599 research outputs found
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
In many applications, it is important to characterize the way in which two
concepts are semantically related. Knowledge graphs such as ConceptNet provide
a rich source of information for such characterizations by encoding relations
between concepts as edges in a graph. When two concepts are not directly
connected by an edge, their relationship can still be described in terms of the
paths that connect them. Unfortunately, many of these paths are uninformative
and noisy, which means that the success of applications that use such path
features crucially relies on their ability to select high-quality paths. In
existing applications, this path selection process is based on relatively
simple heuristics. In this paper we instead propose to learn to predict path
quality from crowdsourced human assessments. Since we are interested in a
generic task-independent notion of quality, we simply ask human participants to
rank paths according to their subjective assessment of the paths' naturalness,
without attempting to define naturalness or steering the participants towards
particular indicators of quality. We show that a neural network model trained
on these assessments is able to predict human judgments on unseen paths with
near optimal performance. Most notably, we find that the resulting path
selection method is substantially better than the current heuristic approaches
at identifying meaningful paths.Comment: In Proceedings of the Web Conference (WWW) 201
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge
Luminoso participated in the SemEval 2018 task on "Capturing Discriminative
Attributes" with a system based on ConceptNet, an open knowledge graph focused
on general knowledge. In this paper, we describe how we trained a linear
classifier on a small number of semantically-informed features to achieve an
score of 0.7368 on the task, close to the task's high score of 0.75.Comment: SemEval 2018, 5 page
Context-aware Path Ranking for Knowledge Base Completion
Knowledge base (KB) completion aims to infer missing facts from existing ones
in a KB. Among various approaches, path ranking (PR) algorithms have received
increasing attention in recent years. PR algorithms enumerate paths between
entity pairs in a KB and use those paths as features to train a model for
missing fact prediction. Due to their good performances and high model
interpretability, several methods have been proposed. However, most existing
methods suffer from scalability (high RAM consumption) and feature explosion
(trains on an exponentially large number of features) problems. This paper
proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems
by introducing a selective path exploration strategy. C-PR learns global
semantics of entities in the KB using word embedding and leverages the
knowledge of entity semantics to enumerate contextually relevant paths using
bidirectional random walk. Experimental results on three large KBs show that
the path features (fewer in number) discovered by C-PR not only improve
predictive performance but also are more interpretable than existing baselines
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