Browsing by Division "ICS - Institute of Computer Science"

Modern systems that deal with inference in texts need automatized methods to extract meaning representations (MRs) from texts at scale. Open Information Extraction (IE) is a prominent way of extracting all potential relations from a given text in a comprehensive manner. Previous work in this area has mainly focused on the extraction of isolated relational tuples. Ignoring the cohesive nature of texts where important contextual information is spread across clauses or sentences, state-of-the- art Open IE approaches are thus prone to generating a loose arrangement of tuples that lack the expressiveness needed to infer the true meaning of complex assertions. To overcome this limitation, we present a method that allows existing Open IE systems to enrich their output with additional meta information. By leveraging the semantic hierarchy of minimal propositions generated by the discourse-aware Text Simplification (TS) approach presented in Niklaus et al. (2019), we propose a mechanism to extract semantically typed relational tuples from complex source sentences. Based on this novel type of output, we introduce a lightweight semantic representation for Open IE in the form of normalized and context-preserving relational tuples. It extends the shallow semantic representation of state-of-the-art approaches in the form of predicate-argument structures by capturing intra-sentential rhetorical structures and hierarchical relationships between the relational tuples. In that way, the semantic context of the extracted tuples is preserved, resulting in more informative and coherent predicate-argument structures which are easier to interpret. In addition, in a comparative analysis, we show that the semantic hierarchy of minimal propositions benefits Open IE approaches in a second dimension: the canonical structure of the simplified sentences is easier to process and analyze, and thus facilitates the extraction of relational tuples, resulting in an improved precision (up to 32%) and recall (up to 30%) of the extracted relations on a large benchmark corpus.

Errors involving medication administration can be costly, both in financial and in human terms. Indeed, there is much potential for errors due to the complexity of the medication administration process. Nurses are often singled out as the only responsible of these errors because they are in charge of drug administration. Nevertheless, the interventions of every actor involved in the process and the system design itself contribute to errors (Wakefield et al. (1998) [23]). Proper inpatient medication safety systems can help to reduce such errors in hospitals. In this paper, we review in depth two recent proposals (Chien et al. (2010) [7]; Huang and Ku (2009) [12]) that pursue the aforementioned objective. Unfortunately, they fail in their attempt mainly due to their security faults but interesting ideas can be drawn from both. These security faults refer to impersonation and replay attacks that could produce the generation of a forged proof stating that certain medication was administered to an inpatient when it was not. We propose a leading-edge solution to enhance inpatient medication safety based on RFID technology that overcomes these weaknesses. Our solution, named Inpatient Safety RFID system (IS-RFID), takes into account the Information Technology (IT) infrastructure of a hospital and covers every phase of the drug administration process. From a practical perspective, our system can be easily integrated within hospital IT infrastructures, has a moderate cost, is very ease to use and deals with security aspects as a key point.

In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.

The World Wide Web has evolved drastically over the past decade -- and the proliferation of Web APIs has turned it into the middleware of choice for most distributed systems. The recent focus on hypermedia-driven APIs together with initiatives such as the Web of Things and Linked Data are now promoting and advancing the development of a new generation of dynamic, open, and long-lived systems on the Web. These systems require agent-based solutions to the point that Web researchers have started to build autonomous systems on their own. It is thus both timely and necessary to investigate and align the latest developments in Web research and multi-agent systems (MAS) research. In this paper, we analyze in hindsight the factors that hindered the widespread acceptance of early Web-based MAS. We argue that the answer lies equally in a lack of practical use cases as well as the premature development and alignment of Web and agent technologies. We then present our vision for a new generation of autonomous systems on the Web, which we call hypermedia MAS, together with the research opportunities and challenges they bring.

Recently, work with Distributed Ledger Technologies (DLTs) has focussed on leveraging the decentralised, immutable ledger for use outside of cryptocurrency. One industry poised to benefit from DLTs is the Industrial Internet of Things (IIoT); as the inherent cryptographic mechanisms and alternative trust model make DLTs an attractive solution for distributed networks. Existing DLTs are unsuitable for the IIoT, owing to the large computational and energy requirements for consensus operations and the slow throughput of validated blocks. With limited processing, energy and storage resources and a deadline sensitive operational environment, DLTs in their current state could serve to introduce intolerable latency into IIoT processes and deplete constrained, device resources. Designed for the IIoT context, and based off Delegated Proof of Stake, this work serves to introduce a new consensus mechanism called Delegated Proof of Proximity (DPoP). Using existing location discovery processes, nodes in close proximity to a sensor event are elected as delegates; whose role is to handle consensus and block generation. In using information already known to IIoT devices, DPoP aims to reduce wasted effort, improve throughput by limiting the number of nodes required for consensus operations and improve scalability and flexibility of DLT solutions as the IIoT network continues to grow.

In exploratory process mining, analysts often start with limited knowledge of the log. As they seek to improve their understanding of the log, they develop expectations about what the results might be. Based on these expectations, they then make inquiries and translate them into queries against the log. However, during the analysis, analysts need to evaluate and compare the results of their queries to be able to validate them against their expectations. In this paper, we propose a framework to support process analysts in validating their query results and to enable them to reflect on their analytical process. The framework helps analysts to record their queries and results and allows them to characterize and compare the results obtained with different queries, thereby facilitating the validation process. We implemented the framework as a Python library that can be easily extended and integrated into existing process mining environments. We also demonstrated the usefulness of the framework through an extensive analysis of a real event log.

Buildings contain electro-mechanical systems that ensure the occupants' comfort, health, and safety. The functioning of these systems is automated through control programs, which are often available as reusable artifacts in a software library. However, matching these reusable control programs to the installed technical systems requires manual effort and adds engineering cost. In this article, we show that such matching can be accomplished fully automatically through logical rules and based on the creation of semantic relationships between descriptions of physical processes and descriptions of technical systems and control programs. For this purpose, we propose a high-level bridging ontology that enables the desired rule-based matching and equips digital twins of the technical systems with the required knowledge about the underlying physical processes in a self-contained manner. We evaluated our approach in a real-life building automation project with a total of 34 deployed air handling units. Our data show that rules based on our bridging ontology enabled the system to infer the suitable choice of control programs automatically in more than 90% of the cases while avoiding almost an hour of manual work for each such match.

Satellites allow spatially precise monitoring of the Earth, but provide only limited information on events of societal impact. Subjective societal impact, however, may be quantified at a high frequency by monitoring social media data. In this work, we propose a multi-modal data fusion framework to accurately identify periods of COVID-19-related lockdown in the United Kingdom using satellite observations (NO2 measurements from Sentinel-5P) and social media (textual content of tweets from Twitter) data. We show that the data fusion of the two modalities improves the event detection accuracy on a national level and for large cities such as London.

NO2 is an atmospheric trace gas that contributes to global warming as a precursor of greenhouse gases and has adverse effects on human health. Surface NO2 concentrations are commonly measured through strictly localized networks of air quality stations on the ground. This work presents a novel dataset of surface NO2 measurements aligned with atmospheric column densities from Sentinel-5P, as well as geographic and meteorological variables and lockdown information. The dataset provides access to data from a variety of sources through a common format and will foster data-driven research into the causes and effects of NO2 pollution. We showcase the value of the new dataset on the task of surface NO2 estimation with gradient boosting. The resulting models enable daily estimates and confident identification of EU NO2 exposure limit breaches. Additionally, we investigate the influence of COVID-19 lockdowns on air quality in Europe and find a significant decrease in NO2 levels.

Nowadays, Internet of Things (IoT) systems can be found everywhere. Thereby, the IoT systems' software greatly varies in terms of openness and extensibility. Especially older legacy IoT systems only provide limited means of interacting and extending their functionality due to proprietary and closed software interfaces. Nevertheless, IoT systems evolve constantly based on new functional requirements and the need for integration with new systems and IoT devices. In this work we investigate the use of Business Process Management (BPM) technologies in combination with Microservices to augment IoT systems in a non-invasive, lightweight manner focusing on integration, data augmentations, and human-in-the-loop. We start by analyzing the interfaces and processes of a smart factory as a representative legacy system to identify missing functionality, which we generalize into functional requirements for IoT systems. We show how these requirements can be addressed using BPM and microservices based on a discussion of suitable BPM concepts and software architecture, which is accompanied by a prototypical implementation. These results inform our proposal and discussion of a generic development process to non-invasively augment processoriented legacy IoT systems using BPM and microservices.

Internet of Things (IoT) technologies can be leveraged to monitor and check the conformance of business process executions in the absence of a Business Process Management (BPM) System. This poses the challenge of detecting process activities from lowlevel sensor data by means of event abstraction. Existing methods rely on fully supervised approaches, combining sensor data and process event logs to train classification models. However, frequently these logs are not available, invalidating the applicability of such methods. In this paper, we propose a semi-automated approach to detecting process activities from sensor data based on frequently repeated subsequences and automata. We evaluate the approach with a proof-of-concept implementation and data from a small-scale smart factory. Our evaluation demonstrates the applicability of the proposed approach and its effectiveness in detecting process activities from sensor data to support domain experts with data analysis through automated suggestions.

In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.

Content negotiation aims at enabling a server to provide a client with a representation of a resource that meets its needs. However, client and server might desire to negotiate constraints that go beyond the media type or language of the alternative representation. This is especially true in the Semantic Web, as a resource can be described with a single media type, but with different vocabularies (FOAF, schema.org, etc.), and may match specific patterns. In this paper, we propose an approach to increase the flexibility when negotiating a representation between client and server. Our approach follows the goals of the World Wide Web and uses a set of existing technologies: SHACL and profile-based negotiation. We define the mechanism (in terms of protocol and algorithm) for clients to announce their expectations and for servers to react and respond to them. We then explain, through a use case, how the same approach could be used in Web-based Multi-Agent Systems to help autonomous agents achieve their goals on the Web.