2026-10-12T00:14:10Z https://www.scidb.cn/oai

10.11922/sciencedb.j00104.00047 2020-10-16T16:42:48Z

2020-10-16 Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding doi:10.11922/sciencedb.j00104.00047 en Four tables and 23 figures of this paper. Table 1 shows the concept space comparison of existing taxonomies. Table 2 presents Hearst pattern examples. Table 3 shows labeling guideline for conceptualization. Table 4 presents precision of short text understanding. Figure 1 shows the framework overviews. Figure 2 is local taxonomy construction. Figure 3 shows horizontal merging. Figure 4 shows vertical merging: single sense alignment. Figure 5 shows vertical merging: multiple sense alignment. Figure 6 is a subgraph of heterogeneous semantic network around watch. Figure 7 is the compression procedure of typed-term co-occurrence network. Figure 8 presents an example of short text understanding. Figure 9 present examples of Chain model and Pairwise model. Figure 10 is a snapshot of the Probase browser. Figure 11 is a snapshot of single instance conceptualization.Figure 12 is a snapshot of context-aware single instance conceptualization. Figure 13 shows an example of short text conceptualization. Figure 14 is the framework of topic search. Figure 15 is a snapshot of the Web tables. Figure 16 shows query recommendation snapshot. Figure 17 shows the correlation of CTR with ads relevance score. Figure 18 presents the distribution of concepts in Microsoft Concept Graph. Figure 19 shows concept coverage of different taxonomies. Figure 20 shows precision of extracted isA pairs on 40 concepts.Figure 21 is precision of isA pairs after each iteration. Figure 22 shows the number of discovered concepts and isA pairs after each iteration. Figure 23 shows precision and nDCG comparison. Knowledge extraction; Conceptualization; Text understanding Lei Ji Yujing Wang Botian Shi Dawei Zhang Zhongyuan Wang Jun Yan PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1162/dint_a_00013 Science Data Bank