A Temporal Knowledge Graph Dataset for Profiling

The Semantic representation plays a vital role in information extraction and information retrieval. Current state-of-the-art for semantic representation are Temporal Knowledge Graphs. Due to tremendous growth of things like Big Data we need a way to devise how we should represent available knowledge such that it can be more effective. Finding out semantically connected things from huge data lake is complex task which comes under the umbrella of Computer Science specifically semantic web. In this document we have presented a novel temporal Knowledge Graph dataset. Proposed dataset encompasses ten different types of relationship among which two relations are temporal based. Dataset incorporate temporal data of hundred people. For data acquisition we have used university campus environment. By using facial recognition technique backed with Convolutional Neural Network (CNN) [1] we have collected data of each person. For training our model on facial features we have collected approx. 500 images of each person. Afterward for profiling people we deployed surveillance cameras on different locations of campus. Finally, acquisitioned data has been transformed to Temporal Knowledge Graph. We will discuss data collection process details in dedicated section. Knowledge Graph is simply a graph representing interconnections between entities [2]. For analysis of dataset we have modelled some semantic queries. Some sample queries are as under: Where were XYZ person went all the day today? Which routes Prof. usually takes in going to department office? How many students were present on main gate in today at XYZ time today? How many people were present on XYZ seminar/ workshop? How many people were present in the Research Lab when short-circuit happened. Which places Alice visited today? Which areas of campus faced Student congestion today? How many students attended the particular event on specific date? How many faculty members were present in today's meeting? How many ambulances/ fire fighters reached on time when certain event occurred? How many people joined in the opening ceremony of department/ Lab? How many people visited the particular department/ departments? What are the peak time of admission in terms of students? How many and which departments Alice visited in her last visit. Which library/ department Bob visits most often? How many students visited accounts office today? How many students visited Chancellor/ Vice Chancellor office today? How many people were at Cafeteria at XYZ time? How many students were present at playground today? How many students were present at gymnasium today? The list is not exhaustive and many questions related to current and past events are required to be probed while future can be predicted. Proposed dataset can be used in different domains like citizen profiling, workplace profiling, parental monitoring, student profiling, autonomous vehicle profiling etc.