{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T05:20:57Z","timestamp":1708060857892},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            K\n            <jats:sc>oko<\/jats:sc>\n            is a declarative information extraction system that incorporates advances in natural language processing techniques in its extraction language. K\n            <jats:sc>oko<\/jats:sc>\n            's extraction language supports simultaneous specification of conditions over the surface syntax and on the structure of the dependency parse tree of sentences, thereby allowing for more refined extractions. Furthermore, the K\n            <jats:sc>oko<\/jats:sc>\n            extraction language allows for aggregating evidence from an input document and supports conditions that are tolerant of linguistic variation of expressing concepts.\n          <\/jats:p>\n          <jats:p>\n            In this demo, we outline the design of K\n            <jats:sc>oko<\/jats:sc>\n            , a system for extracting information and understanding the results of the extraction. K\n            <jats:sc>oko<\/jats:sc>\n            provides an interactive interface that allows participants to write queries, understand the input and results of the queries. In particular, the user can customize the input text, visualize the input text's dependency parse trees, and understand the correspondences between query components, dependency tree nodes, text tokens, and the computation and associated scores that led to an extraction.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236249","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"2018-2021","source":"Crossref","is-referenced-by-count":2,"title":["Koko"],"prefix":"10.14778","volume":"11","author":[{"given":"Xiaolan","family":"Wang","sequence":"first","affiliation":[{"name":"University of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyu","family":"Komiya","sequence":"additional","affiliation":[{"name":"Megagon Labs"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihiko","family":"Suhara","sequence":"additional","affiliation":[{"name":"Megagon Labs"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Feng","sequence":"additional","affiliation":[{"name":"Megagon Labs"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Behzad","family":"Golshan","sequence":"additional","affiliation":[{"name":"Megagon Labs"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alon","family":"Halevy","sequence":"additional","affiliation":[{"name":"Megagon Labs"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang-Chiew","family":"Tan","sequence":"additional","affiliation":[{"name":"Megagon Labs"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proc. LREC","author":"Asai A.","year":"2018","unstructured":"A. Asai , S. Evensen , B. Golshan , A. Halevy , V. Li , A. Lopatenko , D. Stepanov , Y. Suhara , W.-C. Tan , and Y. Xu . HappyDB: A corpus of 100,000 crowdsourced happy moments . In Proc. LREC , 2018 (to appear). https:\/\/rit-public.github.io\/HappyDB\/. A. Asai, S. Evensen, B. Golshan, A. Halevy, V. Li, A. Lopatenko, D. Stepanov, Y. Suhara, W.-C. Tan, and Y. Xu. HappyDB: A corpus of 100,000 crowdsourced happy moments. In Proc. LREC, 2018 (to appear). https:\/\/rit-public.github.io\/HappyDB\/."},{"key":"e_1_2_1_2_1","first-page":"827","volume-title":"EMNLP","author":"Chiticariu L.","year":"2013","unstructured":"L. Chiticariu , Y. Li , and F. R. Reiss . Rule-based information extraction is dead! long live rule-based information extraction systems! In Proc . EMNLP , pages 827 -- 832 , 2013 . L. Chiticariu, Y. Li, and F. R. Reiss. Rule-based information extraction is dead! long live rule-based information extraction systems! In Proc. EMNLP, pages 827--832, 2013."},{"key":"e_1_2_1_3_1","unstructured":"Google Cloud Natural Language API. https:\/\/cloud.google.com\/natural-language\/ Google Cloud Natural Language API. https:\/\/cloud.google.com\/natural-language\/"},{"key":"e_1_2_1_4_1","first-page":"92","volume-title":"Proc. ACL","author":"McDonald R. T.","year":"2013","unstructured":"R. T. McDonald , J. Nivre , Y. Quirmbach-Brundage , Y. Goldberg , D. Das , K. Ganchev , K. B. Hall , S. Petrov , H. Zhang , O. T\u00e4ckstr\u00f6m , C. Bedini , N. B. Castell\u00f3 , and J. Lee . Universal dependency annotation for multilingual parsing . In Proc. ACL , pages 92 -- 97 , 2013 . R. T. McDonald, J. Nivre, Y. Quirmbach-Brundage, Y. Goldberg, D. Das, K. Ganchev, K. B. Hall, S. Petrov, H. Zhang, O. T\u00e4ckstr\u00f6m, C. Bedini, N. B. Castell\u00f3, and J. Lee. Universal dependency annotation for multilingual parsing. In Proc. ACL, pages 92--97, 2013."},{"key":"e_1_2_1_5_1","first-page":"2089","volume-title":"Proc. LREC","author":"Petrov S.","year":"2012","unstructured":"S. Petrov , D. Das , and R. T. McDonald . A universal part-of-speech tagset . In Proc. LREC , pages 2089 -- 2096 , 2012 . S. Petrov, D. Das, and R. T. McDonald. A universal part-of-speech tagset. In Proc. LREC, pages 2089--2096, 2012."},{"key":"e_1_2_1_6_1","unstructured":"spaCy: Industrial-Strength Natural Language Processing in Python. https:\/\/spacy.io\/. spaCy: Industrial-Strength Natural Language Processing in Python. https:\/\/spacy.io\/."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/3213880.3213887"},{"key":"e_1_2_1_8_1","unstructured":"XML Path Language (XPath). Xml path language. https:\/\/www.w3.org\/TR\/xpath\/.  XML Path Language (XPath). Xml path language. https:\/\/www.w3.org\/TR\/xpath\/."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236249","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:06:32Z","timestamp":1672221992000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236249"}},"subtitle":["a system for scalable semantic querying of text"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236249"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236249","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}