Open-World Knowledge Graph Completion (1711.03438v1)

Abstract: 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.

Open-World Knowledge Graph Completion: Advancements and Implications

The paper "Open-World Knowledge Graph Completion" by Baoxu Shi and Tim Weninger presents a novel approach to the task of Knowledge Graph Completion (KGC) under the open-world assumption, introducing the ConMask model. This work is a significant departure from traditional models that operate under the closed-world assumption, where the set of entities and relationships is static, and new entities cannot easily be integrated.

Introduction and Motivation

Knowledge Graphs (KGs) provide a structured representation of knowledge, often formatted as triples composed of a head entity, a relation, and a tail entity. Despite the utility of KGs in various domains such as web search and natural language processing, they remain incomplete. Traditional KGC methods aim to predict missing relationships within a fixed set of entities—an approach limited by the closed-world assumption.

As real-world KGs are dynamic and constantly evolving with new entities, there is a growing need for open-world KGC, which accommodates the introduction of new, unseen entities. The authors propose the ConMask model as a solution to this challenge by leveraging text-based entity descriptions, thus bypassing connectivity limitations inherent in closed-world models.

ConMask Model Methodology

ConMask represents a pivotal development in KGC by incorporating text-based entity embeddings to facilitate open-world completion tasks. Key components of the ConMask model include:

1. Relationship-Dependent Content Masking: This mechanism identifies relevant textual snippets within entity descriptions that pertain to a given relationship, effectively reducing noise and focusing on pertinent information.
2. Target Fusion: Utilizing Fully Convolutional Networks (FCN), ConMask extracts and fuses relevant textual embeddings into relationship-dependent entity representations. This contrasts with traditional topology-based methods, which cannot dynamically extend to new entities.
3. Entity Resolution and Ranking: The model employs a list-wise ranking loss function to optimize entity prediction performance, allowing for rank-based evaluation of potential tail or head entities within the KG.

Evaluation and Results

ConMask's performance was evaluated on both new (DBPedia-based) and existing datasets, demonstrating its efficacy in open-world settings. The experiments indicate that ConMask not only excels in handling entities absent during training but also competes favorably with closed-world models on traditional tasks. The model achieved a marked reduction in mean rank and improvement in HITS@10 and MRR metrics across datasets, underscoring its robustness in open-world entity prediction scenarios.

Implications and Future Directions

The introduction of ConMask addresses critical limitations in existing KGC techniques, offering a scalable and adaptive solution to the problem of integrating dynamic updates into KGs. This shift towards open-world assumption not only enriches the capabilities of KGC models but also aligns them more closely with the real-time growth of knowledge resources like DBPedia.

Theoretically, ConMask's methodology opens avenues for further research in hybrid models that seamlessly integrate structural and textual information. Practically, it promises enhanced applications in areas requiring up-to-date and comprehensive knowledge representations, such as AI-driven decision-making systems and automated factual verification.

Future work can explore refining contextual masking techniques to enhance precision and developing hybrid models that further balance the trade-offs between observed text and latent graph structures. Additionally, scaling the methodology to accommodate various knowledge domains and heterogeneously sourced data could potentiate broader applicative impacts.

In conclusion, the open-world KGC task and the ConMask model present a significant advancement in the field, expanding the horizon of capabilities for adaptive and comprehensive knowledge representation. This work establishes a foundation for continued exploration into dynamically evolving KGs and their integration into future intelligent systems.