Domain-Specific Automatic Scholar ProfilingBased on Wikipedia

Chuai, Ziang; Geng, Qingbo; Jin, Jian

doi:10.1145/3366424.3383565

Cited by 1 publication

(1 citation statement)

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“…It is observed that the embedding vectors are semantically far from original mappings and generate ambiguous entity pairs in translation-based models. To make vectors semantically close TransP [101], a novel translation with penalty-based embedding model is taken into consideration. A novel Relation Embedding method based on local context is proposed to enhance the entity typing performance followed by keyword extraction method to highlight critical concepts in selective bibliographies.…”

Section: Translation-based Modelsmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

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The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

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Section: Translation-based Modelsmentioning

confidence: 99%