21 research outputs found
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
Knowledge Graph Driven Recommendation System Algorithm
In this paper, we propose a novel graph neural network-based recommendation
model called KGLN, which leverages Knowledge Graph (KG) information to enhance
the accuracy and effectiveness of personalized recommendations. We first use a
single-layer neural network to merge individual node features in the graph, and
then adjust the aggregation weights of neighboring entities by incorporating
influence factors. The model evolves from a single layer to multiple layers
through iteration, enabling entities to access extensive multi-order associated
entity information. The final step involves integrating features of entities
and users to produce a recommendation score. The model performance was
evaluated by comparing its effects on various aggregation methods and influence
factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows
an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%,
respectively, which is better than existing benchmark methods like LibFM,
DeepFM, Wide&Deep, and RippleNet
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
Developing link prediction models to automatically complete knowledge graphs
has recently been the focus of significant research interest. The current
methods for the link prediction taskhavetwonaturalproblems:1)the relation
distributions in KGs are usually unbalanced, and 2) there are many unseen
relations that occur in practical situations. These two problems limit the
training effectiveness and practical applications of the existing link
prediction models. We advocate a holistic understanding of KGs and we propose
in this work a unified Generalized Relation Learning framework GRL to address
the above two problems, which can be plugged into existing link prediction
models. GRL conducts a generalized relation learning, which is aware of
semantic correlations between relations that serve as a bridge to connect
semantically similar relations. After training with GRL, the closeness of
semantically similar relations in vector space and the discrimination of
dissimilar relations are improved. We perform comprehensive experiments on six
benchmarks to demonstrate the superior capability of GRL in the link prediction
task. In particular, GRL is found to enhance the existing link prediction
models making them insensitive to unbalanced relation distributions and capable
of learning unseen relations.Comment: Preprint of accepted AAAI2021 pape
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice
The autonomous driving (AD) industry is exploring the use of knowledge graphs
(KGs) to manage the vast amount of heterogeneous data generated from vehicular
sensors. The various types of equipped sensors include video, LIDAR and RADAR.
Scene understanding is an important topic in AD which requires consideration of
various aspects of a scene, such as detected objects, events, time and
location. Recent work on knowledge graph embeddings (KGEs) - an approach that
facilitates neuro-symbolic fusion - has shown to improve the predictive
performance of machine learning models. With the expectation that
neuro-symbolic fusion through KGEs will improve scene understanding, this
research explores the generation and evaluation of KGEs for autonomous driving
data. We also present an investigation of the relationship between the level of
informational detail in a KG and the quality of its derivative embeddings. By
systematically evaluating KGEs along four dimensions -- i.e. quality metrics,
KG informational detail, algorithms, and datasets -- we show that (1) higher
levels of informational detail in KGs lead to higher quality embeddings, (2)
type and relation semantics are better captured by the semantic transitional
distance-based TransE algorithm, and (3) some metrics, such as coherence
measure, may not be suitable for intrinsically evaluating KGEs in this domain.
Additionally, we also present an (early) investigation of the usefulness of
KGEs for two use-cases in the AD domain.Comment: 11 pages, To appear in AAAI 2020 Spring Symposium on Combining
Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020
Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball
Personalized recommender systems are increasingly important as more content
and services become available and users struggle to identify what might
interest them. Thanks to the ability for providing rich information, knowledge
graphs (KGs) are being incorporated to enhance the recommendation performance
and interpretability. To effectively make use of the knowledge graph, we
propose a recommendation model in the hyperbolic space, which facilitates the
learning of the hierarchical structure of knowledge graphs. Furthermore, a
hyperbolic attention network is employed to determine the relative importances
of neighboring entities of a certain item. In addition, we propose an adaptive
and fine-grained regularization mechanism to adaptively regularize items and
their neighboring representations. Via a comparison using three real-world
datasets with state-of-the-art methods, we show that the proposed model
outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K
recommendation.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI
2021