Demonstrating Spindra: A Geographic Knowledge Graph Management System

2019 ◽  
Author(s):  
Yuhan Sun ◽  
Jia Yu ◽  
Mohamed Sarwat
Keyword(s):  
Management System ◽  
Knowledge Graph ◽  
Geographic Knowledge ◽  
Graph Management

scholarly journals Swift Logic for Big Data and Knowledge Graphs

2017 ◽  
Author(s):  
Luigi Bellomarini ◽  
Georg Gottlob ◽  
Andreas Pieris ◽  
Emanuel Sallinger
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computational Complexity ◽  
Management System ◽  
Knowledge Graph ◽  
Complex Reasoning ◽  
Knowledge Graphs ◽  
Reasoning Tasks ◽  
Graph Management ◽  
The Web

Many modern companies wish to maintain knowledge in the form of a corporate knowledge graph and to use and manage this knowledge via a knowledge graph management system (KGMS). We formulate various requirements for a fully fledged KGMS. In particular, such a system must be capable of performing complex reasoning tasks but, at the same time, achieve efficient and scalable reasoning over Big Data with an acceptable computational complexity. Moreover, a KGMS needs interfaces to corporate databases, the web, and machine-learning and analytics packages. We present KRR formalisms and a system achieving these goals.

Shipping Knowledge Graph Management Capabilities to Data Providers and Consumers

2018 ◽  
Author(s):  
Omar Al-Safi ◽  
Christian Mader ◽  
Ioanna Lytra ◽  
Mikhail Galkin ◽  
Kemele Endris ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Graph Management ◽  
Management Capabilities

scholarly journals Conciliating Perspectives from Mapping Agencies and Web of Data on Successful European SDIs: Toward a European Geographic Knowledge Graph

2020 ◽  
Vol 9 (2) ◽  
pp. 62 ◽  
Author(s):  
Bénédicte Bucher ◽  
Esa Tiainen ◽  
Thomas Ellett von Brasch ◽  
Paul Janssen ◽  
Dimitris Kotzinos ◽  
...  
Keyword(s):  
Spatial Data ◽  
Geographical Information ◽  
Knowledge Graph ◽  
Geographic Knowledge ◽  
Computer Scientists ◽  
Web Of Data ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
The One ◽  
The Web

Spatial Data Infrastructures (SDIs) are a key asset for Europe. This paper concentrates on unsolved issues in SDIs in Europe related to the management of semantic heterogeneities. It studies contributions and competences from two communities in this field: cartographers, authoritative data providers, and geographic information scientists on the one hand, and computer scientists working on the Web of Data on the other. During several workshops organized by the EuroSDR and Eurogeographics organizations, the authors analyzed their complementarity and discovered reasons for the difficult collaboration between these communities. They have different and sometimes conflicting perspectives on what successful SDIs should look like, as well as on priorities. We developed a proposal to integrate both perspectives, which is centered on the elaboration of an open European Geographical Knowledge Graph. Its structure reuses results from the literature on geographical information ontologies. It is associated with a multifaceted roadmap addressing interrelated aspects of SDIs.

The Grasper-CL graph management system

1994 ◽  
Vol 7 (4) ◽  
pp. 251-290 ◽  
Author(s):  
Peter D. Karp ◽  
John D. Lowrance ◽  
Thomas M. Strat ◽  
David E. Wilkins
Keyword(s):  
Management System ◽  
Graph Management

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 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 WorldKG: A World-Scale Geographic Knowledge Graph

2021 ◽  
Author(s):  
Alishiba Dsouza ◽  
Nicolas Tempelmeier ◽  
Ran Yu ◽  
Simon Gottschalk ◽  
Elena Demidova
Keyword(s):  
Knowledge Graph ◽  
Geographic Knowledge
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