Rule Learning from Knowledge Graphs Guided by Embedding Models
| Autor: | Mohamed H. Gad-Elrab, Evgeny Kharlamov, Gerhard Weikum, Daria Stepanova, Vinh Thinh Ho |
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| Rok vydání: | 2018 |
| Předmět: |
Text corpus
business.industry Computer science media_common.quotation_subject Probabilistic logic 02 engineering and technology Machine learning computer.software_genre Small set Ranking (information retrieval) 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Quality (business) Artificial intelligence Pruning (decision trees) business computer media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030006709 ISWC (1) |
| DOI: | 10.1007/978-3-030-00671-6_5 |
| Popis: | Rules over a Knowledge Graph (KG) capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules could be generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and external information sources including text corpora. Experiments on real-world KGs demonstrate the effectiveness of our novel approach both with respect to the quality of the learned rules and fact predictions that they produce. |
| Databáze: | OpenAIRE |
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