About Me
Research Interests
Machine Learning, Natural Language Processing, Data Visualization and Analytics, Big Data.
Publications
2021
G Loseto; E Patton; O Seneviratne; W Van Woensel; F Scioscia; L Kagal
Mobile App Development for the Semantic Web of Things with Punya Proceedings Article
In: 20th International Semantic Web Conference: Demo Track (ISWC '21), 2021.
BibTeX | Tags: Internet of Things, Mobile Computing, Semantic Web
@inproceedings{Loseto2021,
title = {Mobile App Development for the Semantic Web of Things with Punya},
author = {G Loseto and E Patton and O Seneviratne and W Van Woensel and F Scioscia and L Kagal},
year = {2021},
date = {2021-10-24},
urldate = {2021-01-01},
booktitle = {20th International Semantic Web Conference: Demo Track (ISWC '21)},
keywords = {Internet of Things, Mobile Computing, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Oshani Seneviratne; William Van Woensel; Giuseppe Loseto; Floriano Scioscia; Evan Patton; Lalana Kagal
Rapid Prototyping of Mobile Apps for Clinical Research using Semantic Web Technologies Proceedings Article
In: 20th International Semantic Web Conference: Demo Track (ISWC '21), 2021.
BibTeX | Tags: Mobile Computing, Mobile Health, Semantic Web
@inproceedings{Seneviratne2021,
title = {Rapid Prototyping of Mobile Apps for Clinical Research using Semantic Web Technologies},
author = {Oshani Seneviratne and William Van Woensel and Giuseppe Loseto and Floriano Scioscia and Evan Patton and Lalana Kagal},
year = {2021},
date = {2021-10-24},
urldate = {2021-01-01},
booktitle = {20th International Semantic Web Conference: Demo Track (ISWC '21)},
keywords = {Mobile Computing, Mobile Health, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Evan Patton; William Van Woensel; Oshani Seneviratne; Giuseppe Loseto; Floriano Scioscia; Lalana Kagal
The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features Proceedings Article
In: 20th International Semantic Web Conference (ISWC '21), 2021.
BibTeX | Tags: Mobile Computing, Mobile Health, Semantic Web
@inproceedings{Patton2021,
title = {The Punya Platform: Building Mobile Research Apps with Linked Data and Semantic Features},
author = {Evan Patton and William Van Woensel and Oshani Seneviratne and Giuseppe Loseto and Floriano Scioscia and Lalana Kagal},
year = {2021},
date = {2021-10-24},
urldate = {2021-01-01},
booktitle = {20th International Semantic Web Conference (ISWC '21)},
keywords = {Mobile Computing, Mobile Health, Semantic Web},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
William Van Woensel; Syed Sibte Raza Abidi
Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL Journal Article
In: Semantic Web Journal, 2018.
Abstract | Links | BibTeX | Tags: Mobile Computing, OWL2 RL, Semantic Web reasoning
@article{SWJ-WVW-2018,
title = {Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL},
author = {William Van Woensel and Syed Sibte Raza Abidi},
url = {http://www.semantic-web-journal.net/system/files/swj1881.pdf},
year = {2018},
date = {2018-08-06},
journal = {Semantic Web Journal},
abstract = {Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.},
keywords = {Mobile Computing, OWL2 RL, Semantic Web reasoning},
pubstate = {published},
tppubtype = {article}
}
Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.
William Van Woensel; Syed Sibte Raza Abidi
Optimizing Semantic Reasoning on Memory-Constrained Platforms using the RETE Algorithm Proceedings Article
In: 15th Extended Semantic Web Conference (ESWC 2018), pp. 682-696, Springer LNCS, Heraklion, Greece, 2018.
Abstract | Links | BibTeX | Tags: Mobile Computing, OWL2 RL, RETE, Semantic Web reasoning
@inproceedings{ESWC-WVW-2018,
title = {Optimizing Semantic Reasoning on Memory-Constrained Platforms using the RETE Algorithm},
author = {William Van Woensel and Syed Sibte Raza Abidi},
doi = {10.1007/978-3-319-93417-4_44},
year = {2018},
date = {2018-06-07},
booktitle = {15th Extended Semantic Web Conference (ESWC 2018)},
pages = {682-696},
publisher = {Springer LNCS},
address = {Heraklion, Greece},
abstract = {Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less re-mote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of ontology-based reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. Further, we show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic reasoning both on the PC and mobile platform.},
keywords = {Mobile Computing, OWL2 RL, RETE, Semantic Web reasoning},
pubstate = {published},
tppubtype = {inproceedings}
}
Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less re-mote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of ontology-based reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. Further, we show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic reasoning both on the PC and mobile platform.
2016
William Van Woensel; Sven Casteleyn
A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources Journal Article
In: Web Semant., vol. 36, no. C, pp. 58–76, 2016, ISSN: 1570-8268.
Links | BibTeX | Tags: Cache replacement, Data caching, Data indexing, Data integration, Mobile Computing, Open world assumption
@article{VanWoensel:2016:MQS:2895617.2895676,
title = {A Mobile Query Service for Integrated Access to Large Numbers of Online Semantic Web Data Sources},
author = { William Van Woensel and Sven Casteleyn},
url = {https://niche.cs.dal.ca/wp-content/uploads/2016/05/A-Mobile-Query-Service-for-Integrated-Access-to-Large-Numbers-of-Online-Semantic-Web-Data-Sources-1.pdf},
doi = {10.1016/j.websem.2015.10.002},
issn = {1570-8268},
year = {2016},
date = {2016-01-01},
journal = {Web Semant.},
volume = {36},
number = {C},
pages = {58--76},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
keywords = {Cache replacement, Data caching, Data indexing, Data integration, Mobile Computing, Open world assumption},
pubstate = {published},
tppubtype = {article}
}
2015
William Van Woensel
Mobile Semantic Query Distribution with Graph-Based Outsourcing of Subqueries Proceedings Article
In: Proceedings of International Workshop on Mobile Deployment of Semantic Technologies (MoDeST 2015), 2015.
Links | BibTeX | Tags: Mobile Computing, Querying Distributed RDF
@inproceedings{van_woensel_modest2015,
title = {Mobile Semantic Query Distribution with Graph-Based Outsourcing of Subqueries},
author = {William Van Woensel},
url = {http://web.cs.dal.ca/~woensel/paper/Mobile Semantic Query Distribution with Graph-Based Outsourcing of Subqueries.pdf},
year = {2015},
date = {2015-10-11},
booktitle = {Proceedings of International Workshop on Mobile Deployment of Semantic Technologies (MoDeST 2015)},
keywords = {Mobile Computing, Querying Distributed RDF},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
William Van Woensel; Sven Casteleyn; Elien Paret; Olga De Troyer
Mobile Querying of Online Semantic Web Data for Context-Aware Applications Journal Article
In: IEEE Internet Computing Special Issue (Semantics in Location-Based Services), vol. 15, no. 6, pp. 32–39, 2011, ISSN: 1089-7801.
Abstract | Links | BibTeX | Tags: Mobile Computing, Semantic Web
@article{5999639,
title = {Mobile Querying of Online Semantic Web Data for Context-Aware Applications},
author = { William Van Woensel and Sven Casteleyn and Elien Paret and Olga De Troyer},
url = {http://web.cs.dal.ca/~woensel/paper/Mobile%20Querying%20of%20Online%20Semantic%20Web%20Data%20for%20Context-Aware%20Applications.pdf},
issn = {1089-7801},
year = {2011},
date = {2011-01-01},
journal = {IEEE Internet Computing Special Issue (Semantics in Location-Based Services)},
volume = {15},
number = {6},
pages = {32--39},
abstract = {Mobile devices are increasingly multifunctional and personal, providing mobile applications with the necessary user information to achieve personalization. At the same time, detection technologies let such devices find nearby physical entities and thus map the user's environment. By exploiting existing online Semantic Web sources about these detected entities, mobile applications can further improve personalization. SCOUT is a mobile application framework that supports linking physical entities to online semantic data sources. It provides applications with an integrated, query-able view on these sources and the user's environment. The authors developed a tailored data management approach to efficiently access these distributed online semantic sources.},
keywords = {Mobile Computing, Semantic Web},
pubstate = {published},
tppubtype = {article}
}
Mobile devices are increasingly multifunctional and personal, providing mobile applications with the necessary user information to achieve personalization. At the same time, detection technologies let such devices find nearby physical entities and thus map the user's environment. By exploiting existing online Semantic Web sources about these detected entities, mobile applications can further improve personalization. SCOUT is a mobile application framework that supports linking physical entities to online semantic data sources. It provides applications with an integrated, query-able view on these sources and the user's environment. The authors developed a tailored data management approach to efficiently access these distributed online semantic sources.