In this paper, we present LinkingPark, our system for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020). LinkingPark is an integrated approach for semantic table interpretation. Our system includes a cascaded pipeline for candidate generation, an iterative coarse-to-fine entity disambiguation algorithm, a multi-pass property linking algorithm, and a type inference algorithm tackling the issue of loose ontology in Wikidata. Results on SemTab 2020 demonstrate the effectiveness of our approach.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we present LinkingPark, our system for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020). LinkingPark is an integrated approach for semantic table interpretation. Our system includes a cascaded pipeline for candidate generation, an iterative coarse-to-fine entity disambiguation algorithm, a multi-pass property linking algorithm, and a type inference algorithm […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[246574],"research-area":[13556,13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[248503,248533,248536],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-713086","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-highlight-award","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-locale-en_us","msr-field-of-study-information-retrieval","msr-field-of-study-ml","msr-field-of-study-nlp"],"msr_publishername":"ceur-ws","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-12-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"Ernesto Jim\u00e9nez-Ruiz, Oktie Hassanzadeh, Vasilis Efthymiou, Jiaoyan Chen, Kavitha Srinivas, Vincenzo Cutrona","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"2nd Prize in SemTab Competition","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/ceur-ws.org\/Vol-2775\/paper7.pdf","label_id":"243132","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Shuang Chen","user_id":0,"rest_url":false},{"type":"text","value":"Alperen Karaoglu","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Carina Negreanu","user_id":40924,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Carina Negreanu"},{"type":"text","value":"Tingting Ma","user_id":0,"rest_url":false},{"type":"text","value":"Jin-Ge Yao","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Jack Williams","user_id":40156,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jack Williams"},{"type":"user_nicename","value":"Andy Gordon","user_id":30825,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Andy Gordon"},{"type":"user_nicename","value":"Chin-Yew Lin","user_id":31493,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Chin-Yew Lin"}],"msr_impact_theme":[],"msr_research_lab":[199560,199561],"msr_event":[],"msr_group":[144919],"msr_project":[792599,511097,714646],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":792599,"post_title":"Table Interpretation","post_name":"table-interpretation","post_type":"msr-project","post_date":"2021-11-05 02:02:36","post_modified":"2024-09-25 11:42:48","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/table-interpretation\/","post_excerpt":"Bringing out the power of semantics in tabular data Tables are commonly used to organize information, playing a key role in data analytics, scientific research, and business communication. The ability to automatically extract semantics in tables can empower many downstream applications such as data analytics, robotic process automation (RPA), knowledge base population, etc. In this project, we explore multiple aspects of semantic table understanding and real-world applications of such technologies. 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