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A scalable approach for statistical learning in semantic graphs
<span title="">2014</span>
<i title="IOS Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pcapks3huberdozbvfqowysuly" style="color: black;">Semantic Web Journal</a>
</i>
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In this paper we apply machine learning to semantic graph data and argue that scalability and robustness can be achieved via an urn-based statistical sampling scheme. ...
Increasingly, data is published in the form of semantic graphs. ...
Scalable Kernel Machine Learning for Semantic Graphs The SUNS approach is based on the scalable urn model described in Section 3.3 and has been introduced in [3] where a feature-based approach was described ...
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Using Semantics and Statistics to Turn Data into Knowledge
<span title="2015-03-25">2015</span>
<i title="Association for the Advancement of Artificial Intelligence (AAAI)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/27wksbinzzhjfow2wuy6m2iefm" style="color: black;">The AI Magazine</a>
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Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. ...
We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art ...
Acknowledgments We would like to thank Shangpu Jiang and Daniel Lowd for sharing their data and offering enthusiastic assistance. ...
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Large-Scale Multimedia Retrieval and Mining [Guest editors' introduction]
<span title="">2011</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2gcnyvxfqrbsnky2upsxenzfd4" style="color: black;">IEEE Multimedia</a>
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scalable approaches for multimedia retrieval and mining. ...
To overcome this drawback, Wu and Hoi propose an online semantics-preserving, metric-learning algorithm for enhancing BoW by minimizing the semantic loss. ...
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Chapter 7 Scalable Knowledge Graph Processing Using SANSA
[chapter]
<span title="">2020</span>
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
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This chapter introduces Scalable Semantic Analytics Stack (SANSA), that addresses the challenge of dealing with large scale RDF data and provides a unified framework for applications like link prediction ...
The size and number of knowledge graphs have increased tremendously in recent years. ...
Spark SQL, for an efficient and scalable inference process.
Machine Learning SANSA-ML is the Machine Learning (ML) library in SANSA. ...
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Machine Learning-Based Semantic Entity Alignment for Multi-Source Data: a Systematic Literature Review
<span title="2021-11-01">2021</span>
<i title="Zenodo">
Zenodo
</i>
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Many machine learning-based semantic entity alignment approaches have been proposed by the recent studies in the field. ...
ML-based semantic entity alignment approaches. ...
[55] propose a novel decentralized scalable learning framework named Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and ...
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Expeditious Generation of Knowledge Graph Embeddings
[article]
<span title="2018-11-09">2018</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2018-03-21">2018</span>
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We show that our embeddings achieve results comparable with the most scalable approaches on knowledge graph completion as well as on a new metric. ...
In this paper, we propose KG2Vec, a simple and fast approach to Knowledge Graph Embedding based on the skip-gram model. ...
We show that our embeddings achieve results comparable with the most scalable approaches on knowledge graph completion as well as on a new metric. ...
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Improving data quality in the linked open data: a survey
<span title="">2018</span>
<i title="IOP Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a>
</i>
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Therefore, it needs (1) a standard for data quality assessment and evaluation which is more appropriate to the LOD; (2) a framework of methods based on statistical relational learning that can improve ...
Hence, based on those standards and an integrative approach, there are opportunities to improve the LOD data quality in the term of incompleteness, inaccuracy and inconsistency, considering to its schema ...
The measured performances for handling data quality in huge LOD by statistical relational learning are learning time, level of learning automation (flexibility), ontology expressiveness, scalability, and ...
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A Scalable AutoML Approach Based on Graph Neural Networks
[article]
<span title="2022-05-18">2022</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2021-10-29">2021</span>
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embeddings to find similar datasets in the database based on its content instead of metadata-based features, (3) models AutoML pipeline creation as a graph generation problem, to succinctly characterize ...
KGpip's meta-learning is a sub-component for AutoML systems. We demonstrate this by integrating KGpip with two AutoML systems. ...
The contributions of this paper are the following: • Section 3 defines a scalable meta-learning approach based on representation learning of mined ML pipeline semantics and datasets for over 100 datasets ...
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Machine Learning Methods for Keyword Extraction and Indexing
<span title="2019-12-31">2019</span>
<i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cj3bm7tgcffurfop7xzswxuks4" style="color: black;">VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE</a>
</i>
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Therefore the machine learning approaches for keyword extraction are proposed. In this paper various machine learning approaches have discussed along with its merits and de-merits. ...
Keyword extraction is a process of identifying the document. Manual keyword extraction is cumbersome and it is in feasible to efficiently identify all the keywords in the document. ...
This work presents a summary of the machine learning approaches for keyword extraction and the indexing structures in the graph-based representation. ...
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4.-8. März 2019
<span title="">2019</span>
<i title="Gesellschaft für Informatik, Bonn">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5aywlmr4zjgshho3wjwhlq42ve" style="color: black;">Datenbanksysteme für Business, Technologie und Web</a>
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(ii) Provide a forum to members of both communities for exchanging ideas. ...
., for implementing efficient systems combining databases and StaRAI. Thus, the goal of this tutorial is two-fold: (i) Present an overview of methods within StaRAI. ...
There exist approaches for learning lifted representations, a prominent one being the colouring algorithm by Ahmadi et al. [Ah13] . ...
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18420/btw2019-ws-27">doi:10.18420/btw2019-ws-27</a>
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A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead
[article]
<span title="2021-12-31">2021</span>
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arXiv
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Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation systems, semantic search, etc. ...
As an outcome, enterprises are putting their effort into constructing and maintaining knowledge graphs to support various downstream applications. Manual approaches are too expensive. ...
Statistical and Probabilistic approaches
information to the knowledge graph. ...
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Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval
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<span title="2019-04-25">2019</span>
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arXiv
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To address the problem, in this paper, we propose a novel hashing approach, dubbed as Discrete Semantic Transfer Hashing (DSTH). ...
Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency. ...
In this method, hash functions are learned in a bit-wise manner with a sequential learning. 5. Latent semantic minimal hashing (LSMH) [26] . ...
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Fast and scalable learning of neuro-symbolic representations of biomedical knowledge
[article]
<span title="2018-04-30">2018</span>
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arXiv
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In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. ...
Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neuro-symbolic representation learning, we show how to train fast (under 1 minute ...
We admit such a relaxation in the OWL semantics commitment of the knowledge graph, because we do not leverage any OWL reasoning for our tasks. ...
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A Scalable Graph-Based Semi-Supervised Ranking System for Content-Based Image Retrieval
<span title="">2013</span>
<i title="IGI Global">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/erarldcupndhnav6hwhvg7hoai" style="color: black;">International Journal of Multimedia Data Engineering and Management</a>
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The authors propose a scalable graph-based semi-supervised ranking system for image retrieval. ...
Active learning is applied to build a dynamic feedback log to extract semantic features of images. Two-layer manifold graphs are then built in both low-level visual and high-level semantic spaces. ...
reasonable number of representative semantics for all images in a database; • Effectively combining low-level visual and high-level semantic similarity measure to build a scalable manifold graph, which ...
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Unsupervised Hierarchical Grouping of Knowledge Graph Entities
[article]
<span title="2019-08-20">2019</span>
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arXiv
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We show that our approach is able to effectively learn entity groups using a scalable procedure in noisy and sparse datasets. ...
In this context, multiple works were developed to utilize logical inference on ontologies and statistical machine learning methods to learn type assertion in knowledge graphs. ...
Acknowledgements This work has been supported by Insight Centre for Data Analytics at National University of Ireland Galway, Ireland (supported by the Science Foundation Ireland grant 12/RC/2289). ...
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