In process mining, a log exploration step allows making sense of the event traces; e.g., identifying event patterns and illogical traces, and gaining insight into their variability. To support expressive log exploration, the event log can be converted into a Knowledge Graph (KG), which can then be queried using general-purpose languages. We explore the creation of semantic KG using the Resource Description Framework (RDF) as a data model, combined with the general-purpose Notation3 (N3) rule language for querying. We show how typical trace querying constraints, inspired by the state of the art, can be implemented in N3. We convert case- and object-centric event logs into a trace-based semantic KG; OCEL2 logs are hereby "flattened" into traces based on object paths through the KG. This solution offers (a) expressivity, as queries can instantiate constraints in multiple ways and arbitrarily constrain attributes and relations (e.g., actors, resources); (b) flexibility, as OCEL2 event logs can be serialized as traces in arbitrary ways based on the KG; and (c) extensibility, as others can extend our library by leveraging the same implementation patterns.

Using rules for home automation presents several challenges, especially when considering multiple stakeholders in addition to residents, such as homeowners, local authorities, energy suppliers, and system providers, who will wish to contribute rules to safeguard their interests. Managing rules from various sources requires a structured procedure, a relevant policy, and a designated authority to ensure authorized and correct contributions and address potential conflicts. In addition, the smart home rule language needs to express conditions and decisions at a high level of abstraction without specifying implementation details such as interfaces, access protocols, and room layout. Decoupling high-level decisions from these details supports the transferability and adaptability of rules to similar homes. This separation also has important implications for structuring the smart home system and the security architecture. Our proposed approach and system implementation introduce a rule management process, a rule administrator, and a domain-specific rule language to address these challenges. In addition, the system provides a learning process that observes residents, detects behavior patterns, and derives rules which are then presented as recommendations to the system.

There is consensus, especially in our current deep-learning era, that more training data almost always helps improve performance of our deep learning models. But the process of collecting labeled data remains a costly and cumbersome task. Naturally, researchers started looking into this problem, which has led to development of various techniques for reducing the labeling cost. Among these, is a popular technique called weak supervision, in which a collection of heuristics and rules are used to label the data. Of course, the labels would be noisy but these weak labels have proven to be valuable as long as the rules have a reasonable error rate.

Towards a computer-interpretable actionable formal model to encode data governance rules Rui Zhao School of Informatics University of Edinburgh Edinburgh, UK s1623641@sms.ed.ac.uk Malcolm Atkinson School of Informatics University of Edinburgh Edinburgh, UK Malcolm.Atkinson@ed.ac.uk Abstract --With the needs of science and business, data sharing and reuse has become an intensive activity for various areas. In many cases, governance imposes rules concerning data use, but there is no existing computational technique to help data-users comply with such rules. We argue that intelligent systems can be used to improve the situation, by recording provenance records during processing, encoding the rules and performing reasoning. We present our initial work, designing formal models for data rules and flow rules and the reasoning system, as the first step towards helping data providers and data users sustain productive relationships. I NTRODUCTION Data ethics and privacy are of rising importance, especially with the establishment of GDPR [1]. Similar issues also apply in research when data from various sources are used as inputs to analyses and simulations. Researchers are aware that there are governance rules applied to the data, but they can easily lose track of the rules when the number of sources becomes large. The large volume of rules brings problem from three aspects: 1) to fully read and understand the rules; 2) to consider the consequence of combining data and their associate rules; 3) to assign rules to output so that results can be used compliantly. One response is to make data open and freely accessible (e.g. This sounds nice but it still leaves rules, for example to properly acknowledge sources and to protect personal and commercially sensitive data, even within collaborating communities [4]. This work has been accepted and should appear in the Proceedings of IEEE eScience 2019 Conference (BC2DC).

The Semantic Web Rule Language (SWRL) is a direct extension of OWL 2 DL with a subset of RuleML, and it is designed to be the rule language of the Semantic Web. This paper explores the state-of-the-art of SWRL's expressiveness extensions proposed over time. As a motivation, the effectiveness of the SWRL/OWL combination in modeling domain facts is discussed while some of the common expressive limitations of the combination are also highlighted. The paper then classifies and presents the relevant language extensions of the SWRL and their added expressive powers to the original SWRL definition. Furthermore, it provides a comparative analysis of the syntax and semantics of the proposed extensions. In conclusion, the decidability requirement and usability of each expressiveness extension are evaluated towards an efficient inclusion into the OWL ontologies.

Whether selling via a website or app, you need to respond immediately to each step of the journey. You need to "see" your customer, understand who they are, collect the right data, evaluate their needs, detect anomalous behavior, assess risk / pricing / product / affordability, dynamically adapt the customer journey and make many other decisions... and all this in real-time. To do all this and more, Zoral has developed one of the world's most advanced, intelligent decision engines Zoral Decision Engine (ZDE). In many ways selling digitally is no different to selling face to face. You need to understand as much as possible about your customer and ask the right questions.

I am a practicing Expert Witness, with report, deposition, and court testimony experience. And I've used that system to automate forensic analysis in the course of my Expert Witness work.An expert system has two main objectives, after capturing a body of expertise in the form of rules: o Provide that expertise in the absence of the expert. However, nothing prevents the creation of expert system rules that do embody such learning behavior, and in fact I often create such rules in the course of my work. Once that happens, I do regard it as AI.A key feature of applying expert systems to legal work is the leverage they provide. If a case involves a large body of code, data, or text, and a judge (who often doesn't understand the technical implications of discovery in such a case) mandates a short deadline for discovery completion, automation is the only way to achieve the needed results. And it's expert systems such as mine that provide that automation.

Finite chase, or alternatively chase termination, is an important condition to ensure the decidability of existential rule languages. In the past few years, a number of rule languages with finite chase have been studied. In this work, we propose a novel approach for classifying the rule languages with finite chase. Using this approach, a family of decidable rule languages, which extend the existing languages with the finite chase property, are naturally defined. We then study the complexity of these languages. Although all of them are tractable for data complexity, we show that their combined complexity can be arbitrarily high. Furthermore, we prove that all the rule languages with finite chase that extend the weakly acyclic language are of the same expressiveness as the weakly acyclic one, while rule languages with higher combined complexity are in general more succinct than those with lower combined complexity.

Finite chase, or alternatively chase termination, is an important condition to ensure the decidability of existential rule languages. In the past few years, a number of rule languages with finite chase have been studied. In this work, we propose a novel approach for classifying the rule languages with finite chase. Using this approach, a family of decidable rule languages, which extend the existing languages with the finite chase property, are naturally defined. We then study the complexity of these languages. Although all of them are tractable for data complexity, we show that their combined complexity can be arbitrarily high. Furthermore, we prove that all the rule languages with finite chase that extend the weakly acyclic language are of the same expressiveness as the weakly acyclic one, while rule languages with higher combined complexity are in general more succinct than those with lower combined complexity.

Existential rules, i.e. Datalog extended with existential quantifiers in rule heads, are currently studied under a variety of names such as Datalog +/-, ∀∃-rules, and tuple-generating dependencies. The renewed interest in this formalism is fuelled by a wealth of recently discovered language fragments for which query answering is decidable. This paper extends and consolidates two of the main approaches in this field — acyclicity and guardedness — by providing (1) complexity-preserving generalisations of weakly acyclic and weakly (frontier-)guarded rules, and (2) a novel formalism of glut-(frontier-)guarded rules that subsumes both. This builds on an insight that acyclicity can be used to extend any existential rule language while retaining decidability. Besides decidability, combined query complexities are established in all cases.