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

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

Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment

Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment

Semantic Similarity Calculation Method using Information Contents-based Edge Weighting

In this paper, we propose Semantic Similarity calculation measurement using INformation contents on EdGEs of ontology (SSINEGE) which is a hybrid edge- and information contents-based methodology. SSINEGE is devised to solve the limitation of the applying the same weighted edges by edge-based similarity. So, SSINEGE adopts information-contents theory to calculate the varied weights of edges. The varied weighted edges by SSINEGE can also solve a problem with the same degree of similarity for all pairs of concepts that are sharing a same Least Common Subsumer (LCS). To minimize the overlapped information-contents on the weighted, SSINEGE adopts the conceptual path between concepts instead of depths of the ontology. To verify the superiority of SSINEGE, we compared SSINEGE with widely used four similarity measurements including Leacock and Chodorow. We conducted two kinds of evaluations: first is calculation of similarity using the varied edge-weighting and second is for the discriminative capability using conceptual distances between comparative concepts. To verify the superiority of SSINEGE, we compared the calculated similarities of SSINEGE with Leacock and Chodorow. As the results, we verified that the calculated similarity of SSINEGE is significantly increased than the other comparatives

RDF Schema Based Ubiquitous Healthcare Service Composition

We suggest a service framework and algorithms of provisioning healthcare services in a ubiquitous computing environment. In order to meet customers need we translate the need into relevant goal and repeatedly refine the goal into sub-goals through commonsense knowledge until there are appropriate services for sub-goals and after, employ the services. The results of this research enable integration and interconnection of devices, applications, and functions within the healthcare services. By RDFSs and their interoperability, a ubiquitous healthcare service composition is achieved, and the hidden semantic distances can be measured dynamically.This work was supported by the Korea Science and Engineering Foundation(KOSEF) through the Advanced Information Technology Research Center (AITrc)