhania, Tuan-Phong Nguyen, Simon Razniewski\n<br>\n<br>\n\nPre-trained language models (LMs) have advanced a range of semantic tasks and have also shown promise for knowledge extraction from the models itself. Although several works have explored this ability in a setting called probing or prompting, the viability of knowledge base construction from LMs has not yet been explored. In this challenge, participants are asked to build actual knowledge bases from LMs, for given subjects and relations. In crucial difference to existing probing benchmarks like LAMA (Petroni et al., 2019), we make no simplifying assumptions on relation cardinalities, i.e., a subject-entity can stand in relation with zero, one, or many object-entities. Furthermore, submissions need to go beyond just ranking the predictions, and materialize outputs, which are evaluated by established KB metrics of precision and recall. The challenge comes with two tracks: (i) a BERT-type LM track with low computational requirements, and (ii) an open track, where participants can use any LM of their choice.\n\n<br>\n<br>\n\n<b>Website: <\/b><a href=\" http:\/\/lm-kbc.github.io\">\nhttp:\/\/lm-kbc.github.io<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7191fe9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7191fe9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-24c00f8\" data-id=\"24c00f8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-116f1a8 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"116f1a8\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.17.0 - 08-11-2023 *\/\n.elementor-widget-divider{--divider-border-style:none;--divider-border-width:1px;--divider-color:#0c0d0e;--divider-icon-size:20px;--divider-element-spacing:10px;--divider-pattern-height:24px;--divider-pattern-size:20px;--divider-pattern-url:none;--divider-pattern-repeat:repeat-x}.elementor-widget-divider .elementor-divider{display:flex}.elementor-widget-divider .elementor-divider__text{font-size:15px;line-height:1;max-width:95%}.elementor-widget-divider .elementor-divider__element{margin:0 var(--divider-element-spacing);flex-shrink:0}.elementor-widget-divider .elementor-icon{font-size:var(--divider-icon-size)}.elementor-widget-divider .elementor-divider-separator{display:flex;margin:0;direction:ltr}.elementor-widget-divider--view-line_icon .elementor-divider-separator,.elementor-widget-divider--view-line_text .elementor-divider-separator{align-items:center}.elementor-widget-divider--view-line_icon .elementor-divider-separator:after,.elementor-widget-divider--view-line_icon .elementor-divider-separator:before,.elementor-widget-divider--view-line_text .elementor-divider-separator:after,.elementor-widget-divider--view-line_text .elementor-divider-separator:before{display:block;content:\"\";border-bottom:0;flex-grow:1;border-top:var(--divider-border-width) var(--divider-border-style) var(--divider-color)}.elementor-widget-divider--element-align-left .elementor-divider .elementor-divider-separator>.elementor-divider__svg:first-of-type{flex-grow:0;flex-shrink:100}.elementor-widget-divider--element-align-left .elementor-divider-separator:before{content:none}.elementor-widget-divider--element-align-left .elementor-divider__element{margin-left:0}.elementor-widget-divider--element-align-right .elementor-divider .elementor-divider-separator>.elementor-divider__svg:last-of-type{flex-grow:0;flex-shrink:100}.elementor-widget-divider--element-align-right .elementor-divider-separator:after{content:none}.elementor-widget-divider--element-align-right .elementor-divider__element{margin-right:0}.elementor-widget-divider:not(.elementor-widget-divider--view-line_text):not(.elementor-widget-divider--view-line_icon) .elementor-divider-separator{border-top:var(--divider-border-width) var(--divider-border-style) var(--divider-color)}.elementor-widget-divider--separator-type-pattern{--divider-border-style:none}.elementor-widget-divider--separator-type-pattern.elementor-widget-divider--view-line .elementor-divider-separator,.elementor-widget-divider--separator-type-pattern:not(.elementor-widget-divider--view-line) .elementor-divider-separator:after,.elementor-widget-divider--separator-type-pattern:not(.elementor-widget-divider--view-line) .elementor-divider-separator:before,.elementor-widget-divider--separator-type-pattern:not([class*=elementor-widget-divider--view]) .elementor-divider-separator{width:100%;min-height:var(--divider-pattern-height);-webkit-mask-size:var(--divider-pattern-size) 100%;mask-size:var(--divider-pattern-size) 100%;-webkit-mask-repeat:var(--divider-pattern-repeat);mask-repeat:var(--divider-pattern-repeat);background-color:var(--divider-color);-webkit-mask-image:var(--divider-pattern-url);mask-image:var(--divider-pattern-url)}.elementor-widget-divider--no-spacing{--divider-pattern-size:auto}.elementor-widget-divider--bg-round{--divider-pattern-repeat:round}.rtl .elementor-widget-divider .elementor-divider__text{direction:rtl}.e-con-inner>.elementor-widget-divider,.e-con>.elementor-widget-divider{width:var(--container-widget-width,100%);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3ee3fc4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3ee3fc4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-18ffea3\" data-id=\"18ffea3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-27cdaa0 elementor-widget elementor-widget-heading\" data-id=\"27cdaa0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Semantic Answer Type, Entity, and Relation Linking Task (SMART2022) <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/iswc2022.semanticweb.org\/wp-content\/uploads\/2022\/05\/3rd.png\" alt=\" \" height=\"20\" width=\"40\"><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e61d0a elementor-widget elementor-widget-text-editor\" data-id=\"6e61d0a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<b>Organizer<\/b>: Nandana Mihindukulasooriya, Mohnish Dubey, Al\ufb01o Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck, Gaetano Rossiello and Debayan Banerjee\n<br>\n<br>\n\nThe challenge, SMART 2022, consists of three tasks Semantic Answer Type prediction task, Entity and Relation Linking tasks. Given a question in natural language, the three tasks in this challenge are to predict the answer type for a given question, identify entities and relations in the questions using a target ontology (e.g., DBpedia or Wikidata). This is a continuation of SMART2020, and SMART2021 challenges.\n<br>\n<br>\n\n<b>Website: <\/b><a href=\"https:\/\/smart-task.github.io\/\">https:\/\/smart-task.github.io\/<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ac70f58 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ac70f58\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-86c881f\" data-id=\"86c881f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f991ffa elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"f991ffa\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-10927bf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"10927bf\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f5c4e4a\" data-id=\"f5c4e4a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d486b46 elementor-widget elementor-widget-heading\" data-id=\"d486b46\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Semantic Reasoning Evaluation Challenge (SemREC 2022) <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/iswc2022.semanticweb.org\/wp-content\/uploads\/2022\/05\/2nd.png\" alt=\" \" height=\"20\" width=\"40\"><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e3b5178 elementor-widget elementor-widget-text-editor\" data-id=\"e3b5178\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<b>Organizer<\/b>: \nGunjan Singh, Raghava Mutharaju, Pavan Kapanipathi\n\n<br>\n<br>\nDespite the development of several ontology reasoning optimizations, the traditional methods either do not scale well or only cover a subset of OWL 2 language constructs. As an alternative, neuro-symbolic approaches are gaining significant attention. However, the existing methods can not deal with very expressive ontology languages. Other than that, some SPARQL query engines also support reasoning, but their performance also is still limited. To find and improve these performance bottlenecks of the reasons, we ideally need several real-world ontologies that span the broad spectrum in terms of their size and expressivity. However, that is often not the case. One of the potential reasons for the ontology developers to not build ontologies that vary in terms of size and expressivity is the performance bottleneck of the reasons. SemREC aims to deal with this chicken and egg problem.\n<br>\nThe second edition of this challenge includes the following tasks-\n<br>\nTask-1. Ontologies.\n<br>\nSubmit a real-world ontology that is a challenge in\nterms of the reasoning time or memory consumed during reasoning.\n<br>\nTask-2. Systems.\n<br>\nSubmit an ontology\/RDFS reasoner that uses neural-symbolic techniques or SPARQL query engines that support reasoning.\n \n<br>\n<br>\n\n<b>Website: <\/b><a href=\"\nhttps:\/\/semrec.github.io\/\">\nhttps:\/\/semrec.github.io\/<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-00ea815 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"00ea815\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-13a4a2c\" data-id=\"13a4a2c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-755962c elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"755962c\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1171a40 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1171a40\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5355aeb\" data-id=\"5355aeb\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2e7722d elementor-widget elementor-widget-heading\" data-id=\"2e7722d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2022) <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/iswc2022.semanticweb.org\/wp-content\/uploads\/2022\/05\/4th.png\" alt=\" \" height=\"20\" width=\"40\"><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab190da elementor-widget elementor-widget-text-editor\" data-id=\"ab190da\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Organizer<\/b>: Nora Abdelmageed, Jiaoyan Chen, Vincenzo Cutrona, Vasilis Efthymiou, Oktie Hassanzadeh, Madelon Hulsebos, Ernesto Jimenez-Ruiz, Juan F. Sequeda, Kavitha Srinivas<\/p><p>This challenge aims at benchmarking systems dealing with the tabular data to KG matching problem, so as to facilitate their comparison on the same basis and the reproducibility of the results.<\/p><p><b>Website: <\/b><a href=\"https:\/\/www.cs.ox.ac.uk\/isg\/challenges\/sem-tab\/\">https:\/\/www.cs.ox.ac.uk\/isg\/challenges\/sem-tab\/<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ec05e54 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ec05e54\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-be8daf6\" data-id=\"be8daf6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7f9aed1 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7f9aed1\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d359fa6 elementor-widget elementor-widget-heading\" data-id=\"d359fa6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Bio2RDF and Kibio federated query in Life Science challenge <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29eed07 elementor-widget elementor-widget-text-editor\" data-id=\"29eed07\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Organizer<\/b>: Fran\u00e7ois Belleau, Mickael Leclerc, R\u00e9gis Ongaro-Carcy, Arnaud Droit<\/p><p>How can we run the Wikidata FAIR paper query without using Wikidata SPARQL endpoint (https:\/\/tinyurl.com\/53ra32bp)? Participants must obtain the same results by using federated query techniques.<\/p><p>Two different tasks are proposed, participant select one:<br \/>(1)Running Wikidata query using federated SPARQL query or other programming approach that will consume Bio2RDF\u2019s endpoints.<br \/>(2)Running Wikidata query using any programming approach that will consume Kibio\u2019s Elasticsearch indexes.<\/p><p>Both endpoints, Bio2RDF and Kibio, store the same datasets extracted from the Wikidata JSON dump. Bio2RDF exposing SPARQL endpoints and RDF triples, Kibio.science exposing Elasticsearch standard API and JSON-LD documents.<\/p><p><strong>Website<\/strong>:http:\/\/kibio.science\/iswc2022<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Semantic Web Challenges Knowledge Base Construction from Pre-trained Language Models (LM-KBC) Organizer: Sneha Singhania, Tuan-Phong Nguyen, Simon Razniewski Pre-trained language models (LMs) have advanced a range of semantic tasks and have also shown promise for knowledge extraction from the models itself. Although several works have explored this ability in a setting called probing or prompting, [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/pages\/1217"}],"collection":[{"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/comments?post=1217"}],"version-history":[{"count":40,"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/pages\/1217\/revisions"}],"predecessor-version":[{"id":1923,"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/pages\/1217\/revisions\/1923"}],"wp:attachment":[{"href":"https:\/\/iswc2022.semanticweb.org\/index.php\/wp-json\/wp\/v2\/media?parent=1217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}