Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion

Qiao, Ziyue; Ning, Zhiyuan; Du, Yi; Zhou, Yuanchun

doi:10.48550/arxiv.2012.07011

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem is that current methods for Knowledge Graph Embedding rely on the graph's topology, treating attribute triples as relation triples, and essential information about entities and relations has not been fully employed [32] [33] [34] failing to utilize the graph's ontology to limit the spurious growth of edges leading to false, misleading, and fabricated knowledge.…”

Section: A Problem Statementmentioning

confidence: 99%

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Tatchukova,

2024

IEEE Access

View full text Add to dashboard Cite

Knowledge Graphs have demonstrated a real advantage in knowledge representation, leveraging graphs NoSQL structures and schema-less technology, which offers superior comprehension, knowledge representation, interpretation, and reasoning. The problem is that current methods for Knowledge Graph Embedding rely on the graph's topology, and essential information about entities and relations has not been fully employed, failing to utilize the graph's ontology to limit the spurious growth of edges, leading to inaccurate, misleading, and fabricated knowledge. This research aims to establish a method to restrict the spurious growth of host KGs by imposing an upper bound on edge embedding using the claim's and the host's ontology graph. Through this research, a Claim-Ontology Signature artifact is designed to facilitate openenvironment KG completion. This artifact establishes the upper bound for the type of edges predicted by the link prediction algorithm, thus preventing the spurious growth of edges within the Knowledge Graph. Further, the artifact is evaluated in the context of three use cases: Host-Guided embedding, Claim-Guided embedding, and Topic-Guided embedding, using a quantitative framework for design science evaluation. The main finding is that the spurious growth of edges can be limited by imposing an upper bound on the possible edge embedding using the claim's graph and the host ontology graph. A secondary finding is that the artifact could serve as an instrument to manage the ontology-topology tradeoff in Knowledge Graphs.

show abstract

Section: A Problem Statementmentioning

confidence: 99%

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Tatchukova,

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Relation-Aware Concept Representation With the aforementioned equations, we obtain three concept representations of u i,m , namely c m,1 , c m,2 , c m,3 . Motivated by the contextualized entity learning of Qiao et al (2020), we calculate the RACR of u i,m by:…”

Section: Integrating Knowledge Bases In Ercmentioning

confidence: 99%

SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition

Zhu

Mao

et al. 2023

AAAI

View full text Add to dashboard Cite

Emotion recognition in conversation (ERC) has received increasing attention from the research community. However, the ERC task is challenging, largely due to the complex and unstructured properties of multi-party conversations. Besides, the majority of daily dialogues take place in a specific context or circumstance, which requires rich external knowledge to understand the background of a certain dialogue. In this paper, we address these challenges by explicitly modeling the discourse relations between utterances and incorporating symbolic knowledge into multi-party conversations. We first introduce a dialogue parsing algorithm into ERC and further improve the algorithm through a transfer learning method. Moreover, we leverage different symbolic knowledge graph relations to learn knowledge-enhanced features for the ERC task. Extensive experiments on three benchmarks demonstrate that both dialogue structure graphs and symbolic knowledge are beneficial to the model performance on the task. Additionally, experimental results indicate that the proposed model surpasses baseline models on several indices.

show abstract

Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion

Cited by 2 publications

References 3 publications

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition

Contact Info

Product

Resources

About