77 research outputs found
Knowledge Graph Embedding: An Overview
Many mathematical models have been leveraged to design embeddings for
representing Knowledge Graph (KG) entities and relations for link prediction
and many downstream tasks. These mathematically-inspired models are not only
highly scalable for inference in large KGs, but also have many explainable
advantages in modeling different relation patterns that can be validated
through both formal proofs and empirical results. In this paper, we make a
comprehensive overview of the current state of research in KG completion. In
particular, we focus on two main branches of KG embedding (KGE) design: 1)
distance-based methods and 2) semantic matching-based methods. We discover the
connections between recently proposed models and present an underlying trend
that might help researchers invent novel and more effective models. Next, we
delve into CompoundE and CompoundE3D, which draw inspiration from 2D and 3D
affine operations, respectively. They encompass a broad spectrum of techniques
including distance-based and semantic-based methods. We will also discuss an
emerging approach for KG completion which leverages pre-trained language models
(PLMs) and textual descriptions of entities and relations and offer insights
into the integration of KGE embedding methods with PLMs for KG completion
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
Let Knowledge Make Recommendations For You
The knowledge graph can make more accurate personalized recommendations for the recommendation system, but it is also interpretative and has traces to follow. The purpose of the recommendation system is to recommend a series of unobserved items for users. At present, recommendation systems based on knowledge graphs are mainly implemented in two ways: Embedding-based and path based. Embedding methods usually directly use information from the knowledge graph to enrich the representation of an item or user. Still, it failed to introduce multi-hop relations, and it is challenging to use semantic network information. A path-based recommendation algorithm utilizes the knowledge graph to gain multi-hop knowledge and compare the similarity between users or items to improve the recommendation effect. This paper (1) Aiming at the problem of how the recommendation algorithm effectively utilizes the semantically related information of knowledge, a self-attention-based knowledge representation learning model is designed to learn the semantic information of the entity-relationship by using the overall triplet of the entity-relationship to achieve high-quality knowledge features, Which brings more and more helpful information to the recommendation. (2) Constructing a content recommendation model with unified, embedded behavior and knowledge features, using historical user preferences combined with knowledge graphs to dynamically learn knowledge features to bring users more accurate and diverse recommendations. (3) Aiming at the problem of knowledge feature representation learning, a self-attention based knowledge representation learning model is proposed. Focusing on the difference in the importance of triples for determining entity semantics, the self-attention mechanism is used to learn semantics from triples to improve knowledge features. The quality of the representation provides high-quality auxiliary information for the recommendation system. The model’s performance is demonstrated through link prediction and triple classification experiments to prove the feasibility of the method proposed in this article
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
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