Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an open-world KGC model called ConMask. This model learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationship-dependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task.
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Learning to Rank Entities for Set Expansion from Unstructured Data
Entity set expansion (ESE) refers to mining ``siblings'' of some user-provided seed entities from unstructured data. It has drawn increasing attention in the IR and NLP communities for its various applications. To the best of our knowledge, there has not been any work towards a supervised neural model for entity set expansion from unstructured data. We suspect that the main reason is the lack of massive annotated entity sets. In order to solve this problem, we propose and implement a toolkit called {DBpedia-Sets}, which automatically extracts entity sets from any plain text collection and can provide a large number of distant supervision data for neural model training. We propose a two-channel neural re-ranking model {NESE} that jointly learns exact and semantic matching of entity contexts. The former accepts entity-context co-occurrence information and the latter learns a non-linear transformer from generally pre-trained embeddings to ESE-task specific embeddings for entities. Experiments on real datasets of different scales from different domains show that {NESE} outperforms state-of-the-art approaches in terms of precision and MAP, where the improvements are statistically significant and are higher when the given corpus is larger.
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- Award ID(s):
- 1813662
- NSF-PAR ID:
- 10228381
- Date Published:
- Journal Name:
- Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2020)
- Page Range / eLocation ID:
- 21 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN.
Supplementary information Supplementary data are available at Bioinformatics online.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3409256.3409811
