Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization
Proceedings of the IEEE International Conference on Semantic Computing
Word embedding is becoming more popular in the Semantic Web community as an effective approach for capturing semantics in various contexts. In this paper, we combine word embedding and topic modeling to model RDF data for the entity summarization task. In our model, ES-LDAext, which is the extended version of our previous model, we utilize the word embedding to supplement the RDF data before applying entity summarization. In addition, in the model presented here, we use RDF literals as a very good source of information to create more reliable and representative summaries for entities. To do that, we use the Named Entity Recognition approach to extract entities within literals before feeding them into the word embedding model to enrich the RDF data. Experimental results demonstrate the effectiveness of the proposed model.
Pouriyeh, Seyedamin, Mehdi Allahyari, Krys Kochut, Gong Cheng, Hamid Reza Arabnia.
"Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization."
Proceedings of the IEEE International Conference on Semantic Computing: 252-255 Laguna Hills, CA: IEEE.
doi: 10.1109/ICSC.2018.00044 source: https://ieeexplore.ieee.org/document/8334467 isbn: 978-1-5386-4408-9