{"id":182492,"date":"2008-06-02T00:00:00","date_gmt":"2009-10-31T09:42:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/harvesting-searching-and-ranking-knowledge-from-the-web\/"},"modified":"2016-09-09T09:46:57","modified_gmt":"2016-09-09T16:46:57","slug":"harvesting-searching-and-ranking-knowledge-from-the-web","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/harvesting-searching-and-ranking-knowledge-from-the-web\/","title":{"rendered":"Harvesting, Searching, and Ranking Knowledge from the Web"},"content":{"rendered":"<div class=\"asset-content\">\n<p>There is a trend to advance the functionality of search engines to a more<br \/>\nexpressive semantic level. This is enabled by employing large-scale information extraction<br \/>\nof entities and relationships from semistructured as well as natural-language Web sources.<br \/>\nIn addition, harnessing Semantic-Web-style ontologies and reaching into Deep-Web sources<br \/>\ncan contribute towards a grand vision of turning the Web into a comprehensive knowledge<br \/>\nbase that can be efficiently searched with high precision.<\/p>\n<p>This talk presents ongoing research at the Max-Planck Institute for Informatics<br \/>\ntowards this objective, centered around the YAGO knowledge base and<br \/>\nthe NAGA search engine. YAGO is a large collection of entities and relational facts that<br \/>\nare harvested from Wikipedia and WordNet with high accuracy and reconciled into a consistent<br \/>\nRDF-style &#8220;semantic&#8221; graph. NAGA provides graph-template-based search over this<br \/>\ndata, with powerful ranking capabilities based on a statistical language model for graphs.<br \/>\nAdvanced queries and the need for ranking approximate matches pose efficiency<br \/>\nand scalability challenges that are addressed by algorithmic and indexing techniques.<\/p>\n<p>This is joint work with Georgiana Ifrim, Gjergji Kasneci, Maya Ramanath, and Fabian Suchanek.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>There is a trend to advance the functionality of search engines to a more expressive semantic level. This is enabled by employing large-scale information extraction of entities and relationships from semistructured as well as natural-language Web sources. In addition, harnessing Semantic-Web-style ontologies and reaching into Deep-Web sources can contribute towards a grand vision of turning [&hellip;]<\/p>\n","protected":false},"featured_media":194647,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-182492","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/-Cnc_QG6_Fs","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182492","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182492\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/194647"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=182492"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=182492"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=182492"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=182492"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=182492"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=182492"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=182492"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=182492"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=182492"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=182492"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}