Research on knowledge graph alignment model based on deep learning

Yu, Chuanming; Wang, Rui; Liu, Ying‐Hsang; Luo, An

doi:10.1016/j.eswa.2021.115768

Cited by 16 publications

(4 citation statements)

References 35 publications

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“…However, these options leave many important questions open in fields such as graph alignment methodologies [73], and we are also aware of having designed a methodology that must be completed in further subgraph interpretations [74] that we plan to develop in further research.…”

Section: Discussionmentioning

confidence: 99%

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

et al. 2023

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The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates with interpretable patterns of web usage, providing scientific reasoning with conceptual fingerprints and helping identify eligible hypotheses. In a previous article, we showed how usage log data, in the form of ‘documentary tracks’, help determine distinct cognitive communities (called adversarial cliques) within sub-graphs. A typology of these documentary tracks through a triplet of measurements from logs (intensity, variety and attention) describes the potential approaches to a (research) question. GRAPHYP assists interpretation as a classifier, with possibilistic graphical modeling. This paper (Paper 2) shows what this approach can bring to scientific reasoning; it involves visualizing complete interpretable pathways, in a multi-hop assessor shift, which users can then explore toward the ‘best possible solution’—the one that is most consistent with their hypotheses. Applying the Leibnizian paradigm of scientific reasoning, GRAPHYP highlights infinitesimal learning pathways, as a ‘multiverse’ geometric graph in modeling possible search strategies answering research questions.

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Section: Discussionmentioning

confidence: 99%

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

et al. 2023

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“…We have also observed that KG research has made significant advances in the geometry of expressively explainable KGs by applying new methods of causal inference to transfer learning or multiverse analysis. Progress is made on new frontiers for KG management such as the optimization of processing heterogeneous data at any scale [45]. Workflow execution is also a hot topic for both, and ELK is studied in environments of exascale computing and for training data in automated analysis [46].…”

Section: Discussionmentioning

confidence: 99%

Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making

et al. 2023

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The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits of data governance but also to consider the widest possible range of relevant information when deciding their next steps. In this paper, we examine how data management and data visualization are used in organizations that use ELK solutions to collect integrated data from different sources in one place and visualize and analyze them in near-real time. We also present some interpretable Knowledge Graphs, GRAPHYP, which are innovative by processing an analytical information geometry and can be used together with an ELK to improve data quality and visualize the data to make informed decisions in organizations. Good decisions are the backbone of successful organizations. Ultimately, this research is about integrating a combined solution between ELK and SKG GRAPHYP and showing users the advantages in this area.

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“…The look at 3-dimensional relation as a translation from the top to the tail commercial enterprise with different entity. In this manner, initiative embedding and relationships are studied to growth the possibilities of this translation (Yu et al, 2021). At that point of research implications, within the busines case, we are able to additional identify to boom the variety and possible probabilities of predicting its meaning.…”

Section: William Adam Was Technical Writer Poet Philosophermentioning

confidence: 99%

Learning of Knowledge Graphs with Entity Descriptions

Conde¹

2022

IJMRA

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The representing learning makes specialty of knowledge graph and it indicates the difference between different entities. The knowledge graph representing with the low-dimensional space. In fact, most of the method usually concern of description of entity which is hard for existing strategies to take benefit of. Here, we recommend a new representing learning method with knowledge graphs that uses entity description. We evaluate our method on two tasks like knowledge graph and entity classification. Experimental effects on actual-world datasets show that our version plays higher than different baseline fashions, especially under the zero-short setting, which indicate that our technique for novel the entity description.

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Research on knowledge graph alignment model based on deep learning

Cited by 16 publications

References 35 publications

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making

Learning of Knowledge Graphs with Entity Descriptions

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