Collective Multi-type Entity Alignment Between Knowledge Graphs

Zhu, Qi; Wei, Hao; Sisman, Bunyamin; Zheng, Da; Faloutsos, Christos; Dong, Xin Luna

doi:10.1145/3366423.3380289

Cited by 36 publications

(24 citation statements)

References 31 publications

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“…The GCN is a model that is inspired by the Convolutional Neural Network (CNN); it receives a subset of the neighboring nodes of a node as an input and discovers low and dense dimensions that can differentiate nodes, and it is usually used in cross-lingual KG alignment [36][37][38][39]. GraphSAGE minimizes information loss by concatenating vectors of neighbors rather than summing them into a single value in the process of neighbor aggregation [40,41]. GAT utilizes the concept of attention to individually deal with the importance of neighbor nodes or relations [21,[42][43][44][45][46][47].…”

Section: Knowledge Graph Alignmentmentioning

confidence: 99%

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Kim

Sohn

2022

Sensors

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Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber–physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment.

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Section: Knowledge Graph Alignmentmentioning

confidence: 99%

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Kim

Sohn

2022

Sensors

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“…Knowledge graphs are defined in several ways. In [51], they are defined as heterogeneous directed graphs, while in [60] knowledge graphs are the same as heterogeneous graphs. But there are also definitions that do not see a knowledge graph as a graph, combined from the aforementioned types, see for example [15] for an overview.…”

Section: Definition 32 (Static Structural Graph Properties)mentioning

confidence: 99%

Graph type expressivity and transformations

M.¹,

Beddar-Wiesing²,

Moallemy-Oureh³

et al. 2021

Preprint

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Graph representations have gained importance in almost every scientific field, ranging from mathematics, biology, social sciences and physics to computer science. In contrast to other data formats, graphs propose the possibility to model relations between entities. Together with the continuously rising amount of available data, graphs therefore open up a wide range of modeling capabilities for theoretical and real-world problems. However, the modeling possibilities of graphs have not been fully exploited. One reason for this is that there is neither an easily comprehensible overview of graph types nor an analysis of their modeling capacities available. As a result, neither the potential of modeling with certain graph types is exhausted nor higher modeling freedom and more efficient computing of graphs after transformation to another graph type is in scope of view of many users. In order to clarify the modeling possibilities of graphs, we order the different graph types, collate their memory complexity and provide an expressivity measure on them. Furthermore, we introduce transformation algorithms between the graph types from which equal expressivity of all graph types can be inferred, i.e., they are able to represent the same information or properties respectively. Finally, we provide a guideline for the question when a graph type transformation is efficient by defining a cost function dependend on the memory complexity and the transformation runtime as a decision-making tool.

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“…In the entity alignment process, typically a large number of nodes -up to 100,00 -are used for training the alignment algorithm. [36,41,44]. CNNs, however, do not typically detect enough categories to support this scale of entity alignment training.…”

Section: Experiments Detailsmentioning

confidence: 99%

Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases

Liu,

Delany,

McKeever

2021

Preprint

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Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, the understanding of hidden feature map activations is limited by the discriminative knowledge gleaned during training. The aim of our work is to explain and expand CNNs models via the mirroring or alignment of the network to an external knowledge base. This will allow us to give a semantic context or label for each visual feature. Using the resultant aligned embedding space, we can match CNN feature activations to nodes in our external knowledge base. This supports knowledge-based interpretation of the features associated with model decisions.To demonstrate our approach, we build two separate graphs from: (1) ConceptNet knowledge base and (2) a public CNN. We use an entity alignment method to align the feature nodes in the CNN with the nodes in the ConceptNet based knowledge graph. We then measure the proximity of CNN graph nodes to semantically meaningful knowledge base nodes. Our results show that in the aligned embedding space, nodes from the knowledge graph are close to the CNN feature nodes that have similar meanings, indicating that nodes from an external knowledge base can act as explanatory semantic references for features in the model. We analyse a variety of graph building methods in order to improve the results from our embedding space. We further demonstrate that by using hierarchical relationships from our external knowledge base, we can locate new unseen classes outside the CNN training set in our embeddings space based on visual feature activations. This suggests that we can adapt our approach to identify unseen classes based on CNN feature activations. Our demonstrated approach of aligning a CNN with an external knowledge base paves the way to reason about and beyond the trained model, with future adaptations to explainable models and zero-shot learning.

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Collective Multi-type Entity Alignment Between Knowledge Graphs

Cited by 36 publications

References 31 publications

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Graph type expressivity and transformations

Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases

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