Link Prediction

This dataset gathers 14,857 entities, 133 relations, and entities corresponding tokenized text from PubMed. It contains 875,698 training pairs, 109,462 development pairs, and 109,462 test pairs.

SINS is a database of continuous real-life audio recordings in a home environment. The home is a vacation home and one person lived there during the recording period of over on week. It was collected using a network of 13 microphone arrays distributed over the multiple rooms. Each microphone array consisted of 4 linearly arranged microphones. Recordings were annotated based on the level of daily activities performed in the environment. Source: https://www.cs.tut.fi/sgn/arg/dcase2017/documents/workshop_papers/DCASE2017Workshop_Dekkers_141.pdf Image Source: https://www.cs.tut.fi/sgn/arg/dcase2017/documents/workshop_papers/DCASE2017Workshop_Dekkers_141.pdf

Taobao dataset which is pre-processed in TGN Style.

This dataset is used for user identity linkage across two online social networks in Chinese. It contains two popular Chinese social platforms: Sina Weibo\footnote{https://weibo.com} and Douban\footnote{https://www.douban.com}. Details: * 9,714 users and 117,218 relations in Weibo; 9,526 users and 120,245 relations in Douban; 1,397 pair of matched users. * Approximate power-law degree distribution and high aggregation coefficient. * Multiple text attributes available, including username, geographical location and recent (text) posts of the users. * Construction time: April 2020.

Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities can be integrated by retraining the model from scratch in principle, such an approach is infeasible for large-scale KGs, where retraining is expensive and new entities may arise frequently. In this paper, we propose and describe a large-scale benchmark to evaluate semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It provides transductive, k-shot, and 0-shot LP tasks, each varying the available information from (i) only KG structure, to (ii) including textual mentions, and (iii) detailed descriptions of the entities. We report on a small study of recent approaches and found that semi-inductive LP performance is far from transductive performance on long-tail entities throughout all experiments. The benchmark provides a test bed for further research into integrating context and textual information in semi-inductive LP models.