RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network (2009.08694v2)

Abstract: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to learn representations of both the sentence as well as facts stored in a KG, improving the overall extraction quality. These facts, including entity attributes (label, alias, description, instance-of) and factual triples, have not been collectively used in the state of the art methods. We evaluate the effect of various forms of representing the KG context on the performance of RECON. The empirical evaluation on two standard relation extraction datasets shows that RECON significantly outperforms all state of the art methods on NYT Freebase and Wikidata datasets. RECON reports 87.23 F1 score (Vs 82.29 baseline) on Wikidata dataset whereas on NYT Freebase, reported values are 87.5(P@10) and 74.1(P@30) compared to the previous baseline scores of 81.3(P@10) and 63.1(P@30).

• The paper introduces RECON, a novel method for relation extraction that integrates knowledge graph context into a graph neural network framework.
• RECON enhances relation inference by leveraging entity attribute context via RNNs and multi-hop triple context via GATs from knowledge graphs.
• Evaluations demonstrate that RECON consistently outperforms state-of-the-art models on datasets like NYT Freebase and Wikidata, showing improved precision and recall.

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

The paper presents RECON, a novel approach for relation extraction (RE) that leverages knowledge graph (KG) context within a graph neural network (GNN) framework. This method enhances sentential relation extraction by integrating entity and triple contexts from a knowledge graph, addressing the limitations of existing RE techniques which primarily rely on sentence-based features or multi-instance learning paradigms.

Core Contributions of RECON

RECON makes notable advancements in relation extraction through a structured approach, which comprises:

1. Entity Attribute Context (EAC): RECON employs a recurrent neural network to encode entity attributes such as labels, aliases, descriptions, and types obtained from KGs. This is a significant step forward as it combines multiple entity properties into a coherent contextual representation, enhancing the model's ability to infer relations.
2. Triple Context Learner: Using a graph attention network (GAT), the model captures features from multi-hop neighborhoods of entities in KGs, learning separate embeddings for entities and relations. This separation allows for more expressive embeddings compared to existing methods that integrate these aspects into a single vector space.
3. Context Aggregator: RECON utilizes a GNN to amalgamate sentence embeddings with entity and triple contexts, enabling sophisticated message passing and relation prediction. This integration is designed to uncover latent relationships not explicitly stated within the text.

Evaluation and Results

The paper evaluates RECON against existing state-of-the-art RE models using NYT Freebase and Wikidata datasets. RECON consistently outperforms these baselines, achieving higher precision and recall rates. Notably, the model demonstrates its superiority in terms of precision over the entire recall range in both datasets, highlighting its ability to utilize KG context effectively even when the sentence-level data is sparse or ambiguous.

Implications and Future Directions

The insights from RECON open several avenues for future research and development:

• Theoretical and Practical Implications: The separation of entity and relation embeddings into distinct vector spaces provides a more nuanced understanding of how these elements interact and could inform the design of more complex RE models. Additionally, the multi-hop neighborhood analysis conducted using GATs underscores the importance of leveraging interconnected data in KGs to improve relation extraction tasks.
• Industry Applications: RECON offers substantial potential for enhancing knowledge graph completion tasks and applications reliant on precise relation extraction, such as question answering systems, voice assistants, and search engines.
• Enhancements and Optimization: Future work could explore the intelligent selection of KG context based on sentence relevancy, leveraging dynamic learning algorithms to optimize training efficiency and output quality for large-scale datasets.

In summary, RECON represents a significant step forward in utilizing KG context for relation extraction, offering a robust framework that could be adapted and extended for various AI tasks requiring nuanced semantic understanding and inference.