scholarly journals Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion

2021 ◽  
Vol 21 (S9) ◽  
Author(s):  
Yinyu Lan ◽  
Shizhu He ◽  
Kang Liu ◽  
Xiangrong Zeng ◽  
Shengping Liu ◽  
...  
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Semantic Representation ◽  
Medical Knowledge ◽  
The State ◽  
Language Models ◽  
Knowledge Graph ◽  
Knowledge Reasoning ◽  
Numerical Computing ◽  
Knowledge Graphs

Abstract Background Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. Methods To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. Results Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. Conclusions In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

High-Quality Knowledge Graphs Generation: R2RML and RML Comparison, Rules Validation and Inconsistency Resolution

2020 ◽  
Author(s):  
Anastasia Dimou
Keyword(s):  
State Of The Art ◽  
The State ◽  
Knowledge Graph ◽  
High Quality ◽  
Inconsistency Resolution ◽  
Knowledge Graphs ◽  
Rdf Graphs ◽  
Graph Generation

In this chapter, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences and explain how knowledge graphs, in the form of RDF graphs, can be generated with each one of the two mapping languages. Then we assess if the vocabulary terms were properly applied to the data and no violations occurred on their use, either using R2RML or RML to generate the desired knowledge graph.

scholarly journals End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion

2019 ◽  
Vol 33 ◽  
pp. 3060-3067 ◽  
Author(s):  
Chao Shang ◽  
Yun Tang ◽  
Jing Huang ◽  
Jinbo Bi ◽  
Xiaodong He ◽  
...  
Keyword(s):  
Knowledge Base ◽  
State Of The Art ◽  
The State ◽  
Graph Connectivity ◽  
Knowledge Graph ◽  
Graph Node ◽  
Convolutional Network ◽  
Node Attributes ◽  
Knowledge Graphs ◽  
End To End

Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-theart ConvE in terms of HITS@1, HITS@3 and HITS@10.

scholarly journals A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development (Preprint)

2019 ◽  
Author(s):  
Linfeng Li ◽  
Peng Wang ◽  
Yao Wang ◽  
Shenghui Wang ◽  
Jun Yan ◽  
...  
Keyword(s):  
Medical Records ◽  
Large Scale ◽  
Semantic Representation ◽  
Medical Knowledge ◽  
Mapping Function ◽  
Graph Algorithm ◽  
Knowledge Graph ◽  
Knowledge Graphs ◽  
Representation Method ◽  
Better Than

BACKGROUND Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. OBJECTIVE We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. METHODS We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. RESULTS Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. CONCLUSIONS The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.

scholarly journals A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development

10.2196/17645 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e17645
Author(s):  
Linfeng Li ◽  
Peng Wang ◽  
Yao Wang ◽  
Shenghui Wang ◽  
Jun Yan ◽  
...  
Keyword(s):  
Medical Records ◽  
Large Scale ◽  
Semantic Representation ◽  
Medical Knowledge ◽  
Mapping Function ◽  
Graph Algorithm ◽  
Knowledge Graph ◽  
Knowledge Graphs ◽  
Representation Method ◽  
Better Than

Background Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. Objective We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. Methods We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. Results Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. Conclusions The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

2020 ◽  
Vol 34 (05) ◽  
pp. 9612-9619
Author(s):  
Zhao Zhang ◽  
Fuzhen Zhuang ◽  
Hengshu Zhu ◽  
Zhiping Shi ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Values ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Knowledge Graph ◽  
Incomplete Knowledge ◽  
Neighborhood Information ◽  
Local Neighborhood ◽  
Proposed Model ◽  
Knowledge Graphs

The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.

scholarly journals R-KG: A Novel Method for Implementing a Robot Intelligent Service

AI ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 117-140 ◽  
Author(s):  
Wu Hao ◽  
Jiao Menglin ◽  
Tian Guohui ◽  
Ma Qing ◽  
Liu Guoliang
Keyword(s):  
Semantic Information ◽  
Environmental Information ◽  
Knowledge Graph ◽  
Markov Logic ◽  
Main Work ◽  
Knowledge Reasoning ◽  
Novel Method ◽  
Markov Logic Network ◽  
The Relationship ◽  
Different Sources

Aiming to solve the problem of environmental information being difficult to characterize when an intelligent service is used, knowledge graphs are used to express environmental information when performing intelligent services. Here, we specially design a kind of knowledge graph for environment expression referred to as a robot knowledge graph (R-KG). The main work of a R-KG is to integrate the diverse semantic information in the environment and pay attention to the relationship at the instance level. Also, through the efficient knowledge organization of a R-KG, robots can fully understand the environment. The R-KG firstly integrates knowledge from different sources to form a unified and standardized representation of a knowledge graph. Then, the deep logical relationship hidden in the knowledge graph is explored. To this end, a knowledge reasoning model based on a Markov logic network is proposed to realize the self-developmental ability of the knowledge graph and to further enrich it. Finally, as the strength of environment expression directly affects the efficiency of robots performing services, in order to verify the efficiency of the R-KG, it is used here as the semantic map that can be directly used by a robot for performing intelligent services. The final results prove that the R-KG can effectively express environmental information.

scholarly journals Appearance and Motion Enhancement for Video-Based Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 11394-11401
Author(s):  
Shuzhao Li ◽  
Huimin Yu ◽  
Haoji Hu
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Complex Model ◽  
The State ◽  
Final Model ◽  
Backbone Network ◽  
Proposed Model ◽  
Art Performance ◽  
Attribute Recognition

In this paper, we propose an Appearance and Motion Enhancement Model (AMEM) for video-based person re-identification to enrich the two kinds of information contained in the backbone network in a more interpretable way. Concretely, human attribute recognition under the supervision of pseudo labels is exploited in an Appearance Enhancement Module (AEM) to help enrich the appearance and semantic information. A Motion Enhancement Module (MEM) is designed to capture the identity-discriminative walking patterns through predicting future frames. Despite a complex model with several auxiliary modules during training, only the backbone model plus two small branches are kept for similarity evaluation which constitute a simple but effective final model. Extensive experiments conducted on three popular video-based person ReID benchmarks demonstrate the effectiveness of our proposed model and the state-of-the-art performance compared with existing methods.

scholarly journals A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Space ◽  
Knowledge Graph ◽  
High Quality ◽  
Gaussian Distributions ◽  
Benchmark Datasets ◽  
Semantic Spaces ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.

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