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

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

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

EMMA -A Formal Basis for Querying Enhanced Multimedia Meta Objects

Abstract. 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 like the retrieval and reuse of content, automatic composition, or adaptation to a user's needs, the multimedia content has to be enriched by additional semantic information, e.g. the semantic interrelationships between single multimedia content items. Enhanced Multimedia Meta Objects (EMMOs) are a novel approach to multimedia content modeling, which combines media, semantic relationships between those media, as well as functionality on the media (such as rendering) into tradeable and versionable knowledge-enriched units of multimedia content. For the processing of EMMOs and the knowledge they incorporate, suitable querying facilities are required. Based on the formal definition of the EMMO model, in this paper, we propose and formally define the EMMO Algebra EMMA, a query algebra that is adequate and complete with regard to the EMMO model. EMMA offers a rich set of orthogonal query operators, which are sufficiently expressive to provide access to all aspects of EMMOs and enable efficient query rewriting and optimization. In addition, they allow for the seamless integration of ontological knowledge within queries, such as supertype/subtype relationships, transitive and inverse associations, etc. Thus, EMMA represents a sound and adequate foundation for the realization of powerful EMMO querying facilities. We have finished the implementation of an EMMO container environment and an EMMA query execution engine, and are currently in the process of evaluating the query algebra in several case studies

*Corresponding author

Abstract: To enable efficient authoring, management and access to multimedia content, media data has to be augmented by semantic metadata and functionality. Semantic representation has to be integrated with domain ontologies to fully exploit domain-specific knowledge. This knowledge can be used within the authoring process and for the efficient management of multimedia content. Also, this knowledge can be used for refining ambiguous user queries by closing the conceptual gap between the user and the information to be retrieved. In our previous research, we have introduced Enhanced Multimedia Metaobjects (EMMOs) as a new approach for semantic multimedia meta modelling, as well as the query algebra EMMA, which is adequate and complete with regard to the EMMO model. This paper illustrates how ontological knowledge can be used within the authoring process of EMMOs, integrated into the EMMO knowledge structures and exploited for refining EMMA queries