Abstract
There exists a precise time period during which a given fact such as an event or a status is valid. In this paper, we propose a new approach to determine the validity time of a fact statement by leveraging unstructured and noisy data from the Web, while overcoming the limitations of existing natural language processing technologies designed for the same task. Given a fact and its temporal relevance text, the proposed solution first constructs a Semantic Bayesian Network, then estimates the validity probabilities of time points using the constructed network. In the interest of dealing with the semantic complexity of keywords, we also present a technique based on relative standard deviation to estimate distortion risks of keywords and incorporate their risk estimation into the process of probability computation. Our experiments on real data shows that the proposed approach can achieve considerable improvements in performance over 2 state-of-the-art alternatives, and the proposed risk reduction technique can effectively improve validity time reasoning’s precision.
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All the data files of our retrieved relevant snippets, extracted keywords and candidate years of the query objects of different topics are available at http://www.wowbigdata.com.cn/ValidTimeReasoning.zip.
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Hou, B., Nafa, Y. (2018). Enabling Temporal Reasoning for Fact Statements: A Web-Based Approach. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_9
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DOI: https://doi.org/10.1007/978-3-319-91455-8_9
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