From STLab

Extracting core Knowledge from Linked Data

• LOD-Analysis-Intro
• LOD-Analysis-Statistics
• LOD-Analysis-Graphs
• LOD-Analysis-TypesAndProperties
• LOD-Analysis-EmergingKP
• LOD-Analysis-Clusters
• LOD-Analysis-AlignmentsToGeneralKP

Introduction

The cloud of Linked Open Data (LOD) appears, in recent research, to be an ideal basis for improving user experience when interacting with Web content across different applications and domains. Using LOD datasets, however, is not straightforward. They often introduce noisy results and do not follow a unified way of organizing their knowledge, and thus, it is unknown how to query them. To deal with these problems we propose a knowledge patterns (KP) based approach to analyze LOD datasets, and we show how the recognition of KP in datasets can support querying them even if their vocabularies are previously unknown. Finally, we discuss results from experiments on three LOD datasets.

Outline

• Types and properties displays the vocabularies extracted from the analyzed datasets. Whether the type and property specifications comply with those of the externally defined vocabularies, where applicable, is within the scope of our research.
• Statistics reports the results of our quantitative analysis
• Graphs displays some of the results shown above in graphical form, organized per dataset.
• Emerging KPs shows emerging patterns.
• Clusters shows how and which path clusters were constructed from the traversible paths extracted from the analyzed datasets.
• Alignments to general KPs page shows the outcome of our manual alignment work from properties and types to knowledge patterns.