Marta Sabou

TU Wien | TU Wien · Institute of Software Technology and Interactive Systems

Artificial Intelligence (AI) Auditability is a core requirement for achieving responsible AI system design. However, it is not yet a prominent design feature in current applications. Existing AI auditing tools typically lack integration features and remain as isolated approaches. This results in manual, high-effort, and mostly one-off AI audits, ne...

The success of artificial intelligence (AI) applications is heavily dependant on the quality of data they rely on. Thus, data curation, dealing with cleaning, organising and managing data, has become a significant research area to be addressed. Increasingly, semantic data structures such as ontologies and knowledge graphs empower the new generation...

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic techniques (a.k.a. neuro-symbolic systems), a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community – Semantic Web Machine Lear...

Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards...

The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web community with an increased interest in the development of systems that rely on both Semantic Web resources and Machine Learning components (SWeMLS, for short). However, understanding trends and best practices in this rapidly growing field is hampered by a lack...

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short). Due to i...

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community – Semantic Web Machine Learning (SWeML for short). Due to i...

Knowledge graphs have been recognized in manufacturing as a suitable technology for integration of multidisciplinary knowledge from heterogeneous data sources. The effective reuse of this knowledge can better inform stakeholders in their decision making processes and consequently, establish a competitive advantage. In contrast to the utilization of...

As ontologies enable advanced intelligent applications, ensuring their correctness is crucial. While many quality aspects can be automatically verified, some evaluation tasks can only be solved with human intervention. Nevertheless, there is currently no generic methodology or tool support available for human-centric evaluation of ontologies. This...

Semantic Web Machine Learning Systems (SWeMLS) characterise applications, which combine symbolic and subsymbolic components in innovative ways. Such hybrid systems are expected to benefit from both domains and reach new performance levels for complex tasks. While existing taxonomies in this field focus on building blocks and patterns for describing...

Both symbolic and subsymbolic AI research have seen a recent surge driven by innovative approaches, such as neural networks and knowledge graphs. Further opportunities lie in the combined use of these two paradigms in ways that benefit from their complementary strengths. Accordingly, there is much research at the confluence of these two research ar...

The verification of Extended Entity Relationship (EER) diagrams and other conceptual models that capture the design of information systems is crucial to ensure reliable systems. To scale up verification processes to larger groups of experts, Human Computation techniques were used focusing primarily on closed tasks, which constrain the number and va...

Empirical software engineering (ESE) focuses on gathering evidence through measurements and experiments involving humans and software systems (software products, processes, and resources). While empirical studies often include considerable human effort for study planning, execution, and data analysis, human computation (HC) methods, such as crowdso...

Smart city infrastructures such as transportation and energy networks are evolving into so-called cyber physical social systems (CPSSs), which collect and leverage citizens’ data in order to adapt services to citizens’ needs. The privacy implications of such systems are, however, significant and need to be addressed. Current systems either try to e...

The success of crowdsourcing based annotation of text corpora depends on ensuring that crowdworkers are sufficiently well-trained to perform the annotation task accurately. To that end, a frequent approach to train annotators is to provide instructions and a few example cases that demonstrate how the task should be performed (referred to as the CON...

Conceptual domain models, such as taxonomies, knowledge graphs or Extended Entity Relationship (EER) diagrams are core to all information systems. The task of verifying the correctness of these models is of high interest to the knowledge and software engineering communities and attracted the first solution approaches using human computation. Yet, s...

When reusing software architectural knowledge, such as design patterns or design decisions, software architects need support for exploring architectural knowledge collections, e.g., for finding related items. While semantic-based architectural knowledge management tools are limited to supporting lookup-based tasks through faceted search and fall sh...

[Context] Models play an important role in Software and Systems Engineering processes. Reviews are well-established methods for model quality assurance that support early and efficient defect detection. However, traditional document-based review processes have limitations with respect to the number of experts, resources, and the document size that...

This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did thi...

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up app...

When reusing software architectural knowledge, such as design patterns or design decisions, software architects need support for exploring architectural knowledge collections, e.g., for finding related items. While semantic-based architectural knowledge management tools are limited to supporting lookup-based tasks through faceted search and fall sh...

Paper 'The SPAR Ontologies' is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

The two-volume set LNCS 11136 and 11137 constitutes the refereed proceedings of the 17th International Semantic Web Conference, ISWC 2018, held in Monterey, USA, in October 2018. The ISWC conference is the premier international forum for the Semantic Web / Linked Data Community. The total of 62 full papers included in this volume was selected from...

Today’s industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yi...

The application of best-practice software inspection processes for early defect detection requires considerable human effort. Crowdsourcing approaches can support inspection activities (a) by distributing inspection effort among a group of human experts and (b) by increasing inspection control. Thus, the application of crowdsourcing techniques aims...

This editorial introduces the special issue based on the best papers from ESWC 2015. And since ESWC’15 marked 15 years of Semantic Web research, we extended this editorial to a position paper that reflects the path that we, as a community, traveled so far with the goal of transforming the Web of Pages to a Web of Resources. We discuss some of the k...

[Context] Models play an important role in Software and Systems Engineering processes. Reviews are well-established methods for model quality assurance that support early and efficient defect detection. However, traditional document-based review processes have limitations with respect to the number of experts, resources, and the document size that...

Traditional Software Inspection is a well-established approach to identify defects in software artifacts and models early and efficiently. However, insufficient method and tool support hinder efficient defect detection in large software models. Recent Human Computation and Crowdsourcing processes may help to overcome this limitation by splitting co...

A key requirement in supporting the work of engineers involved in the design of Cyber-Physical Production Systems (CPPS) is offering tools that can deal with engineering data produced across the various involved engineering disciplines. Such data is created by different discipline-specific tools and is represented in tool-specific data models. Ther...

The timely discovery, sharing and integration of architectural knowledge (AK) have become critical aspects in enabling the software architects to make meaningful conceptual and technical design decisions and trade-offs. In large-scale organizations particular obstacles in making AK available to architects are a heterogeneous pool of internal and ex...

Ein wesentlicher Aspekt für die Umsetzung der Vision von Industrie 4.0 ist die Verbesserung des Engineering-Prozesses von Produktionssystemen . Dieses Kapitel untersucht, welche Beiträge Semantic Web Technologien zu Engineering-Prozessen von Industrie 4.0 einbringen können. Dazu wird ein Analyse-Framework entwickelt, in dem die hauptsächlichen Fähi...

In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domai...

This chapter summarizes and reflects on the material presented in this book. In particular, the chapter aims to conclude on the relation between Industrie 4.0 needs and Semantic Web technologies (SWTs) based on the various intelligent engineering applications (IEAs) reported in Parts III and IV of the book. Concretely, this chapter seeks answers to...

The process of engineering cyber-physical production systems (CPPS) relies on the collaborative work of multiple and diverse teams of engineers who need to exchange and synchronize data created by their domain-specific tools. Building applications that support the CPPS engineering process requires technologies that enable integrating and making sen...

Ontologies are key Semantic Web technologies (SWTs) that provide means to formally and explicitly represent domain knowledge in terms of key domain concepts and their relations. Therefore, the creation of intelligent engineering applications (IEAs) that rely on SWTs depends on the creation of a suitable ontology that semantically models engineering...

The process of designing Cyber Physical Production Systems (CPPS), e.g., modern power plants or steel mills, typically takes place in a multi-disciplinary engineering environment, in which experts from various engineering domains and organizations work together towards creating complex engineering artifacts. The process of designing such complex en...

The Industrie 4.0 vision highlights the need for more flexible and adaptable production systems. This requires making the process of engineering production systems faster and intends to lead to higher quality, but also more complex plants. A key issue in improving engineering processes in this direction is providing mechanisms that can efficiently...

Crowdsourcing techniques provide effective means for solving a variety of ontology engineering problems. Yet, they are mainly used as external support to ontology engineering, without being closely integrated into the work of ontology engineers. In this paper we investigate how to closely integrate crowdsourcing into ontology engineering practices....

The engineering phase of Cyber-Physical Production Systems (CPPS) is a multi-disciplinary process in which representatives of diverse engineering disciplines collaborate to deliver a complex CPPS. To ensure optimal project management as well as to avoid risks of inconsistencies between engineering models created by engineers from different discipli...

The complexity of the social, political and economical settings in which tourism enterprises operate, increasingly require them to perform data analytics tasks that rely on data from various domains (e.g., economy, environmental sustainability). A survey of tourism practitioners performed in this study showed that although such cross-domain analyti...

This chapter introduces the context and aims of this book. In addition, it provides a detailed description of industrial production systems including their life cycle, stakeholders, and data integration challenges. It also includes an analysis of the types of intelligent engineering applications that are needed to support flexible production in lin...

This is the first book to explore how Semantic Web technologies (SWTs) can be used to create intelligent engineering applications (IEAs). Technology-specific chapters reflect the state of the art in relevant SWTs and offer guidelines on how they can be applied in multi-disciplinary engineering settings characteristic of engineering production syste...

The two-volume set LNCS 9981 and 9982 constitutes the refereed proceedings of the 15th International Semantic Web Conference, ISWC 2016, which was held in Kobe, Japan, in October 2016. The 75 full papers presented in these proceedings were carefully reviewed and selected from 326 submissions. The International Semantic Web Conference is the premier...

The two-volume set LNCS 9981 and 9982 constitutes the refereed proceedings of the 15th International Semantic Web Conference, ISWC 2016, which was held in Kobe, Japan, in October 2016. The 75 full papers presented in these proceedings were carefully reviewed and selected from 326 submissions. The International Semantic Web Conference is the premier...

Ein wesentlicher Aspekt für die Umsetzung der Vision von Industrie 4.0 ist die Verbesserung des Engineering-Prozesses von Produktionssystemen . Dieses Kapitel untersucht, welche Beiträge Semantic Web Technologien zu Engineering-Prozessen von Industrie 4.0 einbringen können. Dazu wird ein Analyse-Framework entwickelt, in dem die hauptsächlichen Fähi...

Multi-Disciplinary Engineering (MDEng) environments involve a wide range of models, processes and tools that were not designed to cooperate together. The Ontology-Based Information Integra-tion (OBII) approach has been proposed to address the integration issue within such environments. However, knowledge changes management and analysis (KCMA) proce...

Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exc...

Complementing studies on the economic impact of Open Government Data (OGD), we investigate how this novel Web-enabled movement supports sustainability. An analysis of OGD-based applications reveals that: (1) OGD supports all three pillars of sustainability; (2) citizens and app developers alike are receptive and motivated by sustainability implicat...

Complementary to managing bibliographic information as done by digital libraries, the management of concrete research objects (e.g., experimental workflows, design patterns) is a pre-requisite to foster collaboration and reuse of research results. In this paper we describe the case of the Empirical Software Engineering domain, where researchers use...

In today’s global economy, tourism managers need to consider a range of factors when making important decisions. Besides traditional tourism indicators (such as arrivals or bednights) they also need to take into account indicators from other domains, for example, economy and sustainability. From a technology perspective, building decision support s...

In today’s global economy, tourism managers need to consider a range of factors when making important decisions. Besides traditional tourism indicators (such as arrivals or bednights) they also need to take into account indicators from other domains, for example, economy and sustainability. From a technology perspective, building decision support s...

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approach...

The complexity of the socio-, political- and economical settings in which tourism enterprises operate, increasingly require them to make decisions that take into account data from various domains (e.g., economy, environmental sustainability). Based on a practitioners' survey that we performed, we conclude that although such cross-domain decisions a...

This book constitutes the refereed proceedings of the 12th Extended Semantic Web Conference, ESWC 2014, held in Anissaras, Portoroz, Slovenia, in May/June 2015. The 43 revised full papers presented together with three invited talks were carefully reviewed and selected from 164 submissions. This program was completed by a demonstration and poster se...

Multi-disciplinary engineering (ME) projects are conducted in complex heterogeneous environments, where participants, originating from different disciplines, e.g., mechanical, electrical, and software engineering, collaborate to satisfy project and product quality as well as time constraints. Detecting defects across discipline boundaries early and...

Crowdsourcing techniques have been shown to provide effective means for solving a variety of ontology engineering problems. Yet, they are mainly being used as external means to ontology engineering, without being closely integrated into the work of ontology engineers. In this paper we investigate how to closely integrate crowdsourcing into ontology...

Semantic Web and Linked Data are widely considered as effective and powerful technologies for integrating heterogeneous data models and data sources. However, there is still a gap between promising research results and prototypes and their practical acceptance in industry contexts. In context of our industry partners we observed a lack of tool-supp...

This chapter presents the Media Watch on Climate Change, a publicly available Web intelligence portal that collects, aggregates and visualizes large archives of digital content from multiple stakeholder groups (documents and user comments from news media, blogs, user-generated content from Facebook, Twitter and YouTube, corporate and NGO Web sites,...

Effective and efficient information sharing for reuse and analysis of scientific data from published research papers is an important challenge for researchers working within the empirical software engineering (EMSE) domain. Currently, there is only limited support for storing empirical research data and results in a way that is easy to access and r...

The validation of ontologies using domain experts is expensive. Crowdsourcing has been shown a viable alternative for many knowledge acquisition tasks. We present a Protégé plugin and a workflow for outsourcing a number of ontology validation tasks to Games with a Purpose and paid micro-task crowdsourcing.

Mechanised labour and games with a purpose are the two most popular human computation genres, frequently employed to support research activities in fields as diverse as natural language processing, semantic web or databases. Research projects typically rely on either one or the other of these genres, and therefore there is a general lack of underst...

This volume contains the proceedings of the I-SEMANTICS 2013. I-SEMANTICS offers a forum for the exchange of latest scientific results in semantic systems and complements these topics with new research challenges in the area of social software, semantic content engineering, logic programming and Semantic Web technologies. The conference is in its 9...

This volume contains the proceedings of the I-SEMANTICS 2013. I-SEMANTICS offers a forum for the exchange of latest scientific results in semantic systems and complements these topics with new research challenges in the area of social software, semantic content engineering, logic programming and Semantic Web technologies. The conference is in its 9...