Despite advances in the data, models, and methods underpinning environmental life cycle assessment (LCA), it remains challenging for practitioners to effectively communicate and interpret results. These shortcomings can bias decisions and hinder public acceptance for planning supported by LCA. This paper introduces a method for interpreting LCA results, the Argumentation Corrected Context Weighting-LCA (ArgCW-LCA), to overcome these barriers. ArgCW-LCA incorporates stakeholder preferences, corrects unjustified disagreements, and allows for the inclusion of non-environmental impacts (e.g., economic, social, etc.) using a novel weighting scheme and the application of multi-criteria decision analysis to provide transparent and context-relevant decision support. We illustrate the utility of the method through two case studies: a hypothetical decision regarding energy production and a real-world decision regarding polyphenol extraction technologies. In each case, we surveyed a relevant stakeholder group on their environmental views and fed their responses into the model to provide decision support that is relevant to their perspective. We found marked differences between results using ArgCW-LCA and results from a conventional analysis using an equal-weighting scheme, as well as differentiation between stakeholder preference groups, indicating the importance of applying the perspective of the particular stakeholder group. For instance, there was a rank reversal of alternatives when comparing between an equal weighting approach for all environmental and economic dimensions and ArgCW-LCA. ArgCW-LCA provides opportunity for both public and private sector incorporation of LCA, such as in developing enlightened stakeholder value measures. This is achieved through enabling the LCA practition to provide public and private actors’ interpreted LCA results in a manner that incorporates educated stakeholder perspectives. Furthermore, the method encourages stakeholder multiplicity through participatory design and policymaking that can enhance public backing of actions that can make society more sustainable.

In this paper we are interested in the task of a data engineer choosing what tool to use to perform defeasible reasoning with a first order logic knowledge base. To this end we propose the first benchmark in the literature that allows one to classify first order defeasible reasoning tools based on their semantics, expressiveness and performance.

Abstract. In this paper we focus on the problem of how lineage for existential rules knowledge bases. Given a knowledge base and an atomic ground query, we want to output all minimal provenance paths of the query (i.e. the sequence of rule applications that generates an atom from a given set of facts). Obtaining all minimal provenance paths of a query using forward chaining can be challenging due to the simplifications done during the rule applications of different chase mechanisms. We build upon the notion of Graph of Atoms Dependency (GAD) and use it to solve the problem of provenance path loss in the context of forward chaining with existential rules. We study the properties of this structure and investigate how different chase mechanisms impact its construction.