Abstract
Automatically constructed knowledge bases often suffer from quality issues such as the lack of attributes for existing entities. Manually finding and filling missing attributes is time consuming and expensive since the volume of knowledge base is growing in an unforeseen speed. We, therefore, propose an automatic approach to suggest missing attributes for entities via hierarchical clustering based on the intuition that similar entities may share a similar group of attributes. We evaluate our method on a randomly sampled set of 20,000 entities from DBPedia. The experimental results show that our method can achieve a high precision and outperform existing methods.
This work was supported by the National High Technology R&D Program of China (Grant No. 2012AA011101, 2014AA015102), National Natural Science Foundation of China (Grant No. 61272344, 61202233, 61370055) and the joint project with IBM Research. Corresponding author: Yansong Feng.
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Luo, B., Lu, H., Diao, Y., Feng, Y., Zhao, D. (2014). Detect Missing Attributes for Entities in Knowledge Bases via Hierarchical Clustering. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_35
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DOI: https://doi.org/10.1007/978-3-662-45924-9_35
Publisher Name: Springer, Berlin, Heidelberg
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