Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset Pages Article Swipe
Tarfah Alrashed
,
Dimitris Paparas
,
Omar Benjelloun
,
Ying Sheng
,
Natasha Noy
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-88361-4_20
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-88361-4_20
Semantic markup, such as , allows providers on the Web to describe content using a shared controlled vocabulary. This markup is invaluable in enabling a broad range of applications, from vertical search engines, to rich snippets in search results, to actions on emails, to many others. In this paper, we focus on semantic markup for datasets, specifically in the context of developing a vertical search engine for datasets on the Web, Google’s Dataset Search. Dataset Search relies on to identify pages that describe datasets. While was the core enabling technology for this vertical search, we also discovered that we need to address the following problem: pages from 61% of internet hosts that provide markup do not actually describe datasets. We analyze the veracity of dataset markup for Dataset Search’s Web-scale corpus and categorize pages where this markup is not reliable. We then propose a way to drastically increase the quality of the dataset metadata corpus by developing a deep neural-network classifier that identifies whether or not a page with markup is a dataset page. Our classifier achieves 96.7% recall at the 95% precision point. This level of precision enables Dataset Search to circumvent the noise in semantic markup and to use the metadata to provide high quality results to users.
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- https://link.springer.com/content/pdf/10.1007%2F978-3-030-88361-4_20.pdf
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Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset PagesWork title
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book-chapterOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-01-01Full publication date if available
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Tarfah Alrashed, Dimitris Paparas, Omar Benjelloun, Ying Sheng, Natasha NoyList of authors in order
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https://doi.org/10.1007/978-3-030-88361-4_20Publisher landing page
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https://link.springer.com/content/pdf/10.1007%2F978-3-030-88361-4_20.pdfDirect link to full text PDF
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007%2F978-3-030-88361-4_20.pdfDirect OA link when available
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Computer science, Markup language, Information retrieval, Metadata, World Wide Web, HTML, The Internet, Artificial intelligence, XMLTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.rich | 35 |
| abstract_inverted_index.such | 3 |
| abstract_inverted_index.that | 82, 98, 112, 162 |
| abstract_inverted_index.then | 142 |
| abstract_inverted_index.this | 48, 92, 136 |
| abstract_inverted_index.with | 169 |
| abstract_inverted_index.96.7% | 178 |
| abstract_inverted_index.While | 85 |
| abstract_inverted_index.broad | 26 |
| abstract_inverted_index.focus | 51 |
| abstract_inverted_index.hosts | 111 |
| abstract_inverted_index.level | 186 |
| abstract_inverted_index.noise | 195 |
| abstract_inverted_index.page. | 174 |
| abstract_inverted_index.pages | 81, 106, 134 |
| abstract_inverted_index.range | 27 |
| abstract_inverted_index.using | 14 |
| abstract_inverted_index.where | 135 |
| abstract_inverted_index.Search | 76, 191 |
| abstract_inverted_index.allows | 6 |
| abstract_inverted_index.corpus | 131, 155 |
| abstract_inverted_index.engine | 66 |
| abstract_inverted_index.markup | 20, 54, 114, 126, 137, 170, 198 |
| abstract_inverted_index.paper, | 49 |
| abstract_inverted_index.point. | 184 |
| abstract_inverted_index.recall | 179 |
| abstract_inverted_index.relies | 77 |
| abstract_inverted_index.search | 32, 38, 65 |
| abstract_inverted_index.shared | 16 |
| abstract_inverted_index.users. | 210 |
| abstract_inverted_index.Dataset | 73, 75, 128, 190 |
| abstract_inverted_index.Search. | 74 |
| abstract_inverted_index.actions | 41 |
| abstract_inverted_index.address | 102 |
| abstract_inverted_index.analyze | 121 |
| abstract_inverted_index.content | 13 |
| abstract_inverted_index.context | 60 |
| abstract_inverted_index.dataset | 125, 153, 173 |
| abstract_inverted_index.emails, | 43 |
| abstract_inverted_index.enables | 189 |
| abstract_inverted_index.markup, | 2 |
| abstract_inverted_index.others. | 46 |
| abstract_inverted_index.propose | 143 |
| abstract_inverted_index.provide | 113, 205 |
| abstract_inverted_index.quality | 150, 207 |
| abstract_inverted_index.results | 208 |
| abstract_inverted_index.search, | 94 |
| abstract_inverted_index.whether | 164 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Semantic | 1 |
| abstract_inverted_index.achieves | 177 |
| abstract_inverted_index.actually | 117 |
| abstract_inverted_index.datasets | 68 |
| abstract_inverted_index.describe | 12, 83, 118 |
| abstract_inverted_index.enabling | 24, 89 |
| abstract_inverted_index.engines, | 33 |
| abstract_inverted_index.identify | 80 |
| abstract_inverted_index.increase | 148 |
| abstract_inverted_index.internet | 110 |
| abstract_inverted_index.metadata | 154, 203 |
| abstract_inverted_index.problem: | 105 |
| abstract_inverted_index.results, | 39 |
| abstract_inverted_index.semantic | 53, 197 |
| abstract_inverted_index.snippets | 36 |
| abstract_inverted_index.veracity | 123 |
| abstract_inverted_index.vertical | 31, 64, 93 |
| abstract_inverted_index.Web-scale | 130 |
| abstract_inverted_index.datasets, | 56 |
| abstract_inverted_index.datasets. | 84, 119 |
| abstract_inverted_index.following | 104 |
| abstract_inverted_index.precision | 183, 188 |
| abstract_inverted_index.providers | 7 |
| abstract_inverted_index.reliable. | 140 |
| abstract_inverted_index.Google’s | 72 |
| abstract_inverted_index.Search’s | 129 |
| abstract_inverted_index.categorize | 133 |
| abstract_inverted_index.circumvent | 193 |
| abstract_inverted_index.classifier | 161, 176 |
| abstract_inverted_index.controlled | 17 |
| abstract_inverted_index.developing | 62, 157 |
| abstract_inverted_index.discovered | 97 |
| abstract_inverted_index.identifies | 163 |
| abstract_inverted_index.invaluable | 22 |
| abstract_inverted_index.technology | 90 |
| abstract_inverted_index.drastically | 147 |
| abstract_inverted_index.vocabulary. | 18 |
| abstract_inverted_index.specifically | 57 |
| abstract_inverted_index.applications, | 29 |
| abstract_inverted_index.neural-network | 160 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.86728811 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |