Semantic Support for Computational Land-Use Modelling

Postprin

A Semantic Workflow Mechanism to Realize Experimental Goals and Constraints

Postprin

Biocryomorphology:Integrating Microbial Processes with Ice Surface Hydrology, Topography, and Roughness

Published version. Also available at http://dx.doi.org/10.3389/feart.2015.00078</a

A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change

Agent-based models, perhaps more than other models, feature large numbers of parameters and potentially generate vast quantities of results data. This paper shows through the FEARLUS-G project (an ESRC e-Social Science Initiative Pilot Demonstrator Project) how deploying an agent-based model on the Semantic Grid facilitates international collaboration on investigations using such a model, and contributes to establishing rigorous working practices with agent-based models as part of good science in social simulation. The experimental workflow is described explicitly using an ontology, and a Semantic Grid service with a web interface implements the workflow. Users are able to compare their parameter settings and results, and relate their work with the model to wider scientific debate.Agent-Based Social Simulation, Experiments, Ontologies, Replication, Semantic Grid

An Ontology for Experimentation with a Social Simulation of Land Use Change

Publisher PD

An Empirical Investigation of Learning From the Semantic Web

Peer reviewedPostprin

Knowledge extraction from a small corpus of unstructured safeguarding reports

This paper presents results on the performance of a range of analysis tools for extracting entities and sentiments from a small corpus of unstructured, safeguarding reports. We use sentiment analysis to identify strongly positive and strongly negative segments in an attempt to attribute patterns on the sentiments extracted to specific entities. We use entity extraction for identifying key entities. We evaluate tool performance against non-specialist human annotators. An initial study comparing the inter-human agreement against inter-machine agreement shows higher overall scores from human annotators than software tools. However, the degree of consensus between the human annotators for entity extraction is lower than expected which suggests a need for trained annotators. For sentiment analysis, the annotators reached a higher agreement for annotating descriptive sentences compared to reflective sentences, while the inter-tool agreement was similarly low for the two sentence types. The poor performance of the entity extraction and sentiment analysis approaches point to the need for domain-specific approaches for knowledge extraction on these kinds of document. However, there is currently a lack of pre-existing ontologies in the safeguarding domain. Thus, in future, our focus is the development of such a domain-specific ontology