scholarly journals Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion

2019 ◽  
Vol 8 (6) ◽  
pp. 254 ◽  
Author(s):  
Peiyuan Qiu ◽  
Jialiang Gao ◽  
Li Yu ◽  
Feng Lu
Keyword(s):  
Large Scale ◽  
Semantic Network ◽  
Average Error ◽  
Knowledge Graph ◽  
Web Resource ◽  
Related Information ◽  
Geographic Knowledge ◽  
Relation Prediction ◽  
Low Dimensional

A Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads to a situation where information extraction systems can hardly detect enough references of geographic information in the massive web resource to be able to build relatively complete GeoKGs. This incompleteness, due to missing geo-entities or geo-relations in GeoKG fact triplets, seriously impacts the performance of GeoKG applications. In this paper, a method with geospatial distance restriction is presented to optimize knowledge embedding for GeoKG completion. This method aims to encode both the semantic information and geospatial distance restriction of geo-entities and geo-relations into a continuous, low-dimensional vector space. Then, the missing facts of the GeoKG can be supplemented through vector operations. Specifically, the geospatial distance restriction is realized as the weights of the objective functions of current translation knowledge embedding models. These optimized models output the optimized representations of geo-entities and geo-relations for the GeoKG’s completion. The effects of the presented method are validated with a real GeoKG. Compared with the results of the original models, the presented method improves the metric Hits@10(Filter) by an average of 6.41% for geo-entity prediction, and the Hits@1(Filter) by an average of 31.92%, for geo-relation prediction. Furthermore, the capacity of the proposed method to predict the locations of unknown entities is validated. The results show the geospatial distance restriction reduced the average error distance of prediction by between 54.43% and 57.24%. All the results support the geospatial distance restriction hiding in the GeoKG contributing to refining the embedding representations of geo-entities and geo-relations, which plays a crucial role in improving the quality of GeoKG completion.

scholarly journals Knowledge Graph Representation via Similarity-Based Embedding

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Zhen Tan ◽  
Xiang Zhao ◽  
Yang Fang ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Large Scale ◽  
Low Complexity ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Continuous Space ◽  
Memory Space ◽  
Representation Method ◽  
Relation Prediction ◽  
Low Dimensional ◽  
Almost All

Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.

scholarly journals Knowledge Graph Completion for the Chinese Text of Cultural Relics Based on Bidirectional Encoder Representations from Transformers with Entity-Type Information

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1168
Author(s):  
Min Zhang ◽  
Guohua Geng ◽  
Sheng Zeng ◽  
Huaping Jia
Keyword(s):  
Chinese Text ◽  
Large Scale ◽  
Semantic Information ◽  
Training Model ◽  
Knowledge Graph ◽  
Deep Model ◽  
The Rich ◽  
Relation Prediction ◽  
Type Information ◽  
Cultural Relics

Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale manually labelled data, which greatly increases manual labour. In order to alleviate the scarcity of labelled data in the field of cultural relics and capture the rich semantic information of entities, this paper proposes a model based on the Bidirectional Encoder Representations from Transformers (BERT) with entity-type information for the knowledge graph completion of the Chinese texts of cultural relics. In this work, the knowledge graph completion task is treated as a classification task, while the entities, relations and entity-type information are integrated as a textual sequence, and the Chinese characters are used as a token unit in which input representation is constructed by summing token, segment and position embeddings. A small number of labelled data are used to pre-train the model, and then, a large number of unlabelled data are used to fine-tune the pre-training model. The experiment results show that the BERT-KGC model with entity-type information can enrich the semantics information of the entities to reduce the degree of ambiguity of the entities and relations to some degree and achieve more effective performance than the baselines in triple classification, link prediction and relation prediction tasks using 35% of the labelled data of cultural relics.

Towards QoS-Inferred Internet

2011 ◽  
pp. 672-688
Author(s):  
Mohamed Boucadair ◽  
Pierre Levis ◽  
Pierrick Morand
Keyword(s):  
Quality Of Service ◽  
Routing Protocol ◽  
Large Scale ◽  
Ip Networks ◽  
Service Availability ◽  
Large Set ◽  
Transport Networks ◽  
Related Information ◽  
End To End

IP networks are the federative transport networks for a large set of emerging services. These services demand hard guarantees in term of the service availability, experienced Quality of Service (QoS) and robustness. Moreover, to be able to reach customers on a large scale, most of these services should be deployed with an Inter-domain scope. In order to meet QoS requirements of these services in an interdomain context, several issues should be solved. This chapter focuses on two issues: provider-to-provider agreements and enhancements to inter-domain routing protocol to convey QoS-related information. A concept called Meta-QoS-Class is introduced together with an enriched version of Border Gateway Protocol. This chapter provides a framework suitable for the promotion of QoS-enabled services with an inter-domain scope: the Parallel Internet. This concept is a viable way for the management of IP resources so as to deliver end-to-end QoS-enabled services.

scholarly journals Understanding Negative Sampling in Knowledge Graph Embedding

2021 ◽  
Vol 12 (1) ◽  
pp. 71-81
Author(s):  
Jing Qian ◽  
Gangmin Li ◽  
Katie Atkinson ◽  
Yong Yue
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Direct Impact ◽  
Dimensional Vector Space ◽  
Dynamic Distribution ◽  
Space Efficiency ◽  
Node Classification ◽  
Low Dimensional

Knowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a low-dimensional vector space, which has made steady progress in recent years. Conventional KGE methods, especially translational distance-based models, are trained through discriminating positive samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks, such as recommendation, link prediction and node classification. We summarize current negative sampling approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches and their characteristics. It is a hope that this review can provide some guidelines for new thoughts about negative sampling in KGE.

scholarly journals Drug Information Service during Covid-19 Pandemic

2021 ◽  
Vol 5 (1) ◽  
pp. 30-37
Author(s):  
Sherly Tandi Arrang ◽  
Reynelda Juliani Sagala ◽  
Dion Notario ◽  
Erlia Anggrainy Sianipar ◽  
Fonny Cokro
Keyword(s):  
Large Scale ◽  
Drug Information ◽  
Information Service ◽  
Self Medication ◽  
Drug Information Service ◽  
Related Information ◽  
Catholic University ◽  
Drug Choice ◽  
The Common

Pharmacists and pharmaceutical personnel need to support improving the people’s quality of life during the covid-19 outbreak by providing pharmaceutical services, one of which is the Drug Information Service (PIO). Many regions in Indonesia, including DKI Jakarta, have placed large-scale social restrictions since March 2020. In connection with that, the Department of Pharmacy of Atma Jaya Catholic University of Indonesia (PSFUAJ) organized an online PIO program. This program aimed to provide the service of Covid-19 drug-related information and education to the community. The PIO was carried out from April to July 2020 via whatsapp, email, and telephone. In addition, health education was provided through e-flyers, published on Instagram @pio_farmasi_uaj. Eighty percent of the questions were submitted via whatsapp, while the rest was sent via email. Most of the questions submitted (80%) were categorized into self-edication. Further examination shows that the common questions were related to drug choice (27.78%). These data indicate that most questioners tend to provide self-medication first and that they will only see a doctor if they have not recovered or experienced any improvement. PSFUAJ will continue running this program and disseminating this program to a broader audience.

scholarly journals Intelligent Interaction with Virtual Geographical Environments Based on Geographic Knowledge Graph

2019 ◽  
Vol 8 (10) ◽  
pp. 428 ◽  
Author(s):  
Bingchuan Jiang ◽  
Liheng Tan ◽  
Yan Ren ◽  
Feng Li
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Question Answering ◽  
Knowledge Graph ◽  
Virtual Geographic Environment ◽  
Geographic Knowledge ◽  
Natural Language Question ◽  
Geographic Environment ◽  
Intelligent Interaction ◽  
Language Question

The core of intelligent virtual geographical environments (VGEs) is the formal expression of geographic knowledge. Its purpose is to transform the data, information, and scenes of a virtual geographic environment into “knowledge” that can be recognized by computer, so that the computer can understand the virtual geographic environment more easily. A geographic knowledge graph (GeoKG) is a large-scale semantic web that stores geographical knowledge in a structured form. Based on a geographic knowledge base and a geospatial database, intelligent interactions with virtual geographical environments can be realized by natural language question answering, entity links, and so on. In this paper, a knowledge-enhanced Virtual geographical environments service framework is proposed. We construct a multi-level semantic parsing model and an enhanced GeoKG for structured geographic information data, such as digital maps, 3D virtual scenes, and unstructured information data. Based on the GeoKG, we propose a bilateral LSTM-CRF (long short-term memory- conditional random field) model to achieve natural language question answering for VGEs and conduct experiments on the method. The results prove that the method of intelligent interaction based on the knowledge graph can bridge the distance between people and virtual environments.

scholarly journals Knowledge Graph Representation with Jointly Structural and Textual Encoding

2017 ◽  
Author(s):  
Jiacheng Xu ◽  
Xipeng Qiu ◽  
Kan Chen ◽  
Xuanjing Huang
Keyword(s):  
Graph Representation ◽  
Knowledge Graph ◽  
Neural Models ◽  
Structure Information ◽  
Related Information ◽  
Gating Mechanism ◽  
Deep Architecture ◽  
Classification Tasks ◽  
Low Dimensional ◽  
Representation Of Knowledge

The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline and obtain state-of-the-art results on link prediction and triplet classification tasks.

scholarly journals A Novel Negative Sampling Based on Frequency of Relational Association Entities for Knowledge Graph Embedding

2021 ◽  
Author(s):  
Wanhua Cao ◽  
Yi Zhang ◽  
Juntao Liu ◽  
Ziyun Rao
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Evaluation Criteria ◽  
Relation Extraction ◽  
Graph Embedding ◽  
Semantic Space ◽  
Knowledge Graph ◽  
Knowledge Reasoning ◽  
Relation Prediction ◽  
Low Dimensional

Knowledge graph embedding improves the performance of relation extraction and knowledge reasoning by encoding entities and relationships in low-dimensional semantic space. During training, negative samples are usually constructed by replacing the head/tail entity. And the different replacing relationships lead to different accuracy of the prediction results. This paper develops a negative triplets construction framework according to the frequency of relational association entities. The proposed construction framework can fully consider the quantitative of relations and entities in the dataset to assign the proportion of relation and entity replacement and the frequency of the entities associated with each relationship to set reasonable proportions for different relations. To verify the validity of the proposed construction framework, it is integrated into the state-of-the-art knowledge graph embedding models, such as TransE, TransH, DistMult, ComplEx, and Analogy. And both the evaluation criteria of relation prediction and entity prediction are used to evaluate the performance of link prediction more comprehensively. The experimental results on two commonly used datasets, WN18 and FB15K, show that the proposed method improves entity link and triplet classification accuracy, especially the accuracy of relational link prediction.

scholarly journals Drug-Drug Interactions Prediction via Knowledge Graph and Text Embedding (Preprint)

2021 ◽  
Author(s):  
Meng Wang ◽  
Haofen Wang ◽  
Xing Liu ◽  
Xinyu Ma ◽  
Beilun Wang
Keyword(s):  
Drug Interactions ◽  
Large Scale ◽  
Dimensional Space ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Drug Knowledge ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Different Sources ◽  
Opening Up

UNSTRUCTURED Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs and ignore other interactions. In this paper, we propose a novel framework, called PRD, to predict drug-drug interactions. The framework uses the graph embedding that can overcome data incompleteness and sparsity issues to achieve multiple DDI label prediction. First, a large-scale drug knowledge graph is generated from different sources. Then, the knowledge graph is embedded with comprehensive biomedical text into a common low dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world datasets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.

scholarly journals Dual Quaternion Embeddings for Link Prediction

2021 ◽  
Vol 11 (12) ◽  
pp. 5572
Author(s):  
Liming Gao ◽  
Huiling Zhu ◽  
Hankz Hankui Zhuo ◽  
Jin Xu 
Keyword(s):  
Artificial Intelligence ◽  
Link Prediction ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Dual Quaternion ◽  
Novel Approach ◽  
Quaternion Space ◽  
Knowledge Graphs ◽  
Low Dimensional

The applications of knowledge graph have received much attention in the field of artificial intelligence. The quality of knowledge graphs is, however, often influenced by missing facts. To predict the missing facts, various solid transformation based models have been proposed by mapping knowledge graphs into low dimensional spaces. However, most of the existing transformation based approaches ignore that there are multiple relations between two entities, which is common in the real world. In order to address this challenge, we propose a novel approach called DualQuatE that maps entities and relations into a dual quaternion space. Specifically, entities are represented by pure quaternions and relations are modeled based on the combination of rotation and translation from head to tail entities. After that we utilize interactions of different translations and rotations to distinguish various relations between head and tail entities. Experimental results exhibit that the performance of DualQuatE is competitive compared to the existing state-of-the-art models.

Sign in / Sign up

Export Citation Format

Share Document