Constructing High Precision Knowledge Bases with Subjective and Factual\n Attributes Article Swipe
Ari Kobren
,
Pablo Barrio
,
Oksana Yakhnenko
,
Johann Hibschman
,
Ian Langmore
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1905.12807
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1905.12807
Knowledge bases (KBs) are the backbone of many ubiquitous applications and\nare thus required to exhibit high precision. However, for KBs that store\nsubjective attributes of entities, e.g., whether a movie is "kid friendly",\nsimply estimating precision is complicated by the inherent ambiguity in\nmeasuring subjective phenomena. In this work, we develop a method for\nconstructing KBs with tunable precision--i.e., KBs that can be made to operate\nat a specific false positive rate, despite storing both difficult-to-evaluate\nsubjective attributes and more traditional factual attributes. The key to our\napproach is probabilistically modeling user consensus with respect to each\nentity-attribute pair, rather than modeling each pair as either True or False.\nUncertainty in the model is explicitly represented and used to control the KB's\nprecision. We propose three neural networks for fitting the consensus model and\nevaluate each one on data from Google Maps--a large KB of locations and their\nsubjective and factual attributes. The results demonstrate that our learned\nmodels are well-calibrated and thus can successfully be used to control the\nKB's precision. Moreover, when constrained to maintain 95% precision, the best\nconsensus model matches the F-score of a baseline that models each\nentity-attribute pair as a binary variable and does not support tunable\nprecision. When unconstrained, our model dominates the same baseline by 12%\nF-score. Finally, we perform an empirical analysis of attribute-attribute\ncorrelations and show that leveraging them effectively contributes to reduced\nuncertainty and better performance in attribute prediction.\n
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Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/1905.12807
- https://arxiv.org/pdf/1905.12807
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286740589
All OpenAlex metadata
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https://openalex.org/W4286740589Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1905.12807Digital Object Identifier
- Title
-
Constructing High Precision Knowledge Bases with Subjective and Factual\n AttributesWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2019Year of publication
- Publication date
-
2019-05-28Full publication date if available
- Authors
-
Ari Kobren, Pablo Barrio, Oksana Yakhnenko, Johann Hibschman, Ian LangmoreList of authors in order
- Landing page
-
https://arxiv.org/abs/1905.12807Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1905.12807Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1905.12807Direct OA link when available
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Ambiguity, Computer science, Baseline (sea), Data mining, Artificial intelligence, Machine learning, Binary number, Key (lock), Control (management), Mathematics, Oceanography, Computer security, Geology, Arithmetic, Programming languageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learned\nmodels | 145 |
| abstract_inverted_index.well-calibrated | 147 |
| abstract_inverted_index.KB's\nprecision. | 112 |
| abstract_inverted_index.precision--i.e., | 54 |
| abstract_inverted_index.for\nconstructing | 50 |
| abstract_inverted_index.probabilistically | 82 |
| abstract_inverted_index.store\nsubjective | 21 |
| abstract_inverted_index.their\nsubjective | 136 |
| abstract_inverted_index.friendly",\nsimply | 31 |
| abstract_inverted_index.False.\nUncertainty | 100 |
| abstract_inverted_index.tunable\nprecision. | 186 |
| abstract_inverted_index.reduced\nuncertainty | 213 |
| abstract_inverted_index.each\nentity-attribute | 89, 176 |
| abstract_inverted_index.attribute-attribute\ncorrelations | 204 |
| abstract_inverted_index.difficult-to-evaluate\nsubjective | 70 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.31737421 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |