{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/data-curation-apis","title":"Data Curation APIs","arxiv_id":"1612.03277","date":"2016-12-10","proceeding":null,"authors":["Seyed-Mehdi-Reza Beheshti","Alireza Tabebordbar","Boualem Benatallah","Reza Nouri"],"abstract":"Understanding and analyzing big data is firmly recognized as a powerful and\nstrategic priority. For deeper interpretation of and better intelligence with\nbig data, it is important to transform raw data (unstructured, semi-structured\nand structured data sources, e.g., text, video, image data sets) into curated\ndata: contextualized data and knowledge that is maintained and made available\nfor use by end-users and applications. In particular, data curation acts as the\nglue between raw data and analytics, providing an abstraction layer that\nrelieves users from time consuming, tedious and error prone curation tasks. In\nthis context, the data curation process becomes a vital analytics asset for\nincreasing added value and insights.\n  In this paper, we identify and implement a set of curation APIs and make them\navailable (on GitHub) to researchers and developers to assist them transforming\ntheir raw data into curated data. The curation APIs enable developers to easily\nadd features - such as extracting keyword, part of speech, and named entities\nsuch as Persons, Locations, Organizations, Companies, Products, Diseases,\nDrugs, etc.; providing synonyms and stems for extracted information items\nleveraging lexical knowledge bases for the English language such as WordNet;\nlinking extracted entities to external knowledge bases such as Google Knowledge\nGraph and Wikidata; discovering similarity among the extracted information\nitems, such as calculating similarity between string, number, date and time\ndata; classifying, sorting and categorizing data into various types, forms or\nany other distinct class; and indexing structured and unstructured data - into\ntheir applications.","url_abs":"http://arxiv.org/abs/1612.03277v1","url_pdf":"http://arxiv.org/pdf/1612.03277v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"data-curation-apis","repo_url":"https://github.com/unsw-cse-soc/Data-curation-API","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}