| Challenge: | a paper addresses the problem of mapping natural language text to knowledge base entities. |
| Approach: | They propose a model for mapping natural language text to knowledge base entities using a multi-dimensional entity space obtained from a knowledge graph. |
| Outcome: | The proposed model is applied to large-scale text-to-entity mapping and entity classification tasks with state-of-the-art results. |
Similar Papers
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
| Approach: | They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information. |
| Outcome: | The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information. |
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models (N19-1)
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| Challenge: | a novel method for mapping unrestricted text to knowledge graph entities is proposed . a proof-of-concept experiment has encouraging results comparable to those of state-of the-art systems. |
| Approach: | They propose a method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. |
| Outcome: | The proposed method produces highly interpretable predictions comparable to state-of-the-art systems. |
Towards Entity Spaces (2020.lrec-1)
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| Challenge: | Entities are a central element of knowledge bases and are used in many knowledge-centric tasks including text analysis. |
| Approach: | They propose to use entity spaces to represent a set of associated entities with near-identity to provide a handle to an amorphous grouping of entities. |
| Outcome: | The proposed representations improve recall of entity linking in English by using disambiguation pages. |
Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction (L18-1)
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| Challenge: | Currently, the construction and updating of knowledge bases rely on human labor. |
| Approach: | They propose to map relational phrases in triples from natural language to knowledge base predicate format. |
| Outcome: | The proposed mapping results show high quality and promising coverage on relational phrases compared to previous research. |
Representation Learning of Entities and Documents from Knowledge Base Descriptions (C18-1)
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| Challenge: | Using documents extracted from Wikipedia, we train a neural network model that learns distributed representations of entities and documents directly from a knowledge base. |
| Approach: | They propose a neural network model that learns distributed representations of entities from a knowledge base. |
| Outcome: | The proposed model performs state-of-the-art on fine-grained entity typing and multiclass text classification tasks. |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment (D19-1)
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| Challenge: | Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use dot product-based functions to define dot products over embeddings to better capture semantics of 1-N, N-1 and N-N relations. |
| Outcome: | The proposed framework outperforms existing methods on multilingual datasets. |
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)
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| Challenge: | Existing work on data-to-text generation focused on domain-specific benchmark datasets. |
| Approach: | They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora . |
| Outcome: | The proposed method improves on open domain QA and the LAMA knowledge probe. |
Instilling Type Knowledge in Language Models via Multi-Task QA (2022.findings-naacl)
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| Challenge: | Current methods to learn entity types rely on coarse, noisy labels . current methods rely only on text-to-text pre-training on type-centric questions . |
| Approach: | They propose to instill fine-grained type knowledge in language models by pre-training on type-centric questions. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot dialog state tracking benchmarks and can accurately infer entity types in Wikipedia articles. |
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories (P19-1)
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| Challenge: | Recent work has focused on vector representations which capture different meanings, i.e., senses, of words. |
| Approach: | They propose a bidirectional LSTM model which learns word senses from semantically annotated corpora by focusing on word order. |
| Outcome: | The proposed model achieves state-of-the-art on the SemEval-2014 word-to-sense similarity task and is available online at http://lcl.uniroma1.it/LSTMEmbed. |