5,666 research outputs found
Adversarial Sets for Regularising Neural Link Predictors
In adversarial training, a set of models learn together by pursuing competing
goals, usually defined on single data instances. However, in relational
learning and other non-i.i.d domains, goals can also be defined over sets of
instances. For example, a link predictor for the is-a relation needs to be
consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3)
hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for
deriving an inconsistency loss, measuring the degree to which the model
violates the assumptions on an adversarially-generated set of examples. The
training objective is defined as a minimax problem, where an adversary finds
the most offending adversarial examples by maximising the inconsistency loss,
and the model is trained by jointly minimising a supervised loss and the
inconsistency loss on the adversarial examples. This yields the first method
that can use function-free Horn clauses (as in Datalog) to regularise any
neural link predictor, with complexity independent of the domain size. We show
that for several link prediction models, the optimisation problem faced by the
adversary has efficient closed-form solutions. Experiments on link prediction
benchmarks indicate that given suitable prior knowledge, our method can
significantly improve neural link predictors on all relevant metrics.Comment: Proceedings of the 33rd Conference on Uncertainty in Artificial
Intelligence (UAI), 201
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for
entities and relations, and is proven to be effective in many applications.
Crossover interactions --- bi-directional effects between entities and
relations --- help select related information when predicting a new triple, but
haven't been formally discussed before. In this paper, we propose CrossE, a
novel knowledge graph embedding which explicitly simulates crossover
interactions. It not only learns one general embedding for each entity and
relation as most previous methods do, but also generates multiple triple
specific embeddings for both of them, named interaction embeddings. We evaluate
embeddings on typical link prediction tasks and find that CrossE achieves
state-of-the-art results on complex and more challenging datasets. Furthermore,
we evaluate embeddings from a new perspective --- giving explanations for
predicted triples, which is important for real applications. In this work, an
explanation for a triple is regarded as a reliable closed-path between the head
and the tail entity. Compared to other baselines, we show experimentally that
CrossE, benefiting from interaction embeddings, is more capable of generating
reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201
Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB
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