Popular public knowledge graphs like DBpedia or YAGO are created from Wikipedia as a
source, and thus limited to the information contained therein. At the same time, Wikifarms …

Deep neural networks are becoming ubiquitous in text mining and natural language
processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully …

Large-scale knowledge graphs have become prevalent on the Web and have demonstrated
their usefulness for several tasks. One challenge associated to knowledge graphs is the …

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned
with deriving higher-level insights from data. The tasks performed in this field are knowledge …

While high-quality taxonomies are essential to the Semantic Web, building them for large
knowledge graphs is an expensive process. Likewise, creating taxonomies that accurately …

We present a simple and effective feed-forward neural architecture for discriminating
between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our …

Understanding customers demands and needs is one of the keys to success for large
enterprises. Customers come to a large enterprise with a set of requirements and finding a …

Ontology alignment plays a key role in the management of heterogeneous data sources and
metadata. In this context, various ontology alignment techniques have been proposed to …

Inconsistency handling is an important issue in knowledge management. Especially in
ontology engineering, logical inconsistencies may occur during ontology construction. A …

In this paper, we investigate the use of probabilistic graphical models, specifically stochastic
blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These …