148 research outputs found
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
Towards Exploiting Implicit Human Feedback for Improving RDF2vec Embeddings
RDF2vec is a technique for creating vector space embeddings from an RDF
knowledge graph, i.e., representing each entity in the graph as a vector. It
first creates sequences of nodes by performing random walks on the graph. In a
second step, those sequences are processed by the word2vec algorithm for
creating the actual embeddings. In this paper, we explore the use of external
edge weights for guiding the random walks. As edge weights, transition
probabilities between pages in Wikipedia are used as a proxy for the human
feedback for the importance of an edge. We show that in some scenarios, RDF2vec
utilizing those transition probabilities can outperform both RDF2vec based on
random walks as well as the usage of graph internal edge weights.Comment: Workshop paper accepted at Deep Learning for Knowledge Graphs
Workshop 202
More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
RDF2vec is an embedding technique for representing knowledge graph entities
in a continuous vector space. In this paper, we investigate the effect of
materializing implicit A-box axioms induced by subproperties, as well as
symmetric and transitive properties. While it might be a reasonable assumption
that such a materialization before computing embeddings might lead to better
embeddings, we conduct a set of experiments on DBpedia which demonstrate that
the materialization actually has a negative effect on the performance of
RDF2vec. In our analysis, we argue that despite the huge body of work devoted
on completing missing information in knowledge graphs, such missing implicit
information is actually a signal, not a defect, and we show examples
illustrating that assumption.Comment: Accepted at the Workshop on Combining Symbolic and Sub-symbolic
methods and their Applications (CSSA 2020
I am all EARS: Using open data and knowledge graph embeddings for music recommendations
Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge
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