[Paper Review] A Knowledge Ecosystem for the Food, Energy, and Water System

Praveen Rao, Anas Katib+1 more|arXiv (Cornell University)|Sep 17, 2016

Scientific Computing and Data Management13 references3 citations

TL;DR

This paper proposes a knowledge ecosystem for the Food, Energy, and Water (FEW) system that leverages Semantic Web technologies and statistical relational learning to enable semantic integration of heterogeneous U.S. federal datasets. By constructing a probabilistic knowledge base with rich ontologies and OWL-based reasoning, the system discovers actionable insights—such as drought-induced price increases—through automated inference over RDF quadruples and SPARQL queries on large-scale data.

ABSTRACT

Food, energy, and water (FEW) are key resources to sustain human life and economic growth. There is an increasing stress on these interconnected resources due to population growth, natural disasters, and human activities. New research is necessary to foster more efficient, more secure, and safer use of FEW resources in the U.S. and globally. In this position paper, we present the idea of a knowledge ecosystem for enabling the semantic data integration of heterogeneous datasets in the FEW system to promote knowledge discovery and superior decision making through semantic reasoning. Rich, diverse datasets published by U.S. federal agencies will be utilized. Our knowledge ecosystem will build on Semantic Web technologies and advances in statistical relational learning to (a) represent, integrate, and harmonize diverse data sources and (b) perform ontology-based reasoning to discover actionable insights from FEW datasets.

Motivation & Objective

• To address the challenge of integrating heterogeneous, semantically disparate FEW datasets from U.S. federal agencies such as USDA, NOAA, USGS, and NDMC.
• To build a scalable, evolving knowledge base with rich ontologies that model entities, relationships, and domain rules in the FEW domain.
• To enable automated, ontology-based reasoning over the FEW knowledge base to discover hidden insights and validate scientific hypotheses.
• To support superior decision-making for FEW stakeholders through semantic data integration and scalable query processing.
• To overcome limitations in current data formats (e.g., CSV, Excel) and lack of shared ontologies that hinder intelligent system consumption.

Proposed method

• Represent FEW data as RDF quadruples (subject, predicate, object, context) to capture provenance and enable graph-based modeling.
• Use Semantic Web standards—RDF, SPARQL, and OWL 2—to model entities, classes, properties, and relationships in the FEW domain.
• Apply statistical relational learning and Markov Logic Networks (MLNs) to infer semantic relationships such as owl:sameAs, owl:equivalentProperty, and owl:differentFrom across entities.
• Leverage the RIQ system for efficient SPARQL query processing over RDF named graphs and integrate it with OWL 2 DL reasoners like Pellet for scalable reasoning.
• Employ reification in RDF to represent uncertainty and probabilities in extracted facts and rules.
• Explore parallel query processing using RIQ on Apache Spark to scale reasoning over billions of RDF statements and OWL assertions.

Experimental results

Research questions

• RQ1 How can heterogeneous, large-scale FEW datasets from U.S. federal agencies be semantically integrated into a unified knowledge base?
• RQ2 What techniques enable scalable, probabilistic reasoning over large FEW knowledge bases using OWL 2 DL and statistical relational learning?
• RQ3 How can provenance-aware RDF quadruples and named graphs support traceability and trust in FEW data integration?
• RQ4 In what ways can automated reasoning uncover non-obvious causal relationships—such as drought impacts on meat and biofuel prices—across interdependent FEW systems?
• RQ5 How can semantic reasoning resolve apparent contradictions in FEW data, such as low rainfall claims versus high moisture index values, by incorporating domain-specific context?

Key findings

• The knowledge ecosystem enables automated inference of causal impacts, such as linking a drought in Jackson County, Missouri, to increased meat and biofuel prices through logical deduction from OWL 2 assertions.
• An OWL reasoner can validate apparent contradictions in FEW data, such as a rancher reporting low rainfall while a VegDRI index shows 'green' moisture levels, by recognizing context-specific factors like warm-season grass resilience.
• The integration of RIQ with OWL 2 DL reasoning allows scalable query processing over large RDF datasets by mapping reasoning tasks into complex SPARQL queries on named graphs.
• Statistical relational learning via MLNs supports the inference of semantic equivalences and differences between entities, improving knowledge base harmonization across diverse data sources.
• The system enables end-users to discover actionable insights—such as price fluctuations due to climate events—without manual correlation of disparate datasets.
• The approach demonstrates feasibility for large-scale, automated knowledge discovery in the FEW domain using semantic technologies, though accuracy depends on the quality of statistical inference and data provenance.