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new paper: Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps

May 15th, 2017, by Tim Finin, posted in AI, Machine Learning, NLP, Paper, Semantic Web

Jennifer Sleeman, Milton Halem, Tim Finin, and Mark Cane, Modeling the Evolution of Climate Change Assessment Research Using Dynamic Topic Models and Cross-Domain Divergence Maps, AAAI Spring Symposium on AI for Social Good, AAAI Press, March, 2017.

Climate change is an important social issue and the subject of much research, both to understand the history of the Earth’s changing climate and to foresee what changes to expect in the future. Approximately every five years starting in 1990 the Intergovernmental Panel on Climate Change (IPCC) publishes a set of reports that cover the current state of climate change research, how this research will impact the world, risks, and approaches to mitigate the effects of climate change. Each report supports its findings with hundreds of thousands of citations to scientific journals and reviews by governmental policy makers. Analyzing trends in the cited documents over the past 30 years provides insights into both an evolving scientific field and the climate change phenomenon itself. Presented in this paper are results of dynamic topic modeling to model the evolution of these climate change reports and their supporting research citations over a 30 year time period. Using this technique shows how the research influences the assessment reports and how trends based on these influences can affect future assessment reports. This is done by calculating cross-domain divergences between the citation domain and the assessment report domain and by clustering documents between domains. This approach could be applied to other social problems with similar structure such as disaster recovery.

New paper: A Question and Answering System for Management of Cloud Service Level Agreements

May 13th, 2017, by Tim Finin, posted in AI, KR, NLP, Paper, Semantic Web

Sudip Mittal, Aditi Gupta, Karuna Pande Joshi, Claudia Pearce and Anupam Joshi, A Question and Answering System for Management of Cloud Service Level Agreements,  IEEE International Conference on Cloud Computing, June 2017.

One of the key challenges faced by consumers is to efficiently manage and monitor the quality of cloud services. To manage service performance, consumers have to validate rules embedded in cloud legal contracts, such as Service Level Agreements (SLA) and Privacy Policies, that are available as text documents. Currently this analysis requires significant time and manual labor and is thus inefficient. We propose a cognitive assistant that can be used to manage cloud legal documents by automatically extracting knowledge (terms, rules, constraints) from them and reasoning over it to validate service performance. In this paper, we present this Question and Answering (Q&A) system that can be used to analyze and obtain information from the SLA documents. We have created a knowledgebase of Cloud SLAs from various providers which forms the underlying repository of our Q&A system. We utilized techniques from natural language processing and semantic web (RDF, SPARQL and Fuseki server) to build our framework. We also present sample queries on how a consumer can compute metrics such as service credit.

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