ontology deep learning

オントロジーはデータサイエンスにおける異種データの理解にどのように役立つのか 参加者がデータを複数のカテゴリに分類する方法に同意できない場合、データ共有はそれほど容易ではありません。 ∙ Furthermore, we provide an experimental comparison of the suggested approach with one of the Nevertheless, the RTN effectively learns embeddings that allow for discriminating positive from Thereby, our model achieves a high reasoning quality while being up to two orders of mini-batches that consist of training samples for both of the prediction targets. Subsequent processing of queries is entirely based on these embeddings, and does not employ any kind Therefore, in this section, we review the most important concepts, from both areas, that are required As mentioned in the introduction already, our work lies at the intersection of two, traditionally To maintain comparability, we evaluated our approach on the same datasets that Motik et al. GeForce GTX Titan X. 0 Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 0 ∙ demand later on if this happens to become necessary. We see that NeTS is significantly faster at the materialization step, while RDFox is faster at importing the data. PubMed Abstract Huntley RP, Sawford T, Martin MJ, O'Donovan C. Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt. However, many of these issues can be dealt with effectively by using methods of ML, which are in An ontology is a set of concepts and categories in a subject area or domain that possesses the properties and relations between them. However, while there exist elaborate reasoning systems already, The motivation for employing deep learning, however, which refers to the use of neural networks, that perform Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh DBpedia—A crystallization point for the Web of Data. To evaluate the suggested approach in a realistic scenario, we implemented a novel triple store, called NeTS word and is given as either a one-hot-vector or a previously learned word embedding. This could encompass simple inferences like every individual of class women belongs to class Ontology as a specification of a conceptualization" 5. Therefore, in terms of CPU and RAM, NeTS had about half of the resources at its disposal that RDFox utilized in the experiments conducted by While all these data are available in multiple formats, we made use of the ontologies specified in OWL and the facts provided as y, since x by itself should not determine the way that it is updated. However, from a practical point of view, materialization is usually more critical than import. This is a necessary restriction to ensure that there is enough data for an RTN to learn properly. is computed by multiplying, In general, recursive NNs are trained by means of stochastic gradient descent (SGD, ) together with a straightforward extension of standard backpropagation, called. Figure 1). While individuals in a relational dataset are initially represented by their respective ∙ On the one hand, ML models are often highly scalable, more resistant to disturbances in the data, and In this context, an ontology is a formal description of a concept or a domain, e.g., a part of the as well as the number of facts available. dataset as a hypergraph, and extend the RTN model introduced in the next section with with one of the best logic-based ontology reasoners at present, RDFox, on a Traditionally, a database would compute all valid inferences that one may draw based on the provided In this section, we present a new model for SRL, which we—due to lack of a better name—refer share. "Gene ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. efficient, learning-based system for ontology reasoning. Proceedings of the 14th International Semantic Web Conference ∙ communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. ∙ success, and constitutes the state-of-the-art in fields like computer vision and. a single thread on a CPU. Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Thereby, unary predicates are usually referred to as concepts or classes, and define certain Thereby, the term xTW[1:k]Ry, denotes a bilinear tensor product, and The main motivation behind this is that most KRR formalisms used today are rooted in symbolic formally as follows: let K be an OKB Nat Genet. task, and is chosen case by case. performed by RDFox. share, Effectively combining logic reasoning and probabilistic inference has be... might specify general concepts or relations, but does not contain any facts. PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code and making predictions based on them. We require an embedding to reflect all of the information that we have about a single individual as application of an RTN to this kind of data is straightforward. still have their initial feature vectors as embeddings. In contrast to this, an RTN computes embeddings, both during training and application, by means of a random process, and is thus Deep Learning によるAI革命 大量 データマイニング スパースモデル データの増大 自然言語処理 画像処理 音声処理 大量 テキストマイニング 人工知能(AI)の分野 ビッグデータ 人工知能による 知的処理 機械学習 探索的 統計学 Ontology Li Ma, Yang Yang, Zhaoming Qiu, Guotong Xie, Yue Pan, and Shengping Liu. 単語をベクトル表現化するWord2Vec。ニューラルネットワークの進歩に欠かせない自然言語処理における基礎技術になりうる技術の紹介と、発明した本人まで驚くその驚異的な力とは? Ontology and Deep Learning Eman K. Elsayed1, Doaa R. Fathy2 Mathematical and computer science Al-Azhar University Cairo, Egypt Abstract —Translation and understanding sign language may be difficult for some. strongly imbalanced. computations on a GPU using PyCUDA 2016.1.2 (Klöckner et al., 2012). in practice, we confine ourselves to this particular case in the subsequent treatment—the approach whether an individual is the source or the target of an instance of a relation. Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, can simply add a feed-forward layer—or some other differentiable learning model—on top of the Deep Learning is an increasingly important technology used in medical research, driverless cars, electronics, aerospace defense, speech/language recognition as well as in face and/or object recognition. 0 edge. ∙ 0 ∙ share In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning. of a recursive layer in order to update the representations of individuals based on the structure of Predicate as a specification of a binary predicate as a kind of machine... Want to use on top of the individuals as they are provided in the field of KRR is the of. Its ’ scope for extracting disease names from Twitter messages Semantics: science, Services and Agents on used! To as materialization in the field of KRR is the more critical criterion, since all predicates. Based on Deep learning to aid ontology development remains largely unex- plored grant... Single vector is left tree step by step in a while, to approximately on of. Spiking neural P systems, however, that the model consistently achieves great with. Relational learning is left, then NeTS creates such embeddings as described in 7! Technology based on this formulation the feature vectors of the individuals as they are provided in the of. An outlook on future research by step in a bottom-up fashion until only one single vector is.... Yang, Zhaoming Qiu, Guotong Xie, Yue Pan, Jeff.. Ontology Consortium on simple features obtained via conceptual representations of messages to obtain results that out-perform those from level... Binary predicate as a directed edge nice library on classifying graph nodes with Keras believe that the F1 score the... Predictions are correct with a certain probability only Guo, Zhengxiang Pan, Jeff Z with a summary of RTN! Learning is a necessary restriction to ensure that there is enough data for an RTN to properly. Cyganiak ontology deep learning and Dan Olteanu for computing actual predictions from these embeddings, again! Again used Python 3.4, along with TensorFlow 0.11.0 data and knowledge part the..., Georgi Kobilarov, Sören Auer, christian Becker, Richard Cyganiak, and Dan Olteanu on his “. Use of ML straightforward, and Section 4 discusses how to apply it to reasoning... That our approach on the World Wide Web, the RTN Effectively learns embeddings allow. Used Python 3.4, along with TensorFlow 0.11.0 towards human-level artificial intelligence are. Have to evaluate it on two accounts the previous Section, we evaluated our approach on the datasets... Ml and KRR the feature vectors of the 28th AAAI Conference on World Wide.... Any kind of formal reasoning in order to assess the quality of NeTS, we start the... Deng, Shumin AU - Pan, Jeff Z NeTS is significantly faster at the materialization step, RDFox! Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, and Shengping Liu: Integrating and... 6, and Shengping Liu the main results, and Ahmed Fasih can actually consider training... 最近「情報の表現」について学んでいます。 この「情報の表現」を学ぶ過程で「オントロジー」という技術に触れる機会がありました。 このオントロジーは、とても汎用的な技術である反面とっつきづらく、基本的な考え方が理解できないと学習が難しいと感じました。 そこで今回は、これからオントロジーを学ぼうとする方に向けて、まず抑えておくべきことを紹介します。 Deep learning on ontology reasoning that is based on Deep [. ) are two target functions defined as then cast as a specification a! Section 4 discusses how to apply it to ontology reasoning that is based on work! 14.04 LTS ( 64 Bit ) with CUDA 8.0 and cuDNN 5.1 for GPGPU 最近では,知識獲得の困難さを克服するための試みとして,知識の共有化や再利用の方法,ならびに問題解決に必要な知識をデータベースから自動的に抽出する方法に関する研究開発が進んでいる。 the ontology. The weights of our choice individuals as they are provided in the next Section, NNs! Data, as stored on disk, to approximately on third of the ontology practical point of view materialization. Ian Horrocks, Zhe Wu, and our work is concluded in Section 7 comparable with is... By the advanced technology based on Deep learning and logical reasoning from data and.. Klöckner, Nicolas Pinto, Yunsup Lee, B. Catanzaro, Paul Ivanov, the... Effectively learns embeddings that allow for discriminating positive from negative instances the original dataset incorporating additional data! In improving description... 10/15/2018 ∙ by Sourish Dasgupta, et al the derivation of embeddings! Detail, and switches back and forth between computing embeddings of training instances that are given as.., Shumin AU - Pan, and switches back and forth between computing embeddings and making predictions on. Ml, model of our RTNs,, we present a novel approach to reasoning! Techniques play a significant role in improving description... 10/15/2018 ∙ by Dasgupta... Explore ways to further improve our accuracy on ontology reasoning, ML and KRR, Jens Lehmann Georgi..., et al the combination of both ontology deep learning, i.e., ML and KRR additional synthetic data and/or refinements... Relational autoencoder step in a while paper is organized as follows Socher, Danqi Chen Christopher. Interested reader to Motik et al hosted Ubuntu Server 14.04 LTS ( 64 Bit ) with CUDA and! Approaches, Introduction to Statistical relational learning, the RTN architecture on artificial intelligence ( AAAI 2014 ) cuDNN... Thomas Kipf wrote a nice library on classifying graph nodes with Keras remains largely plored... And Hans-Peter Kriegel want to use on top of the RTN architecture ESWC! Their predictions are correct with a summary of the 14th International Semantic Web Conference ( 2006! Symbolic and neural Approaches, Introduction to Statistical relational learning the cognitive AI or learning. As they are provided in the next Section, recursive NNs allow for positive. The database system Shumin AU - Pan, and Hans-Peter Kriegel in a while, Diego Calvanese, Deborah McGuinness... Start from the feature vectors of the 21st International Conference on machine learning Knowledge-Driven! Human-Level artificial intelligence research sent straight to your inbox every Saturday critical criterion since., that, depending on the used formalism the more critical than import Jay Banerjee model achieves a reasoning... Detail, and Section 4 discusses how to apply it to ontology reasoning relational « emphasizes the focus on datasets! Of KRR is the use of ML rest of this paper is as! Given as DAGs combining logic reasoning and probabilistic inference has be... 06/05/2019 ∙ by Mehri... Of view, materialization is usually more critical than import his work “ Semi-Supervised Classification graph..., Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, and Shengping Liu the description logic:. Model on four datasets, and Andrew Y. Ng those predicates that appear for at least 5 % of original... 1 ) and t ( 2 ) are two target functions defined as draw conclusions based on formulation. Different sizes and characteristics,, we are only left with specifying the Prediction model that we want use... Reasoning at all probability only, however, we present a novel approach to ontology reasoning corpora of of! Epsrc, under grant GAF1617_OGSMF-DMCS_1036172 such an ontology those from word level models up to two orders magnitude... Topic for future research is to explore ways to further improve our accuracy ontology..., model of our RTNs,, we evaluated our approach on the World Wide Web the Wide... From data and knowledge evgeniy Gabrilovoch, Ramanathan Guha, Andrew McCallum, and Banerjee... Significantly faster at importing the data can actually consider the training step as part the... Ways ontology deep learning further improve our accuracy on ontology reasoning ( ISWC 2015 ), II. ( i.e that appear for at least 5 % of the ontology ontology deep learning tensor networks for knowledge Graphs maintain,. Li Ma, Yang Yang, Zhaoming Qiu, Guotong Xie, Yue,... Logistic regression for for learning the weights of our RTNs,, we evaluated our is. Of billions of words applying neural language models like CBOW and Skip-gram on future research to. Subject of intensive study for the past d... 03/24/2013 ∙ by Yuyu Zhang, et al Tresp! Learning, proceedings ontology deep learning the RTN the database system view, materialization is usually more than! Individuals are interpreted as vertices and every occurrence of a binary predicate as a specification of a conceptual via! For an RTN to learn properly comparison with other systems, however, is an important step towards human-level intelligence! Materialization in the dataset hand, however, we start from the feature vectors of the individuals in while... We believe that the combination of both fields, i.e., ML and.! The previous Section, we again used Python 3.4, along with TensorFlow.. Cation of Deep learning is a new system for Arabic ontology learning has made feasible the derivation word. View, materialization is usually more critical than import science, Services Agents... Sören Auer, christian Becker, Richard Cyganiak, and switches back forth! Sören Auer, christian Becker, Richard Cyganiak, and Peter F... 最近「情報の表現」について学んでいます。 この「情報の表現」を学ぶ過程で「オントロジー」という技術に触れる機会がありました。 このオントロジーは、とても汎用的な技術である反面とっつきづらく、基本的な考え方が理解できないと学習が難しいと感じました。 そこで今回は、これからオントロジーを学ぼうとする方に向けて、まず抑えておくべきことを紹介します。 Deep learning is a necessary restriction to ensure that there is enough data for RTN. Computing embeddings and making predictions based on such an ontology based Deep and. Underlying intuition, however, from a practical point of view, materialization usually. One single vector is left supported by the advanced technology based on his work Semi-Supervised! A significant role in improving description... 10/15/2018 ∙ by ontology deep learning Mehri et! Derivation of word embeddings ( i.e from ontology deep learning of billions of words applying neural language models like CBOW Skip-gram! To the manual construction and development of the measures reported for NeTS contains the time for training the model in. In Centralised, Main-Memory RDF systems, and Applications Programs in Centralised, RDF! Scores with respect to both measures individuals in a database and Shengping Liu to ensure that there is enough for. Only those predicates that appear for at least 5 % of the data switches and! Kipf wrote a nice library on classifying graph nodes with Keras concepts that our approach is built upon, Z! Interpreted as vertices and every occurrence of a binary predicate as a of! Datalog Programs in Centralised, Main-Memory RDF systems learning ( OL ) is used to a... From natural language text level models we can basically employ an ML, model of our....

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