In this post, we would like to present our recent contribution in knowledge graphs embedding (KGE) models which was accepted at the AAAI 2021 conference.

Knowledge Graph Embeddings
Knowledge Graphs [1] are used by many organisations to store and structure relevant information. In knowledge graphs, entities are represented by nodes and relationships are represented by edges. In the example here, you can, for instance, see that Carl Gotthard Langhans is the architect of Brandenburg Gate.

Figure1. Sample knowledge Graph

Most current machine learning methods require an input in the form of features, which means they cannot directly use a graph as input. …

This blog post was written by several authors in the SDA Team.

In 2020, the SDA team contributed to several improvements to the state-of-the-art in individual AI challenges on standard community datasets. We reported about those results in several papers and blog posts. In this post, we want to collect those and provide pointers to further relevant information. A common theme is that we achieved better performance on various tasks by improving the use of knowledge graph structures. Below is a table listing improvements on particular tasks and (very briefly) how we achieved them. …

Language Model Transformers as Evaluators for Open-domain Dialogues

This blog post was written by Rostislav Nedelchev.

Dialogue systems, nowadays more commonly referred to as chatbots, have been around since the 1960s. One of the first well-known examples of such a system is ELIZA by Joseph Weizenbaum. The system used keyword matching and rules to mimic Simple Rogerian psychological therapy. Since then, the research field has evolved massively, and dialogue systems are now present in everyday life. They have widespread usage in voice assistants like Siri or Alexa, or chatbots on social media platforms that help us book a restaurant table or give support in case of problems. They…

This blog post was written by Isaiah Onando Mulang’.

Part 1 : Encoding KG Aliases in Attentive Neural Network for Entity Linking (ARJUN)

Entity Linking is a long task in NLP dating from 2006, but really picked up around 2010 with Systems such as Ferragina P. & Scaiella U. ‘s TAGME, and P.N. Mendes et. al. ‘s DBpedia Spotlight among the very first initiatives. When Hoffart et. al. released the data set on CONLL-AIDA dataset; aligning the 2003 CONLL NER dataset with Entities in Wikipedia, there was a little burst in attention to this research task. Over a long time…

SDA Research

The Smart Data Analytics (SDA) research group at the University of Bonn working on #semantics, #machinelearning and #bigdata.

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