742 research outputs found
Lifted rule injection for relation embeddings
Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how to efficiently incorporate commonsense knowledge into such models. A recent approach regularizes relation and entity representations by propositionalization of first-order logic rules. However, propositionalization does not scale beyond domains with only few entities and rules. In this paper we present a highly efficient method for incorporating implication rules into distributed representations for automated knowledge base construction. We map entity-tuple embeddings into an approximately Boolean space and encourage a partial ordering over relation embeddings based on implication rules mined from WordNet. Surprisingly, we find that the strong restriction of the entity-tuple embedding space does not hurt the expressiveness of the model and even acts as a regularizer that improves generalization. By incorporating few commonsense rules, we achieve an increase of 2 percentage points mean average precision over a matrix factorization baseline, while observing a negligible increase in runtime
Empowering Knowledge Bases: a Machine Learning Perspective
The construction of Knowledge Bases requires quite often
the intervention of knowledge engineering and domain experts, resulting
in a time consuming task. Alternative approaches have been developed
for building knowledge bases from existing sources of information such
as web pages and crowdsourcing; seminal examples are NELL, DBPedia,
YAGO and several others. With the goal of building very large sources of
knowledge, as recently for the case of Knowledge Graphs, even more complex
integration processes have been set up, involving multiple sources of
information, human expert intervention, crowdsourcing. Despite signi -
cant e orts for making Knowledge Graphs as comprehensive and reliable
as possible, they tend to su er of incompleteness and noise, due to the
complex building process. Nevertheless, even for highly human curated
knowledge bases, cases of incompleteness can be found, for instance with
disjointness axioms missing quite often. Machine learning methods have
been proposed with the purpose of re ning, enriching, completing and
possibly raising potential issues in existing knowledge bases while showing
the ability to cope with noise. The talk will concentrate on classes
of mostly symbol-based machine learning methods, speci cally focusing
on concept learning, rule learning and disjointness axioms learning problems,
showing how the developed methods can be exploited for enriching
existing knowledge bases. During the talk it will be highlighted as, a
key element of the illustrated solutions, is represented by the integration
of: background knowledge, deductive reasoning and the evidence coming
from the mass of the data. The last part of the talk will be devoted
to the presentation of an approach for injecting background knowledge
into numeric-based embedding models to be used for predictive tasks on
Knowledge Graphs
Combining Representation Learning with Logic for Language Processing
The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for most preprocessing steps and task-specific
assumptions. However, in many cases representation learning requires a large
amount of annotated training data to generalize well to unseen data. Such
labeled training data is provided by human annotators who often use formal
logic as the language for specifying annotations. This thesis investigates
different combinations of representation learning methods with logic for
reducing the need for annotated training data, and for improving
generalization.Comment: PhD Thesis, University College London, Submitted and accepted in 201
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