Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding

Wang, Guanying; Zhang, Wen; Wang, Ruoxu; Zhou, Yalin; Chen, Xi; Zhang, Wei; Zhu, Hai; Chen, Huajun

doi:10.18653/v1/d18-1248

Cited by 73 publications

(40 citation statements)

References 20 publications

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“…Actually in Freebase, the triple format is like (e1, r, e2) where e1 and e2 are the two entities and r defines the relation. So relation can be found in a known KG and can generate large amount of data [4]. Since we mentioned before that we created our own Bangla Freebase which contains a large number of relation with the help of Wikidata query service and SPARQL query language.…”

Section: Creating Bangla Freebasementioning

confidence: 99%

Finding out Noisy Patterns for Relation Extraction of Bangla Sentences

Habib¹,

Anwar²

2020

IJNLC

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Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.

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Section: Creating Bangla Freebasementioning

confidence: 99%

Finding out Noisy Patterns for Relation Extraction of Bangla Sentences

Habib¹,

Anwar²

2020

IJNLC

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“…independently proposed to use attention to address the same limitation, and Du et al (2018) improved by using multilevel self-attention. To account for the noise in distant supervision labels, ; ; Wang et al (2018) suggested different ways of using "soft labels" that do not necessarily agree with the distant supervision labels. Ye et al (2017) proposed a method for leveraging dependencies between different relations in a pairwise ranking framework, while arranged the relation types in a hierarchy aiming for better generalization for relations that do not have enough training data.…”

Section: Related Workmentioning

confidence: 99%

Combining Distant and Direct Supervision for Neural Relation Extraction

Beltagy¹,

Lo²,

Ammar³

2019

Proceedings of the 2019 Conference of the North

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In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model's ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method 1 achieves a new state-of-theart result on the widely used FB-NYT dataset.

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“…Many Knowledge Graphs (KGs), such as Freebase [2] and YAGO [33], have been built in recent years and led to a broad range of applications, including question answering [4], relation extraction [36], and recommender system [49]. KGs store facts as triples in the form of (subject entity, relation, object entity), abridged as (s, r, o).…”

Section: Introductionmentioning

confidence: 99%

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Zhang

Paudel

Wang

et al. 2019

The World Wide Web Conference

Self Cite

153

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Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show * Work done while at UZH.

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Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding

Cited by 73 publications

References 20 publications

Finding out Noisy Patterns for Relation Extraction of Bangla Sentences

Finding out Noisy Patterns for Relation Extraction of Bangla Sentences

Combining Distant and Direct Supervision for Neural Relation Extraction

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

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