52,059 research outputs found
Commonsense Knowledge Base Completion with Structural and Semantic Context
Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and
ConceptNet) poses unique challenges compared to the much studied conventional
knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form
text to represent nodes, resulting in orders of magnitude more nodes compared
to conventional KBs (18x more nodes in ATOMIC compared to Freebase
(FB15K-237)). Importantly, this implies significantly sparser graph structures
- a major challenge for existing KB completion methods that assume densely
connected graphs over a relatively smaller set of nodes. In this paper, we
present novel KB completion models that can address these challenges by
exploiting the structural and semantic context of nodes. Specifically, we
investigate two key ideas: (1) learning from local graph structure, using graph
convolutional networks and automatic graph densification and (2) transfer
learning from pre-trained language models to knowledge graphs for enhanced
contextual representation of knowledge. We describe our method to incorporate
information from both these sources in a joint model and provide the first
empirical results for KB completion on ATOMIC and evaluation with ranking
metrics on ConceptNet. Our results demonstrate the effectiveness of language
model representations in boosting link prediction performance and the
advantages of learning from local graph structure (+1.5 points in MRR for
ConceptNet) when training on subgraphs for computational efficiency. Further
analysis on model predictions shines light on the types of commonsense
knowledge that language models capture well.Comment: AAAI 202
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
- …