References of "Kolbe, Niklas 50025852"
     in
Bookmark and Share    
Full Text
See detailA Formal Approach to Ontology Recommendation for Enhanced Interoperability in Open IoT Ecosystems
Kolbe, Niklas UL

Doctoral thesis (2020)

The vision of the Internet of Things (IoT) promises novel, intelligent applications to improve services across all industries and domains. Efficient data and service discovery are crucial to unfold the ... [more ▼]

The vision of the Internet of Things (IoT) promises novel, intelligent applications to improve services across all industries and domains. Efficient data and service discovery are crucial to unfold the potential value of cross-domain IoT applications. Today, the Web is the primary enabler for integrating data from distributed networks, with more and more sensors and IoT gateways connected to the Web. However, semantic data models, standards and vocabularies used by IoT vendors and service providers are highly heterogeneous, which makes data discovery and integration a challenging task. Industrial and academic research initiatives increasingly rely on Semantic Web technologies to tackle this challenge. Ongoing research efforts emphasize the development of formal ontologies for the description of Things, sensor networks, IoT services and domain-dependent observations to annotate and link data on the Web. Within this context, there is a research gap in investigating and proposing ontology recommendation approaches that foster the reuse of most suitable ontologies relevant to semantically annotate IoT data sources. Improved ontology reuse in the IoT enhances semantic interoperability and thus facilitates the development of more intelligent and context-aware systems. In this dissertation, we show that ontology recommendation can form a key building block to achieve this consensus in the IoT. In particular, we consider large-scale IoT systems, also referred to as IoT ecosystems, in which a wide range of stakeholders and service providers have to cooperate. In such ecosystems, semantic interoperability can only be efficiently achieved when a high degree of consensus on relevant ontologies among data providers and consumers exists. This dissertation includes the following contributions. First, we conceptualize the task of ontology recommendation and evaluate existing approaches with regard to IoT ecosystem requirements. We identify several limitations in ontology recommendation, especially concerning the IoT, which motivates the main focus on ontology ranking in this dissertation. Second, we subsequently propose a novel approach to ontology ranking that offers a fairer scoring of ontologies if their popularity is unknown and thus helps in providing a better recommendation in the current state of the IoT. We employ a `learning to rank' approach to show that qualitative ranking features can improve the ranking performance and potentially substitute an explicit popularity feature. Third, we propose a novel ontology ranking evaluation benchmark to address the lack of comparison studies for ontology ranking approaches as a general issue in the Semantic Web. We develop a large, representative evaluation dataset that we derive from the collected user click logs of the Linked Open Vocabularies (LOV) platform. It is the first dataset of its kind that is capable of comparing learned ontology ranking models as proposed in the literature under real-world constraints. Fourth, we present an IoT ecosystem application to support data providers in semantically annotating IoT data streams with integrated ontology term recommendation and perform an evaluation based on a smart parking use case. In summary, this dissertation presents the advancements of the state-of-the-art in the design of ontology recommendation and its role for establishing and maintaining semantic interoperability in highly heterogeneous and evolving ecosystems of inter-related IoT services. Our experiments show that ontology ranking features that are well designed with regard to the underlying ontology collection and respective user behavior can significantly improve the ranking quality and, thus, the overall recommendation capabilities of related tools. [less ▲]

Detailed reference viewed: 162 (5 UL)
Full Text
Peer Reviewed
See detailLOVBench: Ontology Ranking Benchmark
Kolbe, Niklas UL; Vandenbussche, Pierre-Yves; Kubler, Sylvain UL et al

in Proceedings of The Web Conference 2020 (WWW '20) (2020, April)

Detailed reference viewed: 39 (9 UL)
Full Text
Peer Reviewed
See detailLinked Vocabulary Recommendation Tools for Internet of Things: A Survey
Kolbe, Niklas UL; Kubler, Sylvain UL; Robert, Jérémy UL et al

in ACM Computing Surveys (2019)

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published ... [more ▼]

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap, by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both, empirically and experimentally. Some of the key ndings include that (i) many tools neglect to thoroughly address both, the curation of a vocabulary collection and e ective selection mechanisms; (ii) modern information retrieval techniques are underrepresented; and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet su ciently extended to t today’s IoT projects. [less ▲]

Detailed reference viewed: 138 (10 UL)
Full Text
Peer Reviewed
See detailPopularity-driven Ontology Ranking using Qualitative Features
Kolbe, Niklas UL; Kubler, Sylvain UL; Le Traon, Yves UL

in The Semantic Web - ISWC 2019 (2019)

Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ... [more ▼]

Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies' occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies' popularity, resulting in ineffective ranking scores. This motivated us to investigate - based on the problematic WoT case - whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies' popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model. [less ▲]

Detailed reference viewed: 77 (1 UL)
Full Text
Peer Reviewed
See detailPROFICIENT: Productivity Tool for Semantic Interoperability in an Open IoT Ecosystem
Kolbe, Niklas UL; Robert, Jérémy UL; Kubler, Sylvain et al

in Proceedings of the 14th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (2017, November 07)

The Internet of Things (IoT) is promising to open up opportunities for businesses to offer new services to uncover untapped needs. However, before taking advantage of such opportunities, there are still ... [more ▼]

The Internet of Things (IoT) is promising to open up opportunities for businesses to offer new services to uncover untapped needs. However, before taking advantage of such opportunities, there are still challenges ahead, one of which is the development of strategies to abstract from the heterogeneity of APIs that shape today's IoT. It is becoming increasingly complex for developers and smart connected objects to efficiently discover, parse, aggregate and process data from disparate information systems, as different protocols, data models, and serializations for APIs exist on the market. Standards play an indisputable role in reducing such a complexity, but will not solve all problems related to interoperability. For example, it will remain a permanent need to help and guide data/service providers to efficiently describe the data/services they would like to expose to the IoT. This paper presents PROFICIENT, a productivity tool that fulfills this need, which is showcased and evaluated considering recent open messaging standards and a smart parking scenario. [less ▲]

Detailed reference viewed: 213 (18 UL)
Full Text
Peer Reviewed
See detailEnriching a Situation Awareness Framework for IoT with Knowledge Base and Reasoning Components
Kolbe, Niklas UL; Zaslavsky, Arkady; Kubler, Sylvain UL et al

in Modeling and Using Context (2017, July)

Theimportanceofsystem-levelcontext-andsituationaware- ness increases with the growth of the Internet of Things (IoT). This paper proposes an integrated approach to situation awareness by providing a ... [more ▼]

Theimportanceofsystem-levelcontext-andsituationaware- ness increases with the growth of the Internet of Things (IoT). This paper proposes an integrated approach to situation awareness by providing a semantically rich situation model together with reliable situation infer- ence based on Context Spaces Theory (CST) and Situation Theory (ST). The paper discusses benefits of integrating the proposed situation aware- ness framework with knowledge base and efficient reasoning techniques taking into account uncertainty and incomplete knowledge about situa- tions. The paper discusses advantages and impact of proposed context adaptation in dynamic IoT environments. Practical issues of two-way mapping between IoT messaging standards and CST are also discussed. [less ▲]

Detailed reference viewed: 141 (4 UL)
Full Text
Peer Reviewed
See detailTowards Semantic Interoperability in an Open IoT Ecosystem for Connected Vehicle Services
Kolbe, Niklas UL; Kubler, Sylvain UL; Robert, Jérémy UL et al

in 2017 IEEE Global Internet of Things Summit (GIoTS) Proceedings (2017, July)

A present challenge in today’s Internet of Things (IoT) ecosystem is to enable interoperability across hetero- geneous systems and service providers. Restricted access to data sources and services limits ... [more ▼]

A present challenge in today’s Internet of Things (IoT) ecosystem is to enable interoperability across hetero- geneous systems and service providers. Restricted access to data sources and services limits the capabilities of a smart city to improve social, environmental and economic aspects. Interoperability in the IoT is concerned with both, messaging interfaces and semantic understanding of heterogeneous data. In this paper, the first building blocks of an emerging open IoT ecosystem developed at the EU level are presented. Se- mantic web technologies are applied to the existing messaging components to support and improve semantic interoperability. The approach is demonstrated with a proof-of-concept for connected vehicle services in a smart city setting. [less ▲]

Detailed reference viewed: 241 (11 UL)
Full Text
Peer Reviewed
See detailReasoning over Knowledge-based Generation of Situations in Context Spaces to Reduce Food Waste
Kolbe, Niklas UL; Zaslavsky, Arkady; Kubler, Sylvain UL et al

in Reasoning over Knowledge-based Generation of Situations in Context Spaces to Reduce Food Waste (2016, September 28)

Abstract. Situation awareness is a key feature of pervasive computing and requires external knowledge to interpret data. Ontology-based reasoning approaches allow for the reuse of predefined knowledge ... [more ▼]

Abstract. Situation awareness is a key feature of pervasive computing and requires external knowledge to interpret data. Ontology-based reasoning approaches allow for the reuse of predefined knowledge, but do not provide the best reasoning capabilities. To overcome this problem, a hybrid model for situation awareness is developed and presented in this paper, which integrates the Situation Theory Ontology into Context Space Theory for inference. Furthermore, in an effort to rely as much as possible on open IoT messaging standards, a domain-independent framework using the O-MI/O-DF standards for sensor data acquisition is developed. This framework is applied to a smart neighborhood use case to reduce food waste at the consumption stage. [less ▲]

Detailed reference viewed: 118 (2 UL)