Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding

Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results

Promocijas darbs

Elektroniskā versija nesatur pielikumusDarbā izstrādātas oriģinālas metodes, kas ļauj vizuālus uz paplašinātām UML veida grafu diagrammām balstītus rīkus izmantot praktisku ontoloģiju un semantisko datu vaicājumu veidošanai un attēlošanai. OWL ontoloģiju vizuālas modelēšanas jomā izveidoti līdzekļi konkrētam lietojumam specifiskas notācijas uzdošanai un izmantošanai, tādi ka: mehānisms lietotāja definētu notāciju uzdošanai, ontoloģiju vizualizācijas parametru ietvars, ontoloģiju eksporta modulis un uz gramatikām balstīta priekšāteikšanas metode. Darbā piedāvāts risinājums vizuālai bagātīgu datu vaicājumu veidošanai pār RDF datubāzēm, un to translēšanai uz tekstuālu SPARQL valodu, kurā pierakstītie vaicājumi var tikt tieši izpildīti pār RDF datu bāzēm. Atslēgvārdi: OWL, OWLGrEd, teksta priekšāteicējs, domēnspecifiska ontoloģiju attēlošana, SPARQL, vizuāli vaicājumi, ViziQuerThe doctoral thesis develops original methods that allow visual tools that are based on extended UML-style graph diagrams to be used for creating and visualising practical ontologies and semantic data queries. In the field of visual modeling of OWL ontologies, tools have been developed for creating modeling notations specific to particular applications, such as a mechanism for creating user-defined notations, a framework for ontology visualisation parameters, an ontology export module and a grammar-based auto-completion method. The doctoral thesis presents a solution for the visual formulation of rich data queries over RDF databases, and their translation into the standard textual SPARQL query language. Keywords: OWL, OWLGrEd, text auto-completion, Domain-Specific Ontology Representation, SPARQL, Visual Queries, ViziQuer

Knowledge graph enabled curation and exploration of Nuremberg’s city heritage

An important part in European cultural identity relies on European cities and in particular on their histories and cultural heritage. Nuremberg, the home of important artists such as Albrecht Dürer and Hans Sachs developed into the epitome of German and European culture already during the Middle Ages. Throughout history, the city experienced a number of transformations, especially with its almost complete destruction during World War 2. This position paper presents TRANSRAZ, a project with the goal to recreate Nuremberg by means of an interactive 3D tool to explore the city’s architecture and culture ranging from the 17th to the 21st century. The goal of this position paper is to discuss the ongoing work of connecting heterogeneous historical data from various sources previously hidden in archives to the 3D model using knowledge graphs for a scientifically accurate interactive exploration on the Web

Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents \textit{GRAND}, a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions. RDF2vec first generates graph walks and then uses a language model to obtain embeddings for each node in the graph. This study shows that the walk generation strategy and the embedding model have a significant effect on the performance of the entity typing task. The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes. The results show that the combination of order-aware RDF2vec variants together with the contextual embeddings of the textual entity descriptions achieve the best results

Пополнение онтологических систем знаний на основе моделирования умозаключений с учетом семантики ролей

The article considers the issue of automatic completion of ontology with roles and concepts formed by an intelligent system in the provision of new facts. Implementation of specified calculations allows increasing ontology information content during data stream preprocessing.В статье рассмотрен вопрос, связанный с автоматическим пополнением онтологии ролями и концептами, формируемыми интеллектуальной системой, при предоставлении ей новых фактов. Осуществление указанных предвычислений позволяет повысить информационное содержание онтологии на этапе предварительной обработки потока поступающих данных

Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.Comment: Paper accepted at ISWC2023 In-Use trac

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.Comment: Daniel Bogdoll and Jonas Hendl contributed equally. Accepted for publication at ICCRE 202