Knowledge Graph-Enhanced Blockchains by Integrating a Graph-Data Service-Layer

2019 ◽  
Author(s):  
Belal Abu Naim ◽  
Wolfgang Klas
Keyword(s):  
Knowledge Graph ◽  
Data Service ◽  
Graph Data

scholarly journals Mapping Manuscript Migrations Knowledge Graph: Data for Tracing the History and Provenance of Medieval and Renaissance Manuscripts

2020 ◽  
Vol 6 ◽  
Author(s):  
Toby Burrows ◽  
Doug Emery ◽  
Mitch Fraas ◽  
Eero Hyvönen ◽  
Esko Ikkala ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Graph Data

Discover Relations in the Industry 4.0 Standards Via Unsupervised Learning on Knowledge Graph Embeddings

2021 ◽  
Vol 2 (3) ◽  
pp. 336-347
Author(s):  
Ariam Rivas ◽  
Irlan Grangel-Gonzalez ◽  
Diego Collarana ◽  
Jens Lehmann ◽  
Maria-esther Vidal
Keyword(s):  
Data Model ◽  
Industry 4.0 ◽  
Knowledge Graph ◽  
Similarity Metrics ◽  
Graph Embeddings ◽  
Graph Data ◽  
Detection Algorithms ◽  
Unsupervised Approach ◽  
Main Components ◽  
Smart Factories

Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, and processes inside a smart factory and the interaction among all of them. Furthermore, standardization frameworks classify standards according to their functions into layers and dimensions. Albeit informative, frameworks can categorize similar standards differently. As a result, interoperability conflicts are generated whenever smart factories are described with miss-classified standards. Approaches like ontologies and knowledge graphs enable the integration of standards and frameworks in a structured way. They also encode the meaning of the standards, known relations among them, as well as their classification according to existing frameworks. This structured modeling of the I4.0 landscape using a graph data model provides the basis for graph-based analytical methods to uncover alignments among standards. This paper contributes to analyzing the relatedness among standards and frameworks; it presents an unsupervised approach for discovering links among standards. The proposed method resorts to knowledge graph embeddings to determine relatedness among standards-based on similarity metrics. The proposed method is agnostic to the technique followed to create the embeddings and to the similarity measure. Building on the similarity values, community detection algorithms can automatically create communities of highly similar standards. Our approach follows the homophily principle, and assumes that related standards are together in a community. Thus, alignments across standards are predicted and interoperability issues across them are solved. We empirically evaluate our approach on a knowledge graph of 249 I4.0 standards using the Trans$^*$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.

scholarly journals Power System Network Topology Identification Based on Knowledge Graph and Graph Neural Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Changgang Wang ◽  
Jun An ◽  
Gang Mu
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Network Topology ◽  
Fault Tolerant ◽  
Power Grids ◽  
Knowledge Graph ◽  
Automatic Identification ◽  
Quality Of Data ◽  
Graph Data ◽  
Topology Identification

The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.

scholarly journals Process Knowledge Graph Construction Method for Process Reuse

2019 ◽  
Vol 37 (6) ◽  
pp. 1174-1183
Author(s):  
Xiuling Li ◽  
Shusheng Zhang ◽  
Rui Huang ◽  
Bo Huang ◽  
Sijia Wang
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Construction Method ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Process Knowledge ◽  
Graph Data ◽  
Data Schema ◽  
Knowledge Analysis ◽  
Graph Schema

Aiming at the problems of process knowledge reuse and sharing led by the difficulty of unified representation of complex and diverse process knowledge, a process knowledge graph construction method for process reuse is proposed. Firstly, to ensure the accuracy and universality of data schema for process knowledge, the basic schema of process knowledge graph is constructed based on step-nc. Secondly, to improve process knowledge graph basic schema, the process knowledge graph extensive schema is established through process knowledge analysis and process knowledge combination. Meanwhile, to construct the process knowledge graph schema, the existing rules of experience are represented by SWRL language. Moreover, to instantiate the process knowledge graph schema, the process cases are analyzed under the guidance of process knowledge graph schema and the similarity between process cases is computed by latent semantic analysis technology. And then, process cases are transformed into the structured process knowledge graph representation, and process knowledge graph data is obtained. Finally, the process knowledge graph construction application platform is developed to verify the feasibility of the proposed method.

scholarly journals Parlamenttisampo: eduskunnan aineistojen linkitetyn avoimen datan palvelu ja sen käyttömahdollisuudet

2021 ◽  
Vol 40 (3) ◽  
Author(s):  
Eero Hyvönen ◽  
Laura Sinikallio ◽  
Petri Leskinen ◽  
Senka Drobac ◽  
Jouni Tuominen ◽  
...  
Keyword(s):  
Open Data ◽  
Linked Open Data ◽  
Knowledge Graph ◽  
Graph Data

Semanttinen parlamentti -hankkeessa 2020–2022 luodaan eduskunnan tietokannoista ja niihin liittyvistä muista aineistoista uudenlainen linkitetyn avoimen datan (Linked Open Data, LOD) palvelu, tietoinfrastruktuuri ja semanttinen portaali Parlamenttisampo – eduskunta semant­tisessa webissä, joiden avulla tutkitaan poliittista kulttuuria ja kieltä. Dataa linkittämällä voi-daan rikastaa eduskuntadataa muilla tietolähteillä kuten biografisella tiedolla, terminologioilla ja lainsäädännön dokumenteilla. Parlamenttisampo on kieli- ja semanttisen webin teknologioihin perustuva palvelukokonaisuus tutkijoita, kansalaisia, mediaa ja valtionhallintoa varten. Artik­kelissa esitellään hankkeen visio, ensimmäisiä tuloksia ja niiden hyödyntämismahdollisuuksia: Eduskunnan kaikkien täysistuntojen 1907–2021 yli 900 000 puheesta on valmistunut linkitetyn datan tietämysgraafi (knowledge graph); data on myös saatavilla XML-muodossa, jossa hyö­dynnetään uutta kansainvälistä Parla-CLARIN-formaattia. Ensimmäistä kertaa eduskunnan puheiden koko aikasarja on muunnettu dataksi ja datapalveluksi yhtenäisessä muodossa. Lisäksi puheet on yhdistetty eduskunnan kansanedustajien tietokannasta luotuun ja muista tietolähteistä rikastettuun toiseen tietämysgraafiin laajemmaksi ontologiaperustaiseksi datapalveluksi Fin- Parla. Datapalvelua voidaan käyttää eduskuntatutkimukseen parlamentaarisesta ja edustuksel-lisesta kulttuurista sekä poliittisen kielen käytöstä analysoimalla kansanedustajien täysistunnois­sa pitämiä puheita ja poliitikkojen verkostoja data-analyysin keinoin. Palvelun rajapinnan avulla voidaan myös kehittää eri käyttäjäryhmille sovelluksia, kuten hankkeessa valmistuva Parlament­tisampo.fi-portaali.

scholarly journals Data Poisoning Attack against Knowledge Graph Embedding

2019 ◽  
Author(s):  
Hengtong Zhang ◽  
Tianhang Zheng ◽  
Jing Gao ◽  
Chenglin Miao ◽  
Lu Su ◽  
...  
Keyword(s):  
Question Answering ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Graph Data ◽  
Effectiveness And Efficiency ◽  
Significant Attention

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.

scholarly journals From Tabulated Data to Knowledge Graph: A Novel Way of Improving the Performance of the Classification Models in the Healthcare Data

2021 ◽  
Author(s):  
Nazar Zaki ◽  
Elfadil Abdalla Mohamed ◽  
Tetiana Habuza
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Data Analysis ◽  
Knowledge Graph ◽  
Classification Models ◽  
Effective Technique ◽  
Graph Data ◽  
Healthcare Data ◽  
Suggested Technique ◽  
Tabulated Data

In sectors like healthcare, having classification models that are both reliable and accurate is vital. Regrettably, contemporary classification techniques employing machine learning disregard the correlations between instances within data. This research, to rectify this, introduces a basic but effective technique for converting tabulated data into data graphs, incorporating structural correlations. Graphs have a unique capacity to capture structural correlations between data, allowing us to gain a deeper insight in comparison to carrying out isolated data analysis. The suggested technique underwent testing once the integration of graph data structure-related elements had been carried out and returned superior results to testing solely employing original features. The suggested technique achieved validity by returning significantly improved levels of accuracy.

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