No abstract

Product footprint describes the environmental impacts of a product system. To identify such impact, Life Cycle Assessment (LCA) takes into account the entire lifespan and production chain, from material extraction to final disposal or recycling. This requires gathering data from a variety of heterogeneous sources, but current access to those is limited and often expensive. The BONSAI project, instead, aims to build a shared resource where the community can contribute to data generation, validation, and management decisions. In particular, its first goal is to produce an open dataset and an open source toolchain capable of supporting LCA calculations. This will allow the science of lifecycle assessment to perform in a more transparent and more reproducible way, and will foster data integration and sharing. Linked Open Data and semantic technologies are a natural choice for achieving this goal. In this work, we present the first results of this effort 3 : (1) the core of a comprehensive ontology for industrial ecology and associated relevant data; and (2) the first steps towards an RDF dataset and associated tools to incorporate several large LCA data sources.

Knowledge graphs (KGs) represent facts in the form of subject-predicate-object triples and are widely used to represent and share knowledge on the Web. Their ability to represent data in complex domains augmented with semantic annotations has attracted the attention of both research and industry. Yet, their widespread adoption in various domains and their generation processes have made the contents of these resources complicated. We speak of knowledge graph exploration as of the gradual discovery and understanding of the contents of a large and unfamiliar KG. In this paper, we present an overview of the state-of-the-art approaches for KG exploration. We divide them into three areas: profiling, search, and analysis and we argue that, while KG profiling and KG exploratory search received considerable attention, exploratory KG analytics is still in its infancy. We conclude with an overview of promising future research directions towards the design of more advanced KG exploration techniques.

Life Cycle Sustainability Analysis (LCSA) studies the complex processes describing product life cycles and their impact on the environment, economy, and society. Effective and transparent sustainability assessment requires access to data from a variety of heterogeneous sources across countries, scientific and ecsonomic sectors, and institutions. Moreover, given their important role for governments and policymakers, the results of many different steps of this analysis should be made freely available, alongside the information about how they have been computed in order to ensure accountability. In this paper, we describe how Semantic Web technologies in general and PROV-O in particular, are used to enable transparent sharing and integration of datasets for LCSA. We describe the challenges we encountered in helping a community of domain experts with no prior expertise in Semantic Web technologies to fully overcome the limitations of their current practice in integrating and sharing open data. This resulted in the first nucleus of an open data repository of information about global production. Furthermore, we describe how we enable domain experts to track the provenance of particular pieces of information that are crucial in higher-level analysis.

Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes cumbersome. Thus, being able to cast exploratory queries in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so called example-based methods, in which the user, or the analyst circumvent query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful both in cases where a user is looking for information in an unfamiliar dataset, or simply when she is exploring the data without knowing what to find in there. In this tutorial, we present an excursus over the main methods for exploratory analysis, with a particular focus on examplebased methods. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data.