{"id":144919,"date":"2013-12-04T00:14:08","date_gmt":"2013-12-04T08:14:08","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/group\/knowledge-mining-km\/"},"modified":"2022-09-02T16:19:21","modified_gmt":"2022-09-02T23:19:21","slug":"knowledge-computing","status":"publish","type":"msr-group","link":"https:\/\/www.microsoft.com\/en-us\/research\/group\/knowledge-computing\/","title":{"rendered":"Knowledge Computing"},"content":{"rendered":"
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\tReturn to Microsoft Research Lab – Asia\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n

Knowledge Computing<\/h1>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

The Knowledge Computing group at Microsoft Research Asia aims to build machines that can make good use of knowledge to empower every person and every organization on the planet to achieve more. Natural language processing (NLP), information extraction, table interpretation, and knowledge representation & reasoning are four main focus areas. Natural language processing (NLP) analyzes, understands, and generates languages for effective and efficient human-machine communication. Information extraction (IE) recognizes entity mentions, mention types, named entities, and entity-entity relations to create structured data from natural language texts. Table interpretation (TI) detects column types, cell entities, and column-column relations to facilitate question answering over tables. Knowledge representation & reasoning (KRR) provides the foundation for NLP, IE and TI to represent knowledge symbolically and enable automated reasoning and computation over the representation.<\/p>\n\n\n\n

Over the years, we have worked closely with our product team partners at Office 365 (opens in new tab)<\/span><\/a>, Azure Cognitive Services (opens in new tab)<\/span><\/a>, and Bing (opens in new tab)<\/span><\/a> to bring our research results into Microsoft products and services. Microsoft Forms Design Intelligence (opens in new tab)<\/span><\/a>, PowerPoint Designer (opens in new tab)<\/span><\/a>,\u00a0Excel Data Types AutoDetect<\/a>, Azure Text Analytics (opens in new tab)<\/span><\/a>, Microsoft Video Indexer (opens in new tab)<\/span><\/a>, and Microsoft Recognizers-Text (open source) (opens in new tab)<\/span><\/a> are just a few recent examples which have incorporated technologies developed by the Knowledge Computing group.<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"

The Knowledge Computing group at Microsoft Research Asia aims to build machines that can make good use of knowledge to empower every person on the planet to achieve more.\u00a0Natural\u00a0language\u00a0processing,\u00a0information\u00a0extraction,\u00a0table\u00a0interpretation, and\u00a0knowledge\u00a0representation &\u00a0reasoning\u00a0are four\u00a0main focus\u00a0areas.\u00a0Natural\u00a0language\u00a0processing\u00a0(NLP)\u00a0analyzes,\u00a0understands, and\u00a0generates\u00a0languages\u00a0for\u00a0effective and efficient human-machine communication.\u00a0Information extraction\u00a0(IE)\u00a0recognizes\u00a0entity\u00a0mentions,\u00a0mention types,\u00a0named\u00a0entities,\u00a0and\u00a0entity-entity relations\u00a0to create structured data from\u00a0natural\u00a0language\u00a0texts.\u00a0Table interpretation\u00a0(TI)\u00a0detects\u00a0column types, cell\u00a0entities, and\u00a0column-column\u00a0relations to\u00a0facilitate\u00a0question answering over\u00a0tables.\u00a0Knowledge representation & reasoning\u00a0(KRR) provides\u00a0the\u00a0foundation for NLP,\u00a0IE\u00a0and TI to represent knowledge symbolically\u00a0and enable automated reasoning\u00a0and computation\u00a0over the representation.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_group_start":"","footnotes":""},"research-area":[13556,13545,13555],"msr-group-type":[243694],"msr-locale":[268875],"msr-impact-theme":[264846],"class_list":["post-144919","msr-group","type-msr-group","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-search-information-retrieval","msr-group-type-group","msr-locale-en_us"],"msr_group_start":"","msr_detailed_description":"","msr_further_details":"","msr_hero_images":[],"msr_research_lab":[199560],"related-researchers":[{"type":"user_nicename","display_name":"Danqing Huang","user_id":38724,"people_section":"Section name 0","alias":"dahua"},{"type":"user_nicename","display_name":"Chin-Yew Lin","user_id":31493,"people_section":"Section name 0","alias":"cyl"},{"type":"user_nicename","display_name":"Qianhui Wu","user_id":40741,"people_section":"Section name 0","alias":"qianhuiwu"}],"related-publications":[168543,168545,168548,168550,168877,168878,215142,259830,259836,259842,259857,259863,259869,259875,259881,259887,259893,259899,259917,259923,259929,259938,259944,259950,259956,259962,259968,259974,259980,259986,259992,259998,260004,260010,260016,260022,260031,260037,260043,260049,260055,260061,260070,260082,260094,260109,260118,294959,294998,295007,331316,354626,554901,554907,614520,636348,650316,650322,650331,650337,650343,654678,654684,713086,714352,714370,714379,714388,714394,714400,714409,714415,714574,714607,714613,714631,714637,714655,714673,714679,714688,714694,714700,714706,714712,714718,714724,714730,714736,714748,714754,714760,714766,714772,714784,714790,714796,715219,715570,715579,715891,716125,716134,716140,716146,716152,716161,716167,716176,716182,735946,735964,751540,763942,763948,784855,784864,837127,840871,840880,840886,854748,895557,921747,941109,944742,974562,978312,1016619,1043472],"related-downloads":[],"related-videos":[],"related-projects":[751528,792599,717085,717721,171492,714646],"related-events":[],"related-opportunities":[],"related-posts":[738559],"tab-content":[],"msr_impact_theme":["Computing foundations"],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/144919","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-group"}],"version-history":[{"count":14,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/144919\/revisions"}],"predecessor-version":[{"id":875307,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group\/144919\/revisions\/875307"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=144919"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=144919"},{"taxonomy":"msr-group-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-group-type?post=144919"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=144919"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=144919"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}