{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:30:11Z","timestamp":1761611411987},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p>The increasing popularity of mobile device usage has ushered in many features in modern search engines that help users with various information needs. One of those needs is Knowledge Exploration, where related documents are returned in response to a user query, either directly through right-hand side knowledge panels or indirectly through navigable sections underneath individual search results. Existing knowledge exploration features have relied on a combination of Knowledge Bases and query logs.<\/jats:p>\n          <jats:p>In this paper, we propose Knowledge Carousels of two modalities, namely sideways and downwards, that facilitate exploration of IS-A and HAS-A relationships, respectively, with regard to an entity-seeking query, based on leveraging the large corpus of tables on the Web. This brings many technical challenges, including associating correct carousels with the search entity, selecting the best carousel from the candidates, and finding titles that best describe the carousel. We describe how we address these challenges and also experimentally demonstrate through user studies that our approach produces better result sets than baseline approaches.<\/jats:p>","DOI":"10.14778\/3021924.3021935","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"193-204","source":"Crossref","is-referenced-by-count":18,"title":["Knowledge exploration using tables on the web"],"prefix":"10.14778","volume":"10","author":[{"given":"Fernando","family":"Chirigati","sequence":"first","affiliation":[{"name":"New York University"}]},{"given":"Jialu","family":"Liu","sequence":"additional","affiliation":[{"name":"Google Research NYC"}]},{"given":"Flip","family":"Korn","sequence":"additional","affiliation":[{"name":"Google Research NYC"}]},{"given":"You (Will)","family":"Wu","sequence":"additional","affiliation":[{"name":"Google Research NYC"}]},{"given":"Cong","family":"Yu","sequence":"additional","affiliation":[{"name":"Google Research NYC"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Google Research NYC"}]}],"member":"320","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772692"},{"key":"e_1_2_1_2_1","volume-title":"CIDR","author":"Balakrishnan S.","year":"2015","unstructured":"S. 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