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5 results on '"Qiu, Xule"'

2. Predicting Relations Between RDF Entities by Deep Neural Network

Author

Murata, Tsuyoshi, Onuki, Yohei, Nukui, Shun, Inagi, Seiya, Qiu, Xule, Watanabe, Masao, Okamoto, Hiroshi, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Blomqvist, Eva, editor, Hose, Katja, editor, Paulheim, Heiko, editor, Ławrynowicz, Agnieszka, editor, Ciravegna, Fabio, editor, and Hartig, Olaf, editor

Published
2017
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4. Relation prediction in knowledge graph by Multi-Label Deep Neural Network

Author

Shun Nukui, Tsuyoshi Murata, Hiroshi Okamoto, Masao Watanabe, Qiu Xule, Yohei Onuki, and Seiya Inagi

Subjects
Knowledge graph, Multidisciplinary, Theoretical computer science, Artificial neural network, Architecture model, Computer Networks and Communications, Computer science, TransE, lcsh:T57-57.97, Semantic space, Link prediction, Knowledge graph, DNN, Embedding, TransE, Link prediction, 01 natural sciences, Predicate (grammar), 010305 fluids & plasmas, Computational Mathematics, 0103 physical sciences, lcsh:Applied mathematics. Quantitative methods, Probability distribution, 010306 general physics, DNN, Embedding
Abstract

Knowledge graph will be usefull for the intelligent system. As the relationship prediction on the knowledge graph becomes accurate, construction of a knowledge graph and detection of erroneous information included in a knowledge graph can be performed more conveniently. The goal of our research is to predict a relation (predicate) of two given Knowledge Graph (KG) entities (subject and object). Link prediction between entities is important for developing large-scale ontologies and for KG completion. TransE and TransR have been proposed as the methods for such a prediction. However, TransE and TransR embed both entities and relations in the same (or different) semantic space(s). In this research we propose a simple architecture model with emphasis on relation prediction by using a Multi-Label Deep Neural Network (DNN), and developed KGML. KGML embeds entities only; given subject and object are embedded and concatenated to predict probability distribution of predicates. Since the output of KGML is the probability distribution in [0, 1], output can be classified as positive and negative by using the threshold of 0.5. Since the output of the existing method TransE is the score in [0, ∞), the threshold value must be calculated each time. Experimental results showed that predictions by KGML are more accurate than those by TransE and TransR. KGML is more accurate than DKRL which uses both KG triples and entity descriptions for learning. KGML is more accurate than PTransE in and its learning speed is faster than PTransE. The code of KGML is available at https://github.com/yo0826jp/KGML .

Published
2019

5. Predicting relations between RDF entities by Deep Neural Network

Author

Shun Nukui, Tsuyoshi Murata, Yohei Onuki, Masao Watanabe, Qiu Xule, Seiya Inagi, and Hiroshi Okamoto

Subjects
Artificial neural network, Computer science, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Ontology construction, 020201 artificial intelligence & image processing, 02 engineering and technology, Data mining, computer.file_format, RDF, computer.software_genre, computer, Predicate (grammar)
Abstract

In the process of ontology construction, we often need to find relations between entities described by the Resource Description Framework (RDF). Predicting relations between RDF entities is important for developing large-scale ontologies. The goal of our research is to predict a relation (predicate) of two given entities (subject and object). TransE and TransR have been proposed as the methods for such a prediction. We propose a method for predicting a predicate from a subject and an object by using a Deep Neural Network (DNN), and developed RDFDNN. Experimental results showed that predictions by RDFDNN are more accurate than those by TransE and TransR.

Published
2017

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