scholarly journals A Novel Encoder-Decoder Knowledge Graph Completion Model for Robot Brain

2021 ◽  
Vol 15 ◽  
Author(s):  
Yichen Song ◽  
Aiping Li ◽  
Hongkui Tu ◽  
Kai Chen ◽  
Chenchen Li
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Rapid Development ◽  
Representation Learning ◽  
High Tech ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Human Beings ◽  
Benchmark Datasets ◽  
The Brain

With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.

scholarly journals Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

2020 ◽  
Vol 34 (03) ◽  
pp. 3065-3072 ◽  
Author(s):  
Zhanqiu Zhang ◽  
Jianyu Cai ◽  
Yongdong Zhang ◽  
Jie Wang
Keyword(s):  
Coordinate System ◽  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Polar Coordinate System ◽  
Radial Coordinate ◽  
Polar Coordinate ◽  
Benchmark Datasets ◽  
Knowledge Graphs ◽  
Semantic Hierarchies

Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model—namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE)—which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.

scholarly journals A Survey on Knowledge Graph Embeddings for Link Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 485
Author(s):  
Meihong Wang ◽  
Linling Qiu ◽  
Xiaoli Wang
Keyword(s):  
Language Processing ◽  
Link Prediction ◽  
Knowledge Graph ◽  
Practical Utilization ◽  
Complete Knowledge ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Knowledge Graphs ◽  
Representation Techniques ◽  
Open Nature

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete, having self-defects. This creates the need to build a more complete knowledge graph for enhancing the practical utilization of KGs. Link prediction is a fundamental task in knowledge graph completion that utilizes existing relations to infer new relations so as to build a more complete knowledge graph. Numerous methods have been proposed to perform the link-prediction task based on various representation techniques. Among them, KG-embedding models have significantly advanced the state of the art in the past few years. In this paper, we provide a comprehensive survey on KG-embedding models for link prediction in knowledge graphs. We first provide a theoretical analysis and comparison of existing methods proposed to date for generating KG embedding. Then, we investigate several representative models that are classified into five categories. Finally, we conducted experiments on two benchmark datasets to report comprehensive findings and provide some new insights into the strengths and weaknesses of existing models.

scholarly journals Caps-OWKG: a capsule network model for open-world knowledge graph

2021 ◽  
Author(s):  
Yuhan Wang ◽  
Weidong Xiao ◽  
Zhen Tan ◽  
Xiang Zhao
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Relational Structures ◽  
Open World ◽  
Latent Features ◽  
Knowledge Graphs ◽  
Low Dimensional ◽  
Better Than

AbstractKnowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.

Embodying the Number of an Entity’s Relations for Knowledge Representation Learning

2021 ◽  
Vol 31 (10) ◽  
pp. 1495-1515
Author(s):  
Xinhua Suo ◽  
Bing Guo ◽  
Yan Shen ◽  
Wei Wang ◽  
Yaosen Chen ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Visual Information ◽  
Large Scale ◽  
Critical Role ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Source Information ◽  
Structure Information ◽  
Common Information ◽  
Additional Information

Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information — the number of an entity’s relations which means the number of an entity’s semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity’s relations between entities into the entities’ embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity’s relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.

scholarly journals Scalable Person Re-Identification by Harmonious Attention

2019 ◽  
Vol 128 (6) ◽  
pp. 1635-1653 ◽  
Author(s):  
Wei Li ◽  
Xiatian Zhu ◽  
Shaogang Gong
Keyword(s):  
Large Scale ◽  
Feature Matching ◽  
Representation Learning ◽  
Video Data ◽  
Feature Representation ◽  
Surveillance Video ◽  
Deep Feature ◽  
Benchmark Datasets ◽  
Attention Selection ◽  
The Cost

AbstractExisting person re-identification (re-id) deep learning methods rely heavily on the utilisation of large and computationally expensive convolutional neural networks. They are therefore not scalable to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we address this limitation via jointly learning re-id attention selection. Specifically, we formulate a novel harmonious attention network (HAN) framework to jointly learn soft pixel attention and hard region attention alongside simultaneous deep feature representation learning, particularly enabling more discriminative re-id matching by efficient networks with more scalable model inference and feature matching. Extensive evaluations validate the cost-effectiveness superiority of the proposed HAN approach for person re-id against a wide variety of state-of-the-art methods on four large benchmark datasets: CUHK03, Market-1501, DukeMTMC, and MSMT17.

A quaternion-group knowledge graph embedding model

2021 ◽  
pp. 1-10
Author(s):  
Heng Chen ◽  
Guanyu Li ◽  
Yunhao Sun ◽  
Wei Jiang
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Complex Vector ◽  
Quaternion Group ◽  
Rotation Matrices ◽  
Group Knowledge ◽  
Benchmark Datasets ◽  
Complete Relation

Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: (1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations.

scholarly journals Knowledge-driven drug repurposing using a comprehensive drug knowledge graph

2020 ◽  
Vol 26 (4) ◽  
pp. 2737-2750 ◽  
Author(s):  
Yongjun Zhu ◽  
Chao Che ◽  
Bo Jin ◽  
Ningrui Zhang ◽  
Chang Su ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Drug Repurposing ◽  
Data Representation ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Multiple Drug ◽  
Treatment Information ◽  
Drug Knowledge

Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.

scholarly journals SCOPE OF SPEECH LANGUAGE THERAPY

2017 ◽  
Vol 6 (1) ◽  
pp. 1-2
Author(s):  
Amina Asif Siddiqui
Keyword(s):  
Cognitive Skills ◽  
Right Hemisphere ◽  
Language Disorder ◽  
Rapid Development ◽  
Cerebral Dominance ◽  
Early Years ◽  
Human Beings ◽  
Language Therapy ◽  
The Brain

The consistent indulgence of the developed world in neurological research, during the post second world war era, led to several discoveries and subsequently the laying down of several theories about socio-communicative behaviors in human beings. The areas of the brain responsible for the functions of speech and language were studied further, providing an insight to the role of the brain in cognitive skills of attention, recall, memory, analytical and inferential skills, as well as judgment, necessary during verbal and nonverbal communication. Information about cerebral dominance for language, which lies in the left hemisphere for a larger majority of people; led to establishing the role of the right hemisphere for the supra-segmental features of language, or the meaning of spoken utterances carried by the rhythm in our speech. Thus ‘aphasiology’ emerged as the ‘study of aphasia’ which is an acquired language disorder that results from an insult to the brain, mostly in adults who have used language in the best possible way through their lives; but may also be seen in children known as ‘childhood aphasia.’ The period and pattern of recovery from aphasia varied greatly in adults and children, which led to establishing the theory of ‘critical period’ for language development, shedding more light on the miraculous development and functioning of the human neurological system since birth, for language. Knowledge about the rapid development of the human brain in the early years of life led to the essential concept of ‘early intervention’ of children having any communicative or physical challenges

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