Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs

Towards Profiling Knowledge Graphs

Knowledge Graphs, such as DBpedia, YAGO, or Wikidata, are valuable resources for building intelligent applications like data analytics tools or recommender systems. Understanding what is in those knowledge graphs is a crucial prerequisite for selecing a Knowledge Graph for a task at hand. Hence, Knowledge Graph profiling i.e., quantifying the structure and contents of knowledge graphs, as well ...

Towards Neural Knowledge DNA

In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicate to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to sup...

Towards Automatic Vandalism Detection in OpenStreetMap

The OpenStreetMap (OSM) project, a well-known source of freely available worldwide geodata collected by volunteers, has experienced a consistent increase in popularity in recent years. One of the main caveats that is closely related to this popularity increase is different types of vandalism that occur in the projects database. Since the applicability and reliability of crowd-sourced geodata, a...

Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs ...

Towards Intrinsic Quality Analysis of OpenStreetMap Datasets