Towards Context Driven Modularization of Large Biomedical Ontologies

Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently occurring concepts in the domain corpus define the application context and can therefore potentially yield the relevant ontology modules. We illustrate our approach on an example case that involves a large ontology on human anatomy and report on our first manual experiments

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiolog

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology

AI Hazard Management: A framework for the systematic management of root causes for AI risks

Recent advancements in the field of Artificial Intelligence (AI) establish the basis to address challenging tasks. However, with the integration of AI, new risks arise. Therefore, to benefit from its advantages, it is essential to adequately handle the risks associated with AI. Existing risk management processes in related fields, such as software systems, need to sufficiently consider the specifics of AI. A key challenge is to systematically and transparently identify and address AI risks' root causes - also called AI hazards. This paper introduces the AI Hazard Management (AIHM) framework, which provides a structured process to systematically identify, assess, and treat AI hazards. The proposed process is conducted in parallel with the development to ensure that any AI hazard is captured at the earliest possible stage of the AI system's life cycle. In addition, to ensure the AI system's auditability, the proposed framework systematically documents evidence that the potential impact of identified AI hazards could be reduced to a tolerable level. The framework builds upon an AI hazard list from a comprehensive state-of-the-art analysis. Also, we provide a taxonomy that supports the optimal treatment of the identified AI hazards. Additionally, we illustrate how the AIHM framework can increase the overall quality of a power grid AI use case by systematically reducing the impact of identified hazards to an acceptable level

Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications

Manufacturers often encounter challenges when implementing artificial intelligence (AI) in their manufacturing operations. Similar challenges with other digital transformation technologies have resulted in the emergence of innovation ecosystems. In this paper, we aim to demonstrate the emergence of AI innovation ecosystems and highlight the factors that influence their structure in manufacturing. To achieve this, we conducted a qualitative study of ten manufacturing case studies, analyzing different value propositions, activities, actors, and modules in AI ecosystems in the manufacturing sector. We first visualize the AI innovation ecosystems to showcase their structure and then discuss factors such as trustworthiness, scalability, simulation, and cloud that impact the ecosystem structure. Our study provides practitioners with a better understanding of the structure of AI ecosystems and their influencing factors. For researchers, we introduce influencing factors as a new part of the ecosystem-as-structure concept, which can lead to new research opportunities

Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods

Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amounts of data generated by monitoring tools as well as more and more sophisticated attacks, these ML methods are gaining traction. Knowledge graphs and their corresponding learning techniques such as Graph Neural Networks (GNNs) with their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies, are finding application in the cybersecurity domain. However, similar to other connectionist models, GNNs are lacking transparency in their decision making. This is especially important as there tend to be a high number of false positive alerts in the cybersecurity domain, such that triage needs to be done by domain experts, requiring a lot of man power. Therefore, we are addressing Explainable AI (XAI) for GNNs to enhance trust management by exploring combining symbolic and sub-symbolic methods in the area of cybersecurity that incorporate domain knowledge. We experimented with this approach by generating explanations in an industrial demonstrator system. The proposed method is shown to produce intuitive explanations for alerts for a diverse range of scenarios. Not only do the explanations provide deeper insights into the alerts, but they also lead to a reduction of false positive alerts by 66% and by 93% when including the fidelity metric.Comment: arXiv admin note: text overlap with arXiv:2105.0874

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

A query algebra for ontology-enhanced management of multimedia meta objects

Zsfassung in dt. SpracheExistierende Beschreibungsformate für multimediale Inhalte kodieren in erster Linie die Präsentation des Inhalts, vernachlässigen dabei aber die Informationen, welche der Inhalt vermittelt. Diese präsentationsorientierte Beschreibung multimedialer Inhalte ermöglicht allerdings nur die inflexible, statische Darstellung multimedialer Inhalte. Für die Realisierung anspruchsvollerer Operationen müssen die multimedialen Inhalte mit zusätzlichem semantischem Wissen angereichert werden.Als Grundlage für die semantische Modellierung multimedialer Inhalte in verteilten, kollaborativen Anwendungen wurden im Rahmen dieser Dissertation Enhanced Multimedia Meta Objects (EMMOs) entwickelt. Ein EMMO stellt eine abgeschlossene Einheit multimedialen Inhalts dar, die drei Aspekte des Inhalts untrennbar vereinigt. Der Medienaspekt reflektiert, dass ein EMMO eine Aggregation der Basismedienobjekte darstellt, aus denen der multimediale Inhalt besteht, der semantische Aspekt ermöglicht die Beschreibung von semantischen Assoziationen zwischen den Medienobjekten des EMMOs und der funktionale Aspekt erlaubt es einem EMMO, beliebige, anwendungsspezifische Funktionen zu spezifieren, die von externen Applikationen aufgerufen werden können. Zusätzlich dazu sind EMMOs versionierbar und transferierbar.Um einen effizienten Zugriff auf EMMOs zu ermöglichen, wurde in der vorliegenden Arbeit die Abfragealgebra EMMA entwickelt, welche adäquat und vollständig in Bezug auf das EMMO-Modell ist. Indem einfache und orthogonale Operatoren zur Verfügung gestellt werden, die für die Formulierung komplexerer Abfragen kombiniert werden können, wird die Voraussetzung für effiziente Abfrageoptimierung in EMMA geschaffen. Sowohl das EMMO-Modell als auch die EMMA-Algebra stellen eine solide Basis für die Integration von ontologischem Wissen dar. Im Rahmen dieser Dissertation wird gezeigt, wie man ontologisches Wissen während der Entwicklung von EMMOs einsetzen, in EMMO-Wissensstrukturen integrieren und für die Verfeinerung von EMMA-Abfragen auswerten kann.Today's multimedia content formats primarily encode the presentation of content but not the information the content conveys.However, this presentation-oriented modeling only permits the inflexible, hard-wired presentation of multimedia content. For the realization of advanced operations the multimedia content has to be enriched by additional semantic information. To provide a basis for the semantic modeling of multimedia content in content sharing and collaborative applications, we have developed Enhanced Multimedia Meta Objects (EMMOs). An EMMO constitutes a self-contained piece of multimedia content that indivisibly unites three of the content's aspects. The media aspect reflects that an EMMO aggregates the basic media objects of which the multimedia content consists, the semantic aspect allows the specification of semantic associations between an EMMO's media objects, and, finally, the functional aspect provides means for the definition of arbitrary, domain-specific operations on the content that can be invoked by applications. Furthermore, EMMOs are versionable and tradeable.To enable the efficient retrieval of EMMOs, we have developed the query algebra EMMA, which is adequate and complete with regard to the EMMO model. By providing simple and orthogonal operators, which can be combined to formulate more complex queries, EMMA enables efficient query optimization. Both, the EMMO model and the EMMA algebra, provide a sound basis for the integration of ontological knowledge. We demonstrate how ontological knowledge can be used within the authoring process of EMMOs, can be integrated within the EMMO knowledge structures, and can be exploited for refining EMMA queries.12