Titlebook: Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data; First China Conferen Huajun Chen,Heng Ji,Tong Ruan Confer

Jefferson

書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data影響因子(影響力)




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data影響因子(影響力)學(xué)科排名




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data網(wǎng)絡(luò)公開度




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data被引頻次




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data被引頻次學(xué)科排名




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data年度引用




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data年度引用學(xué)科排名




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data讀者反饋




書目名稱Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data讀者反饋學(xué)科排名

investigate

Biomedical Event Trigger Detection Based on Hybrid Methods Integrating Word Embeddingsngs are learnt from large scale unlabeled texts and integrated as unsupervised features into other rich features based on dependency parse graphs, and thus a lot of semantic information can be represented. Experimental results show our method outperforms the state-of-the-art systems.

output

LD2LD: Integrating, Enriching and Republishing Library Data as Linked Datanect researcher data with publication data such as papers, patents and monograph using entity linking methods. After that, we use simple reasoning to predict missing values and enrich the library data with external data. Finally, we republish the integrated and enriched library data as Linked Data.

諂媚于人

羞辱

Boosting to Build a Large-Scale Cross-Lingual Ontologye the performance of ontology building and instance matching. Experiments output an ontology containing over 3,520,000 English instances, 800,000 Chinese instances, and over 150,000 cross-lingual instance alignments. The F1-measure improvement of Chinese . prediction achieve the highest 32%.

可互換

GRU-RNN Based Question Answering Over Knowledge Baseirs are used to train our multi-step system. We evaluate our system on . and .. The experimental results show that our system achieves comparable performance compared with baseline method with a more straightforward structure.

indemnify

BLANK

multiply

漂亮