Download presentation
Presentation is loading. Please wait.
1
Embedding based entity summarization
ISWC’19
2
Overview Motivation: Leverage the Knowledge graph embedding for entity summarization Contribution: We adopt (weakly) supervised learning to generate high-quality general-purpose training data for entity summarization without the relying on manually labeled data. We devise a context-based knowledge graph embedding fusion both semantic and structural information for entity summarization. The new embedding consider the difference in semantic meaning between facts in different contexts. And the approach results shows it achieve the state-of-the-art of entity summarization. We utilize the embedding with a learning-based ranking algorithm and diversity-oriented re-ranking to generate the final summarization of entity and achieve the state-of-the-art.
3
Methodology Entity’s fact sets KG Embedding Language Model Feature
Ranking DBpedia（KG Data） Labeled Data Automatically labeling Entity Summarizations
4
Methodology KG Embedding Language Model Example: Matt Ginn BirthDate
LSTM LSTM LSTM LSTM LSTM LSTM LSTM LSTM LSTM LSTM LSTM LSTM Example: Matt Ginn BirthDate Knowledge Graph Random Walk Path Set Path Semantic Embedding
5
Methodology
6
Methodology
7
Methodology
8
Evaluation DataSet DBpedia LinkedMDB
For Generalization performance Experiment on automatically labeled data Experiment on KGE leveraged in entity summarization Experiment on entity summarization Matric：F-measure
9
Evaluation Experiment on automatically labeled data Precision Recall
Precision Recall f-measure Ours [Xu et al., 2014]
10
Evaluation Experiment on KGE leveraged in entity summarization
trans-E KGlove fastText Jointly Ours top5 0.251 0.252 0.336 0.255 0.34 top10 0.460 0.456 0.520 0.479 0.517 trans-E KGlove fastText Jointly Ours top5 0.295 0.299 0.309 0.270 0.31 top10 0.522 0.531 0.527 0.537 DBpedia with Embedding Based Clustering DBpedia with Diversity-oriented re-ranking trans-E KGlove fastText Jointly Ours top5 0.21 0.211 0.251 0.264 0.283 top10 0.343 0.388 0.374 0.398 0.412 trans-E KGlove fastText Jointly Ours top5 0.165 0.259 0.22 0.260 0.284 top10 0.340 0.383 0.364 0.387 0.413 IMDB with Embedding Based Clustering IMDB with Diversity-oriented re-ranking
11
Evaluation Experiment on entity summarization
Faces-E CD Ours top5 0.285 0.299 0.31 top10 0.527 0.531 0.537 DBpedia with Diversity-oriented re-ranking Faces-E CD Ours top5 0.252 0.215 0.284 top10 0.348 0.326 0.413 IMDB with Diversity-oriented re-ranking Faces-E CD Ours top5 0.276 0.267 0.302 top10 0.476 0.467 0.501 ALL
Similar presentations
