graph construction

RoadTracer is a dataset for extraction of road networks from aerial images. It consists of a large corpus of high-resolution satellite imagery and ground truth road network graphs covering the urban core of forty cities across six countries. For each city, the dataset covers a region of approximately 24 sq km around the city center. The satellite imagery is obtained from Google at 60 cm/pixel resolution, and the road network from OSM. The dataset is split into a training set with 25 cities and a test set with 15 other cities. Source: RoadTracer: Automatic Extraction of Road Networks from Aerial Images Image Source: Bastani et al

The beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. Source: Rapidly Bootstrapping a Question Answering Dataset for COVID-19

A dataset for inter-personal relationship extraction which aims to facilitate information extraction and knowledge graph construction research. In total, IPRE has over 41,000 labeled sentences for 34 types of relations, including about 9,000 sentences annotated by workers. Source: IPRE: a Dataset for Inter-Personal Relationship Extraction

The **Perfume Co-Preference Network ** dataset comprises comprehensive user reviews and ratings collected from the Persian retail platform Atrafshan. This dataset, central to our research on community detection in fragrance preferences, includes 36,434 comments from 7,387 unique users, providing insights into consumer sentiment towards various perfumes. It is designed to facilitate the analysis of user preferences through sentiment analysis, allowing for the clustering of perfumes based on shared attributes. The dataset features three main components: 1. User Reviews and Perfume Attributes Dataset: This captures user sentiments expressed in comments, along with metadata such as user IDs, perfume details, and ratings across key attributes (scent, longevity, sillage, and design). 2. Emoji Mapping Dataset: This includes 392 common emojis mapped to their Persian equivalents to enhance sentiment analysis accuracy. 3. Sentiment Classification Results: This section includes three CSV files that detail sentiment classifications biased toward specific perfume attributes: Scent, Longevity, and Sillage. These classifications are derived from user comments using the ParsBert model, integrating user ratings to provide a nuanced understanding of consumer preferences. For access to the dataset and further details, please visit our GitHub repository. Total number of user comments: 36,434 Total number of unique users: 7,387 Number of emojis in mapping: 392 Number of CSV files with sentiment classifications: 3