An Embedding-Based Approach to Rule Learning in Knowledge Graphs

Omran, Pouya Ghiasnezhad; Wang, Kewen; Wang, Zhe

doi:10.1109/tkde.2019.2941685

Cited by 37 publications

(21 citation statements)

References 21 publications

(32 reference statements)

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“…Logical rules can been extracted by rule mining tools like AMIE [77]. The recent RLvLR [78] proposes a scalable rule mining approach with efficient rule searching and pruning, and uses the extracted rules for link prediction.…”

Section: Rule-based Reasoningmentioning

confidence: 99%

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A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Ji,

Pan,

Cambria

et al. 2020

Preprint

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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 on 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.

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Section: Rule-based Reasoningmentioning

confidence: 99%

“…Chekol et al [145] explored Markov logic network and probabilistic soft logic for reasoning over uncertain temporal knowledge graphs. RLvLR-Stream [78] considers temporal close-path rules and learns the structure of rules from knowledge graph stream for reasoning.…”

Section: Temporal Logical Reasoningmentioning

confidence: 99%

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Ji,

Pan,

Cambria

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…AMIE [42] is a horn rule mining system which can work on large-scale KGs like YAGO2 [1]. Omran et al [43] proposed a system RLvLR for extracting closed-path rules, a special class of first-order rules, from RDF knowledge graphs. In contrast to traditional Inductive Logic Programming (ILP) learning approaches, PLvLR uses technology of embeddings to explore plausible paths instead of using a refinement operator to search the rule space.…”

Section: ) Real-world Kg Acquisitionmentioning

confidence: 99%

A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDD^xs)

Feng

Mayer

et al. 2021

IEEE Access

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Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of technologies, have now become a new form of knowledge representation, and a cornerstone for several applications from generic to specific industrial use cases. However, in some specific domains such as law enforcement, a real and large domain-oriented KG is often unavailable due to data privacy concerns. In such domains it is necessary to generate a synthetic KG which mimics the properties of a real KG in the domain. Although during the last two decades, a variety of graph data generators has been proposed to achieve the generation of different kinds of networks, the state-of-the-art synthetic graph data generators are not feasible to generate a realistic and synthetic KGs because KGs always contain data characteristics with specified semantics. In this work, we propose a schema-driven synthetic KG generation approach with extended graph differential dependencies (GDD x ), which is an extension of the recently developed graph entity/differential dependencies that represent formal constraints for graph data to enable the generation of desired graph patterns in synthetic KG. Next, we develop an effective KG generation algorithm that employs the schema and the pre-defined GDD x s. Finally, we evaluate our synthetic KG generator and compare with several stateof-the-art synthetic graph generators. The results from the experiments show that our KG generation method can generate KGs that exhibit the desired graph patterns, node attributes and degree distributions associated with each entity type in the graph's schema.

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“…In recent years, the emergence of large-scale knowledge graphs has promoted the development of knowledge-aware representation [14]. The knowledge graph contains rich knowledge [15]. Knowledgeaware representation brings in rich semantic information from the knowledge graph, which significantly improves the effectiveness of the search algorithms [16] and provides new opportunities for better understanding queries and documents [17,18].…”

Section: Related Workmentioning

confidence: 99%

A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

Kou

et al. 2021

Wireless Communications and Mobile Computing

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Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

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An Embedding-Based Approach to Rule Learning in Knowledge Graphs

Cited by 37 publications

References 21 publications

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDD^xs)

A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

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An Embedding-Based Approach to Rule Learning in Knowledge Graphs

Cited by 37 publications

References 21 publications

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDDxs)

A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

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Product

Resources

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A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDD^xs)