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Explainable Deep RDFS Reasoner [article]

Bassem Makni, Ibrahim Abdelaziz, James Hendler
2020 arXiv   pre-print
Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules.  ...  In the graph words approach, RDF graphs are represented as a sequence of graph words where inference can be achieved through neural machine translation.  ...  Unfortunately, deep learning based reasoners suffer from a problem that is common for deep learning approaches in general: the lack of explainability.  ... 
arXiv:2002.03514v1 fatcat:jrsdnemcwbhurp5vjpanimx5im

Skip Vectors for RDF Data: Extraction Based on the Complexity of Feature Patterns [article]

Yota Minami, Ken Kaneiwa
2022 arXiv   pre-print
Machine learning tasks for RDF graphs adopt three methods: (i) support vector machines (SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational graph convolutional networks.  ...  In our evaluation experiments with RDF data, such as Wikidata, DBpedia, and YAGO, we compare our method with RDF graph kernels in an SVM.  ...  Machine learning tasks for such RDF data adopt three methods: (i) support vector machines (SVMs) with RDF graph kernels, (ii) RDF graph embeddings, and (iii) relational graph convolutional networks (R-GCNs  ... 
arXiv:2201.01996v3 fatcat:olqyuxmlkjesxmqvzewcjiwfxq

RDF Graph Visualization by Interpreting Linked Data as Knowledge [chapter]

Rathachai Chawuthai, Hideaki Takeda
2016 Lecture Notes in Computer Science  
As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be  ...  These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users.  ...  This research aims to offer an approach to the presentation of RDF graph visualization as a learning tool by interpreting RDF data as knowledge structures.  ... 
doi:10.1007/978-3-319-31676-5_2 fatcat:qzh7uc73ojakbkgrhha4yrbbrq

Querying massive RDF data using Spark

Mouad Banane, Hassan II University, Morocco
2019 International Journal of Advanced Trends in Computer Science and Engineering  
In this paper, we propose a new solution based on Apache Spark for massive querying and RDF data.  ...  With the increase in RDF data volumes available, many research efforts have been made to allow for distributed and efficient evaluation of SPARQL queries.  ...  flow and offers machine learning and graph-oriented processing functions.  ... 
doi:10.30534/ijatcse/2019/68842019 fatcat:tu3btrw63ba2rfdu3iz6btquuu

Continuous support for rehabilitation using machine learning

Patrick Philipp, Nicole Merkle, Kai Gand, Carola Gißke
2019 it - Information Technology  
Therefore, we will explain the use case of patient rehabilitation at home, the basic challenges in this field and machine learning applications that could address these challenges by technical means.  ...  Thus, we need computer-assisted patient rehabilitation, which monitors the fitness of the current patient plan to detect sub-optimality, proposes personalised changes for a patient and eventually generalizes  ...  Learning with knowledge modelling There are two general ways to exploit RDF-based data for machine learning. The first option is referred to as vocabulary-based semantics.  ... 
doi:10.1515/itit-2019-0022 fatcat:smdq4ahj3jdhdg3dlaurynnmz4

Stable multi-label boosting for image annotation with structural feature selection

YueTing Zhuang, YaHong Han, Fei Wu, JiaCheng Yang
2011 Science China Information Sciences  
Chu • Research on knowledge base, RDF and data mining Research on spatial database, spatial objects indexing and accessing • Research on machine learning and its application to multimedia content analysis  ...  Develop a RDF engine on a distributed graph database to host one of Microsoft's billion-triple RDF knowledgebases • Design query plans for RDF query processing in distributed graph databases PhenoMining  ... 
doi:10.1007/s11432-011-4483-5 fatcat:xoocx6alfjbftjxxg6c6wsmnfy

Application of Clustering to Analyze Academic Social Networks

Sobha Rani K, Raju KVSVN, V.Valli Kumari
2013 International journal of Web & Semantic Technology  
When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis.  ...  We have chosen academic social networks and clustered nodes to simplify network analysis.  ...  The design objective of FOAF is to allow integration of data across different applications. The project is based on machine readable web home pages for people, companies and other data.  ... 
doi:10.5121/ijwest.2013.4202 fatcat:cv7ifutow5egjex3kf4j3kl5uq

RDFFrames: Knowledge Graph Access for Machine Learning Tools [article]

Aisha Mohamed, Ghadeer Abuoda, Abdurrahman Ghanem, Zoi Kaoudi, Ashraf Aboulnaga
2021 arXiv   pre-print
Knowledge graphs represented as RDF datasets are integral to many machine learning applications.  ...  Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system.  ...  RELATED WORK Data Preparation for Machine Learning.  ... 
arXiv:2002.03614v4 fatcat:ywl7mswhvng7vogmgx7jeznoyi

RDF2Vec: RDF Graph Embeddings for Data Mining [chapter]

Petar Ristoski, Heiko Paulheim
2016 Lecture Notes in Computer Science  
Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature  ...  in RDF graphs.  ...  [12] introduce two general RDF graph kernels, based on intersection graphs and intersection trees. Later, the intersection tree path kernel was simplified by Vries et al. [33] .  ... 
doi:10.1007/978-3-319-46523-4_30 fatcat:hvt25fbunrcr7hqeuuxlnqxbim

Graph Data on the Web: extend the pivot, don't reinvent the wheel [article]

Fabien Gandon, Franck Michel, Andrea Tettamanzi, Elena Cabrio
2019 arXiv   pre-print
Wimmics stands for Web-Instrumented Man-Machine Interactions, Communities, and Semantics.  ...  The following sections group motivations for different directions of work and collect reasons for the creation of a working group on RDF 2.0 and other recommendations of the RDF family.  ...  , machine learning) to assess whether a URI can be dereferenced to RDF content.  ... 
arXiv:1903.04181v1 fatcat:3hasyse2k5hk7mdf5qeplk47uq

Machine intelligence today: applications, methodology, and technology

Bernhard G. Humm, Hermann Bense, Michael Fuchs, Benjamin Gernhardt, Matthias Hemmje, Thomas Hoppe, Lukas Kaupp, Sebastian Lothary, Kai-Uwe Schäfer, Bernhard Thull, Tobias Vogel, Rigo Wenning
2021 Informatik-Spektrum  
The focus is on ontologies (knowledge-based AI) and machine learning.  ...  This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence.  ...  RDF versus labeled property graphs Resource Description Framework (RDF) 22 is a W3C 23 standard for data interchange on the web.  ... 
doi:10.1007/s00287-021-01343-1 fatcat:p3jlf7icabe4vbzqziurodfdle

Semantic Wonder Cloud: Exploratory Search in DBpedia [chapter]

Roberto Mirizzi, Azzurra Ragone, Tommaso Di Noia, Eugenio Di Sciascio
2010 Lecture Notes in Computer Science  
The system exploits not only pure semantic connections in the underlying RDF graph but it mixes the meaning of such information with external non-semantic knowledge sources, such as web search engines  ...  Acknowledgment We are very grateful to Diego Guario and Moritz Stefaner for the implementation of SWOC and to Enrico Motta for fruitful discussion.  ...  In fact, RDF triples are conceived to represent information for machine-to-machine interaction.  ... 
doi:10.1007/978-3-642-16985-4_13 fatcat:pts6bnxkqrfhdfprd2a7gp77gy

Student Query Trend Assessment with Semantical Annotation and Artificial Intelligent Multi-Agents

2017 Eurasia Journal of Mathematics, Science and Technology Education  
Research in era of data representation to contribute and improve key data policy involving the assessment of learning, training and English language competency.  ...  This can be done using platform of semantic web data model; well known as Resource Description Framework (RDF).  ...  It will provide us to get DB data that is in both human and machine readable format. Figure 15. RDF Schema Graph XML conversion is followed by RDF conversion.  ... 
doi:10.12973/eurasia.2017.00763a fatcat:cjr6ma4hxjex3fk3nrsbfd4nyq

Semantic-Guided Feature Selection for Industrial Automation Systems [chapter]

Martin Ringsquandl, Steffen Lamparter, Sebastian Brandt, Thomas Hubauer, Raffaello Lepratti
2015 Lecture Notes in Computer Science  
Although valuable insights for plant operators and engineers can be gained from such data sets, they often remain undiscovered due to the problem of applying machine learning algorithms in high-dimensional  ...  By providing access to semantic data models for industrial data acquisition systems, we enable the explicit incorporation of such domain knowledge.  ...  The emerging field of machine learning in Linked Data has brought up a number of graph kernel functions particularly designed for RDF graph data. Definition 2 (RDF Graph Kernel).  ... 
doi:10.1007/978-3-319-25010-6_13 fatcat:hkgc27ndx5dh3fnkkcoeqtofta

A Survey on Ontologies for Context Reasoning

Yoosoo Oh
2015 Indian Journal of Science and Technology  
Usually, ontology has compatibility with XML and RDF. XML is a specification for describing data with markup tags, and RDF is a markup language based on the XML syntax 4, 9 .  ...  . • RDF123 is a web service for converting data to an RDF graph. • HOZO is an ontology visualization tool that supports group ontology developments. • OWLViz is a visual editor for OWL and is available  ... 
doi:10.17485/ijst/2015/v8i1/84648 fatcat:7lqvvfgiznbtbhwb2rylccm3ii
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