5 research outputs found
Improved Knowledge Graph Embedding using Background Taxonomic Information
Knowledge graphs are used to represent relational information in terms of
triples. To enable learning about domains, embedding models, such as tensor
factorization models, can be used to make predictions of new triples. Often
there is background taxonomic information (in terms of subclasses and
subproperties) that should also be taken into account. We show that existing
fully expressive (a.k.a. universal) models cannot provably respect subclass and
subproperty information. We show that minimal modifications to an existing
knowledge graph completion method enables injection of taxonomic information.
Moreover, we prove that our model is fully expressive, assuming a lower-bound
on the size of the embeddings. Experimental results on public knowledge graphs
show that despite its simplicity our approach is surprisingly effective
Diachronic Embedding for Temporal Knowledge Graph Completion
Knowledge graphs (KGs) typically contain temporal facts indicating
relationships among entities at different times. Due to their incompleteness,
several approaches have been proposed to infer new facts for a KG based on the
existing ones-a problem known as KG completion. KG embedding approaches have
proved effective for KG completion, however, they have been developed mostly
for static KGs. Developing temporal KG embedding models is an increasingly
important problem. In this paper, we build novel models for temporal KG
completion through equipping static models with a diachronic entity embedding
function which provides the characteristics of entities at any point in time.
This is in contrast to the existing temporal KG embedding approaches where only
static entity features are provided. The proposed embedding function is
model-agnostic and can be potentially combined with any static model. We prove
that combining it with SimplE, a recent model for static KG embedding, results
in a fully expressive model for temporal KG completion. Our experiments
indicate the superiority of our proposal compared to existing baselines