Supporting Semantic Data Enrichment at Scale

Ciavotta, Michele; Cutrona, Vincenzo; Paoli, Francesco; Nikolov, Nikolay; Palmonari, Matteo; Roman, Dumitru

doi:10.1007/978-3-030-78307-5_2

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…In [ 34 ], the authors make a valuable contribution to the landscape through the introduction of a novel semantic virtualisation approach, which focuses on cross-domain IoT platform sensors and places emphasis on generating interoperable data acquisition plans. Ciavotta et al [ 35 ] present a pipeline that prioritises performance-centric data enrichment and demonstrates its effectiveness in managing extensive datasets and large-scale studies.…”

Section: Related Workmentioning

confidence: 99%

Reshaping Smart Cities through NGSI-LD Enrichment

González,

Martín,

Santana

et al. 2024

Sensors

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The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems.

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Section: Related Workmentioning

confidence: 99%

Reshaping Smart Cities through NGSI-LD Enrichment

González,

Martín,

Santana

et al. 2024

Sensors

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“…This pattern indicates that there are triples with the predicate publisher in the data set, that have Book as the most specific type among the types of the subject and Publisher as the most specific type among the types of the object. 15 Finally, the semantic profile also includes several statistics. In the following we provide an overview of the statistics produced by ABSTAT considering the highlighted pattern in Fig.…”

Section: Abstat Profilesmentioning

confidence: 99%

“…Such patterns might be considered as "views" that allow to speed up knowledge discovery. These profiles have been proved to support different downstream tasks [15,21,23,58,64].…”

Section: Introductionmentioning

confidence: 96%

Understanding the structure of knowledge graphs with ABSTAT profiles

Spahiu

Palmonari

Principe

et al. 2023

Self Cite

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While there has been a trend in the last decades for publishing large-scale and highly-interconnected Knowledge Graphs (KGs), their users often get overwhelmed by the task of understanding their content as a result of their size and complexity. Data profiling approaches have been proposed to summarize large KGs into concise and meaningful representations, so that they can be better explored, processed, and managed. Profiles based on schema patterns represent each triple in a KG with its schema-level counterpart, thus covering the entire KG with profiles of considerable size. In this paper, we provide empirical evidence that profiles based on schema patterns, if explored with suitable mechanisms, can be useful to help users understand the content of big and complex KGs. ABSTAT provides concise pattern-based profiles and comes with faceted interfaces for profile exploration. Using this tool we present a user study based on query completion tasks. We demonstrate that users who look at ABSTAT profiles formulate their queries better and faster than users browsing the ontology of the KGs. The latter is a pretty strong baseline considering that many KGs do not even come with a specific ontology to be explored by the users. To the best of our knowledge, this is the first attempt to investigate the impact of profiling techniques on tasks related to knowledge graph understanding with a user study.

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“…The purpose of this process is to improve the utility of the data, making it more easily understandable and more effectively processed and analyzed. Semantic enrichment finds application across numerous types of data and contexts, for example social media [5], [6], images [7], [8], databases [9], simulations [10], data preparation in data science processes [11], mobility (which we will delve into more deeply later), and more. Despite the significant differences in the types of data and objectives across this vast body of literature, there are a few notable recurring themes pertinent to our work.…”

Section: Related Workmentioning

confidence: 99%

Semantic Enrichment of Mobility Data: A Comprehensive Methodology and the MAT-BUILDER System

Lettich,

Pugliese,

Renso

et al. 2023

IEEE Access

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The widespread adoption of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. This data often needs to be enriched with a variety of semantic dimensions, or aspects, that provide contextual and heterogeneous information about the surrounding environment, resulting in the creation of multiple aspect trajectories (MATs). Common examples of aspects can be points of interest, user photos, transportation means, weather conditions, social media posts, and many more. However, the literature does not currently provide a consensus on how to semantically enrich mobility data with aspects, particularly in dynamic scenarios where semantic information is extracted from numerous and heterogeneous external data sources. In this work, we aim to address this issue by presenting a comprehensive methodology to facilitate end users in instantiating their semantic enrichment processes of movement data. The methodology is agnostic to semantic aspects and external semantic data sources. The vision behind our methodology rests on three pillars: (1) three design principles which we argue are necessary for designing systems capable of instantiating arbitrary semantic enrichment processes; (2) the MAT-BUILDER system, which embodies these principles; (3) the use of an RDF knowledge graph-based representation to store MATs datasets, thereby enabling uniform querying and analysis of enriched movement data. We qualitatively evaluate the methodology in two complementary example scenarios, where we show both the potential in generating interesting and useful semantically enriched mobility datasets, and the expressive power in querying the resulting RDF trajectories with SPARQL.

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Supporting Semantic Data Enrichment at Scale

Cited by 7 publications

References 20 publications

Reshaping Smart Cities through NGSI-LD Enrichment

Reshaping Smart Cities through NGSI-LD Enrichment

Understanding the structure of knowledge graphs with ABSTAT profiles

Semantic Enrichment of Mobility Data: A Comprehensive Methodology and the MAT-BUILDER System

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