HASAN , Rakebul

As the complexity of the Semantic Web increases, efficient ways to query the Semantic Web data is becoming increasingly impor-tant. Moreover, consumers of the Semantic Web data may need expla-nations for debugging or understanding the reasoning behind producing the data. In this paper, firstly we address the problem of SPARQL query performance...

Our goal is to assist users in understanding SPARQL query performance, query results, and derivations on Linked Data. To help users in understanding query performance, we provide query performance predictions based on the query execution history. We present a machine learning approach to predict query performances. We do not use statistics...

Linked Data consumers may need explanations for debug-ging or understanding the reasoning behind producing the data. They may need the possibility to transform long explanations into more un-derstandable short explanations. In this paper, we discuss an approach to explain reasoning over Linked Data. We introduce a vocabulary to de-scribe...

In this paper we address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. Traditional approaches for estimating SPARQL query cost are based on statistics about the underlying data. However, in many use-cases involving querying Linked Data,...

We address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. We show how to model SPARQL queries as feature vectors, and use k -nearest neighbors regression and Support Vector Machine with the nu-SVR kernel to accurately (R^2 value of...

Semantic Web applications use interconnected distributed data and inferential capabilities to compute their results. The users of Semantic Web applications might find it difficult to understand how a result is produced or how a new piece of information is derived in the process. Explanation enables users to understand the process of obtaining...

Semantic Web applications use interconnected distributed data and inferential capabilities to compute their results. The users of Semantic Web applications might find it difficult to understand how a result is produced or how a new piece of information is derived in the process. Explanation enables users to understand the process of obtaining...

In this paper first we address the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to predict SPARQL query execution time. We generate the training dataset from real queries collected...

A user of a Semantic Web application may not trust its results because he may not understand how the application produces its results using distributed data and inferential capabilities. Explanation-aware Semantic Web applications provide explanations of their reasoning - explaining why an application has performed a given step or which...

In this paper first we address the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to predict SPARQL query execution time. We generate the training dataset from real queries collected...

Collaborative Semantic Web applications produce ever changing interlinked Semantic Web data. Applications that utilize these data to obtain their results should provide explanations about how the results are obtained in order to ensure the effectiveness and increase the user acceptance of these applications. Justifications providing meta...

In this paper, we present an approach to explain SPARQL query results for Linked Data using why-provenance. We present a non-annotation-based algorithm to generate why-provenance and show its feasibility for Linked Data. We present an explanation-aware federated query processor prototype and show the presentation of our explanations. We present...