Abstract
Temporal features, such as date and time or time of an event, employ concise semantics for any kind of information retrieval, and therefore for linked data information retrieval. However, we have found that most linked data information retrieval techniques pay little attention on the power of temporal feature inclusion. We propose a keyword-based linked data information retrieval framework, called TLDRet, that can incorporate temporal features and give more concise results. Preliminary evaluation of our system shows promising performance.
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the QA system [9] uses exact matching between input keywords and linked data resources currently.
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In DBPedia QALD-2 set, there are 9 temporal feature related questions, 4 are from training question set and 5 are from test question set.
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In average calculation, for each individual question we put F1 measure = 0 (zero), if recall = 0 (zero).
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Rahoman, MM., Ichise, R. (2014). TLDRet: A Temporal Semantic Facilitated Linked Data Retrieval Framework. In: Kim, W., Ding, Y., Kim, HG. (eds) Semantic Technology. JIST 2013. Lecture Notes in Computer Science(), vol 8388. Springer, Cham. https://doi.org/10.1007/978-3-319-06826-8_18
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DOI: https://doi.org/10.1007/978-3-319-06826-8_18
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