Leveraging multilingual descriptions for link prediction: Initial experiments

In most Knowledge Graphs (KGs), textual descriptions ofentities are provided in multiple natural languages. Additional informa-tion that is not explicitly represented in the structured part of the KGmight be available in these textual descriptions. Link prediction modelswhich make use of entity descriptions usually consider only one language.However, descriptions given in multiple languages may provide comple-mentary information which should be taken into consideration for thetasks such as link prediction. In this poster paper, the benefits of mul-tilingual embeddings for incorporating multilingual entity descriptionsinto the task of link prediction in KGs are investigate

Migranalytics: Entity-based analytics of migration tweets

This poster focuses on a visual analysis of the tweets related to European migration crisis. It uses TweetsKB as a starting point and then formulates a search criteria for extracting tweets by enriching semantic entities and hashtags starting from the seed word \Refugee". It combines European migration statistics with the information obtained by the tweets and provides visual analysis from different perspectives

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

Temporal Evolution of the Migration-related Topics on Social Media?

This poster focuses on capturing the temporal evolution of migration-related topics on relevant tweets. It uses Dynamic Embedded Topic Model (DETM) as a learning algorithm to perform a quantitative and qualitative analysis of these emerging topics. TweetsKB is extended with the extracted Twitter dataset along with the results of DETM which considers temporality. These results are then further analyzed and visualized. It reveals that the trajectories of the migration-related topics are in agreement with historical events