8,279 research outputs found
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile
method to perform machine learning on relational data such as knowledge graphs.
In this work, we propose holographic embeddings (HolE) to learn compositional
vector space representations of entire knowledge graphs. The proposed method is
related to holographic models of associative memory in that it employs circular
correlation to create compositional representations. By using correlation as
the compositional operator HolE can capture rich interactions but
simultaneously remains efficient to compute, easy to train, and scalable to
very large datasets. In extensive experiments we show that holographic
embeddings are able to outperform state-of-the-art methods for link prediction
in knowledge graphs and relational learning benchmark datasets.Comment: To appear in AAAI-1
Computational fact checking from knowledge networks
Traditional fact checking by expert journalists cannot keep up with the
enormous volume of information that is now generated online. Computational fact
checking may significantly enhance our ability to evaluate the veracity of
dubious information. Here we show that the complexities of human fact checking
can be approximated quite well by finding the shortest path between concept
nodes under properly defined semantic proximity metrics on knowledge graphs.
Framed as a network problem this approach is feasible with efficient
computational techniques. We evaluate this approach by examining tens of
thousands of claims related to history, entertainment, geography, and
biographical information using a public knowledge graph extracted from
Wikipedia. Statements independently known to be true consistently receive
higher support via our method than do false ones. These findings represent a
significant step toward scalable computational fact-checking methods that may
one day mitigate the spread of harmful misinformation
Link prediction in very large directed graphs: Exploiting hierarchical properties in parallel
Link prediction is a link mining task that tries to find new edges within a given graph. Among the targets of link prediction there is large directed graphs, which are frequent structures nowadays. The typical sparsity of large graphs demands of high precision predictions in order to obtain usable results. However, the size of those graphs only permits the execution of scalable algorithms. As a trade-off between those two problems we recently proposed a link prediction algorithm for directed graphs that exploits hierarchical properties. The algorithm can be classified as a local score, which entails scalability. Unlike the rest of local scores, our proposal assumes the existence of an underlying model for the data which allows it to produce predictions with a higher precision. We test the validity of its hierarchical assumptions on two clearly hierarchical data sets, one of them based on RDF. Then we test it on a non-hierarchical data set based on Wikipedia to demonstrate its broad applicability. Given the computational complexity of link prediction in very large graphs we also introduce some general recommendations useful to make of link prediction an efficiently parallelized problem.Peer ReviewedPostprint (published version
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