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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Aligning Knowledge Base and Document Embedding Models using Regularized Multi-Task Learning |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-3-030-00670-9 |
| Page Range | 21 - 37 |
| Event Title | The Semantic Web – ISWC 2018 |
| Event Type | conference |
| Event Location | Monterey, CA, USA |
| Event Start Date | October 8 - 2018 |
| Event End Date | October 12 - 2018 |
| Number | 11136 |
| Place of Publication | Cham |
| Publisher | Springer |
| Abstract Text | Knowledge Bases (KBs) and textual documents contain rich and complementary information about real-world objects, as well as relations among them. While text documents describe entities in freeform, KBs organizes such information in a structured way. This makes these two information representation forms hard to compare and integrate, limiting the possibility to use them jointly to improve predictive and analytical tasks. In this article, we study this problem, and we propose KADE, a solution based on a regularized multi-task learning of KB and document embeddings. KADE can potentially incorporate any KB and document embedding learning method. Our experiments on multiple datasets and methods show that KADE effectively aligns document and entities embeddings, while maintaining the characteristics of the embedding models. |
| Related URLs | |
| Digital Object Identifier | 10.1007/978-3-030-00671-6_2 |
| Other Identification Number | merlin-id:16389 |
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| Additional Information | ISBN: 978-3-030-00671-6 (E) |