Knowledge Graph Completion: A Review

Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines.

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“…The facts in

are represented as triples of the form ⟨ h , r , t ⟩ where

and

. LP aims to enrich

with new facts by predicting the missing links between existing nodes ( Chen et al, 2020 )—i.e. predicting head ⟨?, r , t ⟩ or tail ⟨ h , r , ?⟩.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-infused Learning for Entity Prediction in Driving Scenes

2021

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“…Structure Embedding (SE) model (13) considers the correspondence of head entity HD and tail entity TL. During the existence of triplet values (HD,TL,R), overlap in a definite relation space RS n occur.…”

Section: Structured Embeddingmentioning

confidence: 99%

Knowledge graph embedding via the properties of mapping and dynamic matrix

Devi¹,

Kalaivani²

2021

IJST

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Objective:The main objective of this research is constructing a knowledge graph using a superior model that leverage the knowledge through relational mapping properties and the matrix constructed dynamically based on the changes in the relational mapping. Methods: This research work proposes symbolic objects in the knowledge graph that are signified by two vectors, the first vector denotes the entity/relation and the vector denote the matrix which is constructed with the help of mapping properties. The proposed methodology uses only minimum parameters and avoids multiplication processes which make the proposed scheme effective in the large scale graphs. The matrix representation with the assistance of mapping properties reduces the computational complication and the time requires to attain the knowledge is also minimized. Findings: The empirical research on knowledge graph mining gives significant insights and it is applied on benchmark datasets namely FB15K-237 and WN18RR. The performance are evaluated in terms of precision and recall in percentage.

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“…The concept of knowledge graph was put forward in the next generation intelligent search engine project released by Google in 2012 [1]. Its core technology is to extract the entity and its attribute feature information and the relationship information between entities from web pages, so as to form a knowledge network and create a new intelligent search mode with the support of knowledge network.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Matching Query Method of Natural Language Knowledge Graph Based on Hierarchical Graph Topological Sequence

Zou

2022

IEEE Access

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In recent years, although the application of knowledge graph in natural language processing has made some progress, there are still some key problems to be solved, especially the matching query problem in natural language knowledge graph. Since the basic data model of knowledge graph is graph, the matching query problem in natural language knowledge graph is usually transformed into graph matching query problem. However, at present, the traditional graph matching technology applied in knowledge graph consumes too much time and has low query efficiency, which cannot meet the needs of users for large-scale natural language knowledge graph query. Based on the full analysis of the defects of the traditional graph matching technology applied in the knowledge graph, according to the characteristics of the natural language knowledge graph, in order to improve the query efficiency, we propose an accurate matching query method of graph hierarchical topological sequence based on the graph model of knowledge graph. Through experiment analysis, compared with the traditional graph model matching algorithm applied in knowledge graph query, this method can quickly filter the unqualified knowledge graph candidate sets, effectively reduce the number of knowledge graph candidate sets, and make it have more advantages in matching efficiency and time performance. In addition, compared with two algorithms of GIndex and FG-Index, this method has better performance in index construction time, average size of candidate set and average running time of online update.

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Knowledge Graph Completion: A Review

Cited by 185 publications

References 72 publications

Knowledge-infused Learning for Entity Prediction in Driving Scenes

Knowledge-infused Learning for Entity Prediction in Driving Scenes

Knowledge graph embedding via the properties of mapping and dynamic matrix

An Accurate Matching Query Method of Natural Language Knowledge Graph Based on Hierarchical Graph Topological Sequence

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