77 citations · 353 across the 60 of their papers we have counts for
7 papers · 1 filter
Embedding Geographic Locations for Modelling the Natural Environment using Flickr Tags and Structured Data
Shelan S. Jeawak, Christopher B. Jones, Steven Schockaert
Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for mod…
Improving Cross-Lingual Word Embeddings by Meeting in the Middle
Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke +1
Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. Recently, it has been shown that these embeddings can be effectively learned by aligning two…
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors
Luis Espinosa-Anke, Steven Schockaert
We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these…
Learning Conceptual Space Representations of Interrelated Concepts
Zied Bouraoui, Steven Schockaert
Several recently proposed methods aim to learn conceptual space representations from large text collections. These learned representations asso- ciate each object from a given doma…
From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules
Víctor Gutiérrez-Basulto, Steven Schockaert
Recent years have witnessed the successful application of low-dimensional vector space representations of knowledge graphs to predict missing facts or find erroneous ones. However,…
VC-Dimension Based Generalization Bounds for Relational Learning
Ondrej Kuzelka, Yuyi Wang, Steven Schockaert
In many applications of relational learning, the available data can be seen as a sample from a larger relational structure (e.g. we may be given a small fragment from some social n…
PAC-Reasoning in Relational Domains
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules. In particular, our aim is to provide bounds on the (expect…