Abstract
In knowledge bases such as Wikidata, it is possible to assert a large set of properties for entities, ranging from generic ones such as name and place of birth to highly profession-specific or background-specific ones such as doctoral advisor or medical condition. Determining a preference or ranking in this large set is a challenge in tasks such as prioritisation of edits or natural-language generation. Most previous approaches to ranking knowledge base properties are purely data-driven, that is, as we show, mistake frequency for interestingness. In this work, we have developed a human-annotated dataset of 350 preference judgments among pairs of knowledge base properties for fixed entities. From this set, we isolate a subset of pairs for which humans show a high level of agreement (87.5% on average). We show, however, that baseline and state-of-the-art techniques achieve only 61.3% precision in predicting human preferences for this subset. We then develop a technique based on a combination of general frequency, applicability to similar entities and semantic similarity that achieves 74% precision. The preference dataset is available at https://www.kaggle.com/srazniewski/wikidatapropertyranking.
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Notes
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For instance, as of March 21st, 2016, there were 2202 properties, while as of February 7, 2017, there are 2719 according to https://tools.wmflabs.org/hay/propbrowse/.
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Not to be mixed with ensemble learning, a machine learning approach where consecutive instances of the same classifier are trained especially on records that previous instances predicted wrongly. Ensemble learning requires a sufficient amount of labeled training data, which is not available in our case.
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Razniewski, S., Balaraman, V., Nutt, W. (2017). Doctoral Advisor or Medical Condition: Towards Entity-Specific Rankings of Knowledge Base Properties. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_37
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