In the past years, a new generation of systems has emerged, which apply recent advances in generative Artificial Intelligence (AI) in combination with traditional technologies. Specifically, generative AI is being delegated tasks in natural language or vision understanding within complex hybrid architectures that also include databases, procedural code, and interfaces. Process Knowledge Graphs (PKG) have a long-standing tradition within symbolic AI research. On the one hand, PKGs can play an important role in describing complex, hybrid applications, thus opening the way for addressing fundamental challenges such as explaining and documenting such systems (unpacking). On the other hand, by organising complex processes in simpler building blocks, PKGs can potentially increase accuracy and control over such systems (repacking). In this position paper, we discuss opportunities and challenges of PGRs and their potential role towards a more robust and principled design of AI applications.

During an emergency event, such as a fire evacuation, support services benefit from having information about people who may require special assistance. In this context, health data represents a particularly important source of information, as it can allow an emergency response system to build an accurate picture of people's relevant health conditions and use this to advise responders. However, to perform this task, a system needs to represent and reason over the evolution of health conditions over time. Crucially, it needs to predict the probability that a potentially relevant condition mentioned in a health record is still valid at the time of the emergency. In this paper, we propose a methodology for representing the evolution of health conditions and reasoning about them in the context of an emergency scenario. To support our approach with data, we develop a pipeline to capture knowledge about condition evolution from reliable sources in natural language. We incorporate these two components into a system that predicts a person's likelihood of being vulnerable during an emergency event. Finally, we demonstrate that representing and reasoning about condition evolution improves the quality and precision of the recommendations provided by our system to emergency services.

The task of identifying pieces of evidence in texts is of fundamental importance in supporting qualitative studies in various domains, especially in the humanities. In this paper, we coin the expression themed evidence, to refer to (direct or indirect) traces of a fact or situation relevant to a theme of interest and study the problem of identifying them in texts. We devise a generic framework aimed at capturing themed evidence in texts based on a hybrid approach, combining statistical natural language processing, background knowledge, and Semantic Web technologies. The effectiveness of the method is demonstrated on a case study of a digital humanities database aimed at collecting and curating a repository of evidence of experiences of listening to music. Extensive experiments demonstrate that our hybrid approach outperforms alternative solutions. We also evidence its generality by testing it on a different use case in the digital humanities.

Linked lists represent a countable number of ordered values , and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID , a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.

When publishing data, data licences are used to specify the actions that are permitted or prohibited, and the duties that target data consumers must comply with. However, in complex environments such as a smart city data portal, multiple data sources are constantly being combined, processed and redistributed. In such a scenario, deciding which policies apply to the output of a process based on the licences attached to its input data is a difficult, knowledge-intensive task. In this paper, we evaluate how automatic reasoning upon semantic representations of policies and of data flows could support decision making on policy propagation. We report on the results of a user study designed to assess both the accuracy and the utility of such a policy-propagation tool, in comparison to a manual approach.

Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.