William Van Woensel

@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}

}

@article{Mohammadhassanzadeh2017,

title = {Semantics-based Plausible Reasoning to Extend the Knowledge Coverage of Medical Knowledge Bases for Improved Clinical Decision Support},

author = {Hossein Mohammadhassanzadeh and William Van Woensel and Samina Raza Abidi and Syed Sibte Raza Abidi},

url = {http://rdcu.be/paPY},

doi = {10.1186/s13040-017-0123-y},

year  = {2017},

date = {2017-02-10},

journal = {Journal of BioData Mining},

volume = {10},

number = {7},

abstract = {Background

Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.



Results

We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead.



Conclusions

We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases.},

keywords = {Analogical reasoning, Inductive generalization, Medical knowledge bases, Plausible reasoning, Semantic Web reasoning},

pubstate = {published},

tppubtype = {article}

}