EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Abstract

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK-a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks-aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed-the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. We show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

Knowledge Graph-Enabled Cancer Data Analytics

“…A knowledge graph denotes a graph of entities with one or more properties that maintain defined semantic relationships to other entities. In a knowledge graph, the entities are represented as nodes, and the relationships are represented as edges [8], [9]. Many academic institutions (e.g., YAGO, DBPedia) and industries (e.g., Google, Microsoft, Facebook) are creating and taking advantage of the knowledge graph technology [10], [11].…”

“…It is easy to map different data models like relational, tree, key value store, and graph to RDF triples. The implication of this is that the RDF is actually a generic data model [9], [14].…”

“…This is what makes the RDF a better option for dynamic data integration. Furthermore, the RDF does not need code changes or software redesigns to handle schema evolution [9]. r Easy to incorporate semantics and links: A large collection of ontologies [15] and vocabularies [16] are available, which model the concepts of a domain.…”

“…The RDF supports easy ontology and vocabulary integration compared to the RDBMS [17]. It is easy to dynamically link multiple data sources in the RDF, which is tedious in the RDBMS [9], [18].…”

“…The above types of queries require numerous joins, which is an expensive operation in the RDBMS's Structured Query Language (SQL). However, joining is easy in SPARQL Protocol and RDF Query Language (SPARQL) because SPARQL is particularly designed to query graph structures [9].…”