Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction (1307.7973v1)

Abstract: This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over existing methods that rely on text features alone.

An Overview of "Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction"

The paper "Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction," authored by Jason Weston, Antoine Bordes, Oksana Yakhnenko, and Nicolas Usunier, presents a novel approach to the task of relation extraction (RE) that employs embedding models to integrate information from both text and structured knowledge bases (KB). Traditional relation extraction techniques have primarily relied on textual features alone, but this paper aims to enhance those methods by leveraging the vast, structured information available in knowledge bases such as Freebase.

Core Contributions

The proposed model introduces a dual embedding strategy that learns low-dimensional vector representations for both words and entities within a shared vector space. This approach is designed to address the challenge of weakly supervised relation extraction, where explicit labels are sparse and costly to obtain. The key contributions can be summarized as follows:

1. Joint Embedding Framework: The model concurrently uses weakly labeled text data and triples from the KB. By training on both these data sources, it can generalize and infer the plausibility of missing triples that are not directly observed within the KB.
2. Ranking-Based Learning: The framework relies on a ranking-based objective function, which encourages correct predictions of entity relations based on mention embeddings. This is facilitated through a scoring mechanism applied to both the textual and KB embeddings.
3. Empirical Evaluation: The model was tested on a dataset aligning New York Times articles with Freebase relations, demonstrating its ability to outperform systems using text features alone. Notably, the model handles a subset of Freebase data containing 4 million entities and 23,000 relationships.

Experimental Insights

The empirical validation highlights the effectiveness of combining KB information with textual mention data. The results, as visualized through precision-recall curves, show a notable performance edge for the proposed model, especially when both text and KB data are integrated. The dataset used for evaluation had to overcome alignment challenges due to the evolving nature of Freebase relationships, yet the model's robustness is clear in its superior recall within the range of [0, 0.1], outperforming comparator methods including those employing multi-instance multi-label learning strategies.

Theoretical and Practical Implications

The implications of this research are multifaceted. Theoretically, it opens the avenue for further exploration into the integration of structured and unstructured data, reinforcing the paradigm of embedding-based relational learning. Practically, the successful incorporation of KBs into relation extraction processes can enhance numerous applications, including semantic search, information retrieval, and knowledge graph completion.

Future Prospects

Future developments in this area can aim at refining the model's scalability and adaptability to other large-scale KBs beyond Freebase. Furthermore, the methodology could be extended to address more complex tasks such as entity linking and end-to-end knowledge graph construction. Additionally, exploring parameter sharing methods across different embedding spaces could further enrich the model's predictive capabilities.

In summary, this paper makes a compelling case for the integration of language and KBs through embedding models, setting a foundational precedent for advancements in the field of information extraction. As AI continues to evolve, methodologies such as this could become pivotal in efficiently harnessing the rich tapestry of knowledge scattered across disparate data formats.