A knowledge graph embeddings based approach for author name disambiguation using literals

Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively

Extending the Visualization of Classification Interaction with Semantic Associations

General classification schemes hold the potential for being applied to large quantities of information resources. Yet the underlying infrastructure requires empirical understanding of the interaction between classifications and their inherent characteristics, as well as the inherent characteristics of the resources they classify. An important step is described here based on an attempt to derive terms from subject vocabularies (subject headings, index terms, terms from thesauri) in relation to UDC strings extracted from a random sample of KU Leuven MARC records and OCLC WorldCat MARC records. Results show see the clear presence of semantic clusters, which in future research might be generated from UDC strings and associated with other statistically-significant correlations to develop a navigable classificatory infrastructure for data-mining and information-sharing