## 1 Introduction
### Motivating Example of Linked Open Data in ZTF Repository  
Astronomy is the science that investigates the physics, chemistry, and evolution of celestial objects and phenomena that originate outside the Earth's atmosphere, such as supernova explosions, gamma ray bursts, and cosmic microwave background radiation [14]. The four Vs of big data in the astronomy realm [15; 16] are as follows: volume, variety, velocity, and value. Volume refers to the quantity of data. Terabytes, petabytes, and even exabytes are used to describe data. As a result, big data present issues in terms of collection, cleansing, curation, integration, storage, processing, indexing, search, sharing, transferring, mining, analysis, and visualization. Conventional technologies are incapable of dealing with such massive amounts of data. Numerous ground- and space-based wide sky surveys are generating an avalanche of data in all fields of astronomy. Variety is an indication of data complexity. Astronomical data are primarily comprised of images, spectra, time series, and simulation data. The majority of data are stored in catalogs or databases. The data from separate telescopes or projects are stored in their own formats, which challenges the analysis phase integration of data from disparate sources. Each data item contains a thousand or more features; this creates an issue of high dimensionality. Additionally, data come in a variety of formats: structured, semi-structured, unstructured, and mixed. Velocity refers to the rate at which data are generated, transmitted, and analyzed. In terms of data volume, LSST will generate one SDSS per night for a period of ten years. Clearly, batch, stream, near-real-time, or real-time data analysis is required. LSST anticipates discovering 1000 new supernovae per night for ten years, implying that at least 10-100,000 notifications will be requested. How scientists will efficiently mine, correctly identify, and target supernova candidates, as well as conduct follow-up observations in ten years, are significant problems. The term \"value\" refers to the data's great astronomical worth. It is exciting and inspirational to discover strange, rare, unexpected, and novel objects or occurrences in astronomy. Similarly, identifying a novel distribution trend or law is extremely valuable. Numerous scientific data organizations have recently encountered the challenge of offering data management solutions for huge, heterogeneous datasets [17; 18]. For instance, data from the Zwicky Transient Facility (ZTF) include relational data, imagery, light curves, and text. The ZTF data contain Epochal Science images and catalog files which further point to reference images and catalog files, so we can say that this falls under the category of linked open data. ZTF is a new time-domain survey that employs a wide-field survey camera to search for and analyze supernovae, variable stars, binaries, active galactic nuclei (AGN), and asteroids [19]. The ZTF is intended for the detection of near-Earth asteroids, unique and rapidly changing flux transients, and all types of variable sources in the Galactic plane [20]. The Intermediate Palomar Transient Factory (iPTF) project which was launched in the beginning of 2013 also features similar linkages between epoch science images and reference images, and is built on the heritage of the Palomar Transient Factory which is linked open data. This project was headed by Caltech (PTF) [21]. Through historical Palomar Transient Factory data and fast follow-up investigations of transient sources, iPTF enhanced tools for data reduction and source classification. In 2017, iPTF became the Zwicky Transient Factory, utilizing a rebuilt version of the same telescope that was used for iPTF. ZTF observations are made with a new camera that has a 47-square-degree field of view and is installed on the [PERSON] 48-inch (1.2 m) [PERSON] telescope. The camera consists of 16 CCDs divided into four readout quadrants. As a result, each ZTF exposure generates 64 CCD quadrant images [19].