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2017 | Buch

The Semantic Web

14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 – June 1, 2017, Proceedings, Part II

herausgegeben von: Eva Blomqvist, Diana Maynard, Aldo Gangemi, Rinke Hoekstra, Prof. Dr. Pascal Hitzler, Olaf Hartig

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Computer Science

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Über dieses Buch

The two volumes LNCS 10249 and 10250 constitute the refereed proceedings of the 14th International Semantic Web Conference, ESWC 2017, held in Portorož, Slovenia.The 51 revised full papers presented were carefully reviewed and selected from 183 submissions. In addition, 10 PhD papers are included, selected out of 14 submissions. The papers are organized in the following tracks: semantic data management, big data, and scalability; linked data; machine learning; mobile web, sensors, and semantic streams; natural language processing and information retrieval; vocabularies, schemas, and ontologies; reasoning; social web and web science; semantic web and transparency; in use and industrial track; and PhD symposium.

Inhaltsverzeichnis

Frontmatter

In Use and Industrial Track

Frontmatter
Applying Semantic Web Technologies to Assess Maintenance Tasks from Operational Interruptions: A Use-Case at Airbus
Abstract
Airbus, one of the leading Aircraft company in Europe, collects and manages a substantial amount of unstructured data from airlines companies, related to events occurring during the exploitation of an aircraft. Those events are called “Operational Interruptions” (OI) describing observations and the work performed associated by operators in form of short text. At the same time, Airbus maintains a dataset of programmed maintenance task (MPD) for each family of aircraft. Currently, OIs are reported by companies in Excel spreadsheets and experts have to find manually in the OIs the ones that are most likely to match an existing task. In this paper, we describe a semi-automatic approach using semantic technologies to assist the experts of the domain to improve the matching process of OIs with related MPD. Our approach combines text annotation using GATE and a graph matching algorithm. The evaluation of the approach shows the benefits of using semantic technologies to manage unstructured data and future applications for data integration at Airbus.
Ghislain Auguste Atemezing
Modeling Company Risk and Importance in Supply Graphs
Abstract
Managing one’s supply chain is a key task in the operational risk management for any business. Human procurement officers can manage only a limited number of key suppliers directly, yet global companies often have thousands of suppliers part of a wider ecosystem, which makes overall risk exposure hard to track. To this end, we present an industrial graph database application to account for direct and indirect (transitive) supplier risk and importance, based on a weighted set of measures: criticality, replaceability, centrality and distance. We describe an implementation of our graph-based model as an interactive and visual supply chain risk and importance explorer. Using a supply network (comprised of approximately 98, 000 companies and 220, 000 relations) induced from textual data by applying text mining techniques to news stories, we investigate whether our scores may function as a proxy for actual supplier importance, which is generally not known, as supply chain relationships are typically closely guarded trade secrets. To our knowledge, this is the largest-scale graph database and analysis on real supply relations reported to date.
Lucas Carstens, Jochen L. Leidner, Krzysztof Szymanski, Blake Howald
Declarative Data Transformations for Linked Data Generation: The Case of DBpedia
Abstract
Mapping languages allow us to define how Linked Data is generated from raw data, but only if the raw data values can be used as is to form the desired Linked Data. Since complex data transformations remain out of scope for mapping languages, these steps are often implemented as custom solutions, or with systems separate from the mapping process. The former data transformations remain case-specific, often coupled with the mapping, whereas the latter are not reusable across systems. In this paper, we propose an approach where data transformations (i) are defined declaratively and (ii) are aligned with the mapping languages. We employ an alignment of data transformations described using the Function Ontology ( https://static-content.springer.com/image/chp%3A10.1007%2F978-3-319-58451-5_3/449825_1_En_3_IEq1_HTML.gif ) and mapping of data to Linked Data described using the rdf Mapping Language (rml). We validate that our approach can map and transform dbpedia in a declaratively defined and aligned way. Our approach is not case-specific: data transformations are independent of their implementation and thus interoperable, while the functions are decoupled and reusable. This allows developers to improve the generation framework, whilst contributors can focus on the actual Linked Data, as there are no more dependencies, neither between the transformations and the generation framework nor their implementations.
Ben De Meester, Wouter Maroy, Anastasia Dimou, Ruben Verborgh, Erik Mannens
BalOnSe: Temporal Aspects of Dance Movement and Its Ontological Representation
Abstract
In this paper, we propose an approach to describe the temporal aspects of ontological representation of dance movement. By nature, human movement consists of complex combinations of spatiotemporal events, a fact that creates a big challenge for representing, searching, and reasoning about movement-related content, such as movement annotations on video dances. We have defined MoveOnto, a movement ontology whose expressive power captures movements that range from body states and transitions based on the semantics of Labanotation, to generic actions or specialized vocabularies of specific dance genres, e.g., ballet or folk. We combine the ontology description with temporal reasoning in Datalog-MTL, based on temporal rules of the movement events. Finally, we present the specifications and requirements for dance exploration from a user’s perspective and describe the architecture of BalOnSe, a specific system that is currently under implementation on top of MoveOnto according to them. BalOnSe consists of a web-based application with semantic annotation, search, and browsing on the movements, as well as a backend with archival and query processing functionality based on temporal rules.
Katerina El Raheb, Theofilos Mailis, Vladislav Ryzhikov, Nicolas Papapetrou, Yannis Ioannidis
Reasoning on Engineering Knowledge: Applications and Desired Features
Abstract
The development and operation of highly flexible automated systems for discrete manufacturing, which can quickly adapt to changing products, has become a major research field in industrial automation. Adapting a manufacturing system to a new product for instance requires comparing the systems functionality against the requirements imposed by the changed product. With an increasing frequency of product changes, this comparison should be automated. Unfortunately, there is no standard way to model the functionality of a manufacturing system, which is an obstacle to automation. The engineer still has to analyze all documents provided by engineering tools like 3D-CAD data, electrical CAD data or controller code. In order to support this time consuming process, it is necessary to model the so-called skills of a manufacturing system. A skill represents certain features an engineer has to check during the adaption of a manufacturing system, e.g. the kinematic of an assembly or the maximum load for a gripper. Semantic Web Technologies (SWT) provide a feasible solution for modeling and reasoning on the knowledge of these features. This paper provides the results of a project that focused on modeling the kinematic skills of assemblies. The overall approach as well as further requirements are shown. Since not all expectations on reasoning functionality could be met by available reasoners, the paper focuses on desired reasoning features that would support the further use of SWT in the engineering domain.
Constantin Hildebrandt, Matthias Glawe, Andreas W. Müller, Alexander Fay
A Compressed, Inference-Enabled Encoding Scheme for RDF Stream Processing
Abstract
The number of sensors producing data streams at a high velocity keeps increasing. This paper describes an attempt to design an inference-enabled, distributed, fault-tolerant framework targeting RDF streams in the context of an industrial project. Our solution gives a special attention to the latency issue, an important feature in the context of providing reasoning services. Low latency is attained by compressing the scheme and data of processed streams with a dedicated semantic-aware encoding solution. After providing an overview of our architecture, we detail our encoding approach which supports a trade-off between two common inference methods, i.e., materialization and query reformulation. The analysis of results of our prototype emphasize the relevance of our design choices.
Jérémy Lhez, Xiangnan Ren, Badre Belabbess, Olivier Curé
From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards
Abstract
In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.
Henrique Santos, Victor Dantas, Vasco Furtado, Paulo Pinheiro, Deborah L. McGuinness
Ontology-Driven Unified Governance in Software Engineering: The PoolParty Case Study
Abstract
Collaborative software engineering environments have transformed the nature of workflows typically undertaken during the design of software artifacts. However, they do not provide the mechanism needed to integrate software requirements and implementation issues for unified governance in the engineering process. In this paper we present an ontology-driven approach that exploits the Design Intent Ontology (DIO) for aligning requirements specification with the issues raised during software development and software maintenance. Our methodology has been applied in an industrial setting for the PoolParty Thesaurus server. We integrate the requirements specified and issues raised by PoolParty customers and developers, and provide a graph search powered, unified governance dashboard implementation over the annotated and integrated datasets. Our evaluation shows an impressive 50% increase in efficiency when searching over datasets semantically annotated with DIO as compared to searching over Confluence and JIRA.
Monika Solanki, Christian Mader, Helmut Nagy, Margot Mückstein, Mahek Hanfi, Robert David, Andreas Koller
A Hypercat-Enabled Semantic Internet of Things Data Hub
Abstract
An increasing amount of information is generated from the rapidly increasing number of sensor networks and smart devices. A wide variety of sources generate and publish information in different formats, thus highlighting interoperability as one of the key prerequisites for the success of Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we propose a semantic enrichment of the BT Hypercat Data Hub, using well-accepted Semantic Web standards and tools. We propose an ontology that captures the semantics of the imported data and present the BT SPARQL Endpoint by means of a mapping between SPARQL and SQL queries. Furthermore, federated SPARQL queries allow queries over multiple hub-based and external data sources. Finally, we provide two use cases in order to illustrate the advantages afforded by our semantic approach.
Ilias Tachmazidis, Sotiris Batsakis, John Davies, Alistair Duke, Mauro Vallati, Grigoris Antoniou, Sandra Stincic Clarke
ArmaTweet: Detecting Events by Semantic Tweet Analysis
Abstract
Armasuisse Science and Technology, the R&D agency for the Swiss Armed Forces, is developing a Social Media Analysis (SMA) system to help detect events such as natural disasters and terrorist activity by analysing Twitter posts. The system currently supports only keyword search, which cannot identify complex events such as ‘politician dying’ or ‘militia terror act’ since the keywords that correctly identify such events are typically unknown. In this paper we present ArmaTweet, an extension of SMA developed in a collaboration between armasuisse and the Universities of Fribourg and Oxford that supports semantic event detection. Our system extracts a structured representation from the tweets’ text using NLP technology, which it then integrates with DBpedia and WordNet in an RDF knowledge graph. Security analysts can thus describe the events of interest precisely and declaratively using SPARQL queries over the graph. Our experiments show that ArmaTweet can detect many complex events that cannot be detected by keywords alone.
Alberto Tonon, Philippe Cudré-Mauroux, Albert Blarer, Vincent Lenders, Boris Motik
smartAPI: Towards a More Intelligent Network of Web APIs
Abstract
Data science increasingly employs cloud-based Web application programming interfaces (APIs). However, automatically discovering and connecting suitable APIs for a given application is difficult due to the lack of explicit knowledge about the structure and datatypes of Web API inputs and outputs. To address this challenge, we conducted a survey to identify the metadata elements that are crucial to the description of Web APIs and subsequently developed the smartAPI metadata specification and associated tools to capture their domain-related and structural characteristics using the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This paper presents the results of the survey, provides an overview of the smartAPI specification and a reference implementation, and discusses use cases of smartAPI. We show that annotating APIs with smartAPI metadata is straightforward through an extension of the existing Swagger editor. By facilitating the creation of such metadata, we increase the automated interoperability of Web APIs. This work is done as part of the NIH Commons Big Data to Knowledge (BD2K) API Interoperability Working Group.
Amrapali Zaveri, Shima Dastgheib, Chunlei Wu, Trish Whetzel, Ruben Verborgh, Paul Avillach, Gabor Korodi, Raymond Terryn, Kathleen Jagodnik, Pedro Assis, Michel Dumontier

PhD Symposium

Frontmatter
Automating the Dynamic Interactions of Self-governed Components in Distributed Architectures
Abstract
The ongoing digitalization and penetration of the Web into each aspect of software development creates new possibilities and challenges. The flexible reuse of components promises to drastically reduce the implementation and maintenance effort. But growing complexity in terms of variety and dynamic changes bring monolithic approaches to their limits. In this paper, an approach is presented which enables components in distributed systems to observe, judge and independently react to dynamic changes in their neighborhood. Reducing the overall complexity to smaller and easier to manage subproblems leads to more flexible and reliable systems. The target is a delegation of decision making to the single components.
Sebastian R. Bader
Building and Processing a Knowledge-Graph for Legal Data
Abstract
The increasing size and availability of data opens the door for new application areas. Data which has previously been kept separated can be linked and therefore enhanced with additional data from other sources. The linking of data requires a certain data representation such that it can be used in particular domains. In this paper we describe the problem of data representation and search within data exemplified by the legal domain. We propose an approach to represent the legal data (legal norms and court decisions) of Austria and show how this data can be used to build a legal knowledge graph, usable in various applications for lawyers, attorneys, citizens or journalists.
Erwin Filtz
Ontology Matching Algorithms for Data Model Alignment in Big Data
Abstract
Big Data commonly refers to large data with different formats and sources. The problem of managing heterogeneity among varied information resources is increasing. For instance, how to handle variations in meaning or ambiguity in entity representation still remains a challenge. Ontologies can be used to overcome this heterogeneity. However, information cannot be processed across ontologies unless the correspondences among the elements are known. Ontology matching algorithms (systems) are thus needed to find the correspondences (alignments). Many ontology matching algorithms have been proposed in recent literature, but most of them do not consider data instances. The few that do consider data instances still face the big challenge of ensuring high accuracy when dealing with Big Data. This is because existing ontology matching algorithms only consider the problem of handling voluminous data, but do not incorporate techniques to deal with the problem of managing heterogeneity among varied information (i.e., different data formats and data sources). This research aims to develop robust and comprehensive ontology matching algorithms that can find high-quality correspondences between different ontologies while addressing the variety problem associated with Big Data.
Ruth Achiaa Frimpong
Ontology-Based Data Access Mapping Generation Using Data, Schema, Query, and Mapping Knowledge
Abstract
Ontology-Based Data Access systems provide access to non-rdf data using ontologies. These systems require mappings between the non-rdf data and ontologies to facilitate this access. Manually defining such mappings can become a costly process when dealing with large and complex data sources, and/or multiple data sources at the same time. This resulted in different mapping generation tools. While a number of these tools use knowledge from the original data, existing Linked Data, schemas, and/or mappings, they still fall short when dealing with complex challenges and the user effort can be high. In this paper, we propose an approach, together with an evaluation, that discovers and uses extended knowledge from existing (Linked) Data, schemas, query workload, and mappings, and combines it with knowledge provided by the mapping process to generate a new mapping. Our approach aims to improve the mapping quality, while decreasing the task complexity, and subsequently the user effort.
Pieter Heyvaert, Anastasia Dimou, Ruben Verborgh, Erik Mannens
Engaging Librarians in the Process of Interlinking RDF Resources
Abstract
By publishing metadata as RDF and interlinking these resources with other RDF datasets on the Semantic Web, libraries have the potential to expose their collections to a larger audience, increase the use of their materials, and allow for more efficient user searches. Despite these benefits, there are many barriers to libraries fully participating in the Semantic Web. Increasing numbers of libraries are devoting valuable time and resources to publishing RDF datasets, yet little meaningful use is being made of them due to lack of interlinking. The goal of this research is to explore the barriers faced by librarians in participating in the Semantic Web with a particular focus on the process of interlinking. We will also explore how interlinking could be made more engaging for this domain.
Lucy McKenna
A Knowledge-Based Framework for Improving Accessibility of Web Sites
Abstract
Many sites in the World Wide Web are, unfortunately, not accessible or usable for people with impairments despite several existing guidelines. This paper describes an approach for improving the accessibility of web sites using ontologies as the foundation for several tools. The approach is investigated as a PhD thesis as part of other research that uses ontologies to provide disabled or elderly people with assistance for several everyday tasks.
Jens Pelzetter
Iterative Approach for Information Extraction and Ontology Learning from Textual Aviation Safety Reports
Abstract
Textual aviation safety reports are one of the main resources that contain valuable information to understand incidents and accidents in a high-risk industry such as the aviation domain. The reporting process, hence, is essential to provide these reports. Most of the time, the reporting process is done manually, and typically, poorly structured data are provided by the reporters. Automated content analysis for these reports has attracted researchers to extract the required information to perform many tasks, and they used several techniques to achieve it. Ontologies provide formal and explicit specifications of conceptualizations and play a crucial role in the information extraction process. In this paper, we propose a novel iterative ontology-based approach of information extraction and semantic annotations for aviation safety reports and augmenting back the aviation safety ontology with new concepts and relations depending on the terms already annotated in the discovered report model.
Lama Saeeda
Integrative Data Management for Reproducibility of Microscopy Experiments
Abstract
Reproducibility is a fundamental factor in every domain of science since it allows scientists to trust data and results. The scientific community is interested in the results of experiments which are reproducible, reusable and understandable. In this paper, we present our work towards reproducibility of scientific experiments taking into account the use case of microscopy. We aim to analyze the components that are vital for reproducibility and to develop an integrative data management platform for scientific experiments. In this article, we show the use of Semantic Web technologies to conserve an experiment environment and its workflow. This allows scientists to ask queries related to an experiment and compare results. We present our approach for scientists to represent, search and share their experimental data and results to the scientific community for better data interoperability and reuse. Our overall goal is to extend data management and Semantic Web technologies to enable reproducibility.
Sheeba Samuel
Towards an Open Extensible Framework for Empirical Benchmarking of Data Management Solutions: LITMUS
Abstract
Developments in the context of Open, Big, and Linked Data have led to an enormous growth of structured data on the Web. To keep up with the pace of efficient consumption and management of the data at this rate, many Data Management Solutions There exists many efforts for benchmarking these domain specific DMSs, however, (i) reproducing these third party benchmarks is an extremely tedious task, and (ii) there is a lack of a common framework which enables and advocates the extensibility and re-usability of the benchmarks. We propose LITMUS, one such framework for benchmarking data management solutions. LITMUS will go beyond classical storage benchmarking frameworks by allowing for analysing the performance of DMSs across query languages. In this early stage doctoral work, we present the LITMUS concept as well as the considerations that led to its preliminary architecture, and progress reported so far in its realisation.
Harsh Thakkar
Enhancing White-Box Machine Learning Processes by Incorporating Semantic Background Knowledge
Abstract
Currently, most of white-box machine learning techniques are purely data-driven and ignore prior background and expert knowledge. A lot of this knowledge has already been captured in domain models, i.e. ontologies, using Semantic Web technologies. The goal of this research proposal is to enhance the predictive performance and required training time of white-box models by incorporating the vast amount of available knowledge in the pre-processing, feature extraction and selection phase of a machine learning process.
Gilles Vandewiele
Backmatter
Metadaten
Titel
The Semantic Web
herausgegeben von
Eva Blomqvist
Diana Maynard
Aldo Gangemi
Rinke Hoekstra
Prof. Dr. Pascal Hitzler
Olaf Hartig
Copyright-Jahr
2017
Electronic ISBN
978-3-319-58451-5
Print ISBN
978-3-319-58450-8
DOI
https://doi.org/10.1007/978-3-319-58451-5

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