Topical profiling of the datasets contained in the Linking Open Data (LOD) cloud has been of interest since such kind of data became available within the Web. Different automatic classification approaches have been proposed in the past, in order to overcome the manual task of assigning topics for each and every individual (new) dataset. Although the quality of those automated approaches is comparably sufficient, it has been shown, that in most cases a single topical label per dataset does not capture the topics described by the content of the dataset. Therefore, within the following study, we introduce a machine-learning based approach in order to assign a single topic, as well as multiple topics for one LOD dataset and evaluate the results. As part of this work, we present the first multi-topic classification benchmark for LOD cloud datasets, which is freely accessible. In addition, the article discusses the challenges and obstacles, which need to be addressed when building such a benchmark.

Spahiu, B., Maurino, A., Meusel, R. (2019). Topic Profiling Benchmarks in the Linked Open Data Cloud: Issues and Lessons Learned. SEMANTIC WEB, 10(2), 329-348 [10.3233/SW-180323].

Topic Profiling Benchmarks in the Linked Open Data Cloud: Issues and Lessons Learned

Spahiu, B
;
Maurino, A;
2019

Abstract

Topical profiling of the datasets contained in the Linking Open Data (LOD) cloud has been of interest since such kind of data became available within the Web. Different automatic classification approaches have been proposed in the past, in order to overcome the manual task of assigning topics for each and every individual (new) dataset. Although the quality of those automated approaches is comparably sufficient, it has been shown, that in most cases a single topical label per dataset does not capture the topics described by the content of the dataset. Therefore, within the following study, we introduce a machine-learning based approach in order to assign a single topic, as well as multiple topics for one LOD dataset and evaluate the results. As part of this work, we present the first multi-topic classification benchmark for LOD cloud datasets, which is freely accessible. In addition, the article discusses the challenges and obstacles, which need to be addressed when building such a benchmark.
Articolo in rivista - Articolo scientifico
Benchmarking, Topic Classification, Linked Open Data, LOD, Topical Profiling
English
2019
2019
10
2
329
348
partially_open
Spahiu, B., Maurino, A., Meusel, R. (2019). Topic Profiling Benchmarks in the Linked Open Data Cloud: Issues and Lessons Learned. SEMANTIC WEB, 10(2), 329-348 [10.3233/SW-180323].
File in questo prodotto:
File Dimensione Formato  
Topic_Profiling_Benchmarks_in_the_Linked_Open_Data_Cloud__Issues_and_Lessons_Learned (2).pdf

accesso aperto

Descrizione: Articolo Principale
Tipologia di allegato: Submitted Version (Pre-print)
Dimensione 689.44 kB
Formato Adobe PDF
689.44 kB Adobe PDF Visualizza/Apri
1-Topic_Profiling_Benchmarks_in_the_Linked_Open_Data_Cloud__Issues_and_Lessons_Learned.pdf

Solo gestori archivio

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 686.92 kB
Formato Adobe PDF
686.92 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/219547
Citazioni
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 6
Social impact