  Large knowledge graphs increasingly add value to various applications that
require machines to recognize and understand queries and their semantics, as in
search or question answering systems. Latent variable models have increasingly
gained attention for the statistical modeling of knowledge graphs, showing
promising results in tasks related to knowledge graph completion and cleaning.
Besides storing facts about the world, schema-based knowledge graphs are backed
by rich semantic descriptions of entities and relation-types that allow
machines to understand the notion of things and their semantic relationships.
In this work, we study how type-constraints can generally support the
statistical modeling with latent variable models. More precisely, we integrated
prior knowledge in form of type-constraints in various state of the art latent
variable approaches. Our experimental results show that prior knowledge on
relation-types significantly improves these models up to 77% in link-prediction
tasks. The achieved improvements are especially prominent when a low model
complexity is enforced, a crucial requirement when these models are applied to
very large datasets. Unfortunately, type-constraints are neither always
available nor always complete e.g., they can become fuzzy when entities lack
proper typing. We show that in these cases, it can be beneficial to apply a
local closed-world assumption that approximates the semantics of relation-types
based on observations made in the data.
