Knowledge graph representation for experience sampling data: A proof of concept
Creators: name: Ammar, Ammar
name: Ippel, Lianne
name: Celebi, Remzi
name: Delespaul, Philippe
Publisher: name: Zenodo
Year: 2020

description: How data are represented is crucial for researchers to facilitate analyses and explore a dataset. Recently, knowledge graph representation has gained popularity among researchers and large companies to model their data for different applications (e.g., web search, question answering). In a knowledge graph, a researcher creates a network of linked concepts, entities, and instances, from which data-driven rules can be derived. Therefore, knowledge graphs are powerful tools to explore and understand data via an underlying network or ‘graph structure’. A semantic model is used to interpret data in a knowledge graph where each node and relation has a type. While knowledge graphs are often encountered in health sciences, their application within the social sciences is rare. This presentation demonstrates how to model longitudinal survey data using the Resource Description Framework (RDF). RDF is a standard knowledge graph representation on the web that researchers can use to interpret and inspect data. The RDF uses the following data structure: Subject - Predicate - Object, e.g., Patient - feels - unhappy. For this study, we use data collected via the Experience Sampling Method smartphone application, called Psymate™. This app notifies the user 10 times a day to fill out a short questionnaire about the current mood and whereabouts. The notifications, or beeps, are unevenly spread throughout the day, though on average 90 minutes apart. To represent the ESM data, we created an RDF data model consisting of Subject, SurveyBeep, Answer, Date classes. Subject represents an individual that takes part in a survey. SurveyBeep represents a survey entity completed by an individual for a specific moment in time (called a beep). Individuals' answer (Answer) corresponds to the answer to each question in a survey. Each question in a survey is defined as a relation between SurveyBeep and Answer and is a sub-type of ‘question’ relation. Date class indicates the date when a survey is completed. Also, the nextDate relation is used to capture the longitudinal relationship between two surveys (SurveyBeep) completed on consecutive dates. After modeling the data as RDF, we used GraphDB (http://graphdb.ontotext.com/) tool to store, query, and visualize this data. In addition to visualizing a small part of the knowledge graph, we can use GraphDB to analyze the data with SPARQL language, which allows us to query the RDF data more easily compared to relational databases. For example, we can use a SPARQL query to find which subjects' moods have changed dramatically in a few consecutive days. This knowledge representation allows us to formulate the behavior change of an individual over time in form of RDF triples as path and subgraph in the knowledge graph. In the future, we plan to use graph mining algorithms to extract meaningful patterns from this knowledge graph. We will focus on mining the frequent paths and subgraphs which could help us to understand the subjects’ behavior change over time.