Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link prediction. Various amount of scholarly metadata have been made vailable as knowledge graphs from the diversity of data providers and agents. However, these high-quantities of data remain far from quality criteria in terms of completeness while growing at a rapid pace. Most of the attempts in completing such KGs are following traditional data digitization, harvesting and collaborative curation approaches. Whereas, advanced AI-related approaches such as embedding models - specifically designed for such tasks - are usually evaluated for standard benchmarks such as Freebase and Wordnet. The tailored nature of such datasets prevents those approaches to shed the lights on more accurate discoveries. Application of such models on domain-specific KGs takes advantage of enriched meta-data and provides accurate results where the underlying domain can enormously benefit. In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata. The results show a significant shift in the accuracy and performance evaluation of the model on a dataset with scholarly metadata. The newly proposed version of TransE obtains 99.9% for link prediction task while original TransE gets 95%. In terms of accuracy and Hit@10, TransE outperforms other embedding models such as ComplEx, TransH and TransR experimented over scholarly knowledge graphs
翻译:知识图表(KGs),即信息作为语义图的表示,为包括问答、建议和链接预测在内的许多任务提供了一个重要的测试床。大量学术元数据作为数据提供者和代理方多样性的知识图表而成为了各种数据提供者和代理方的知识图表。然而,这些数据的高数量在完整性方面仍然远远没有达到质量标准,而以快速增长的速度增长。完成这类知识图表的大多数尝试都遵循传统的数据数字化、收获和协作整理方法。而先进的AI相关方法,如嵌入模型(专门为此类任务设计的)通常为FreeBase和Wordnet等标准基准进行评估。这类数据集的定制性质使得无法使这些方法在更准确的发现上亮亮亮亮光。将这类模型应用于特定域的模型利用了经过更新的元数据,并提供了准确的结果,使基础领域大有裨益。在这项工作中,Trans-E嵌入模型经过协调,用于对学术元数据进行具体联系的预测任务。结果显示,对数据库模型的准确性和性评估模型的准确性和绩效有显著变化,例如FreebaseBase_95 Transtranstransexalalalalalal 和Trechalstexalstital 。