Mining the Web of Linked Data with RapidMiner

Ristoski, Petar; Bizer, Christian; Paulheim, Heiko

doi:10.1016/j.websem.2015.06.004

Cited by 67 publications

(18 citation statements)

References 27 publications

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“…Mynarz et al [49] have considered using user specified SPARQL queries in combination with SPARQL aggregates. FeGeLOD [65] and its successor, the RapidMiner Linked Open Data Extension [72], have been the first fully automatic unsupervised approach for enriching data with features that are derived from LOD. The approach uses six different unsupervised feature generation strategies, exploring specific or generic relations.…”

Section: Lod In Machine Learningmentioning

confidence: 99%

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RDF2Vec: RDF graph embeddings and their applications

Ristoski

Rosati

Noia

et al. 2019

Self Cite

142

130

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Linked Open Data has been recognized as a valuable source for background information in many data mining and information retrieval tasks. However, most of the existing tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. We evaluate our approach on three different tasks: (i) standard machine learning tasks, (ii) entity and document modeling, and (iii) content-based recommender systems. The evaluation shows that the proposed entity embeddings outperform existing techniques, and that pre-computed feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.

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Section: Lod In Machine Learningmentioning

confidence: 99%

“…The approach uses six different unsupervised feature generation strategies, exploring specific or generic relations. It has been shown that such feature generation strategies can be used in many data mining tasks [66,72].…”

Section: Lod In Machine Learningmentioning

confidence: 99%

RDF2Vec: RDF graph embeddings and their applications

Ristoski

Rosati

Noia

et al. 2019

Self Cite

142

130

View full text Add to dashboard Cite

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“…The basic principles of TEP are to: (a) move the code to the data, including tools and resources; (b) work in a virtual workplace environment with access to relevant non-RS and RS data, processing and analysis tools, platform services, and functions such as tools for data mining, visualization, and the most relevant development tools (IDL-Interactive Data Language, Python, R), or communication tools such as social networks; and (c) access and share data and collaborate among the user community, scientists, and governance. In the future, large-scale worldwide sensor networks and TEP based on the semantic web, ontology, and linked open data/spatially-linked open data approaches [189,199] will form the basis of data archiving, intelligent data processing, integration, retrieval, modeling, and the classification of all RS data and the complex additional data of forest health data and their indicators [200].…”

Section: Standardized In Data Analysis (Thematic Exploitation Platforms)mentioning

confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

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“…In [76] a featureselection method based on ontology is proposed. The data mining environment RapidMiner [40] includes a LOD extension which provides a set of operators for augmenting existing datasets with additional attributes from open data sources [65]. In [53] semantic technologies are used to assist data scientists in selecting appropriate modelling techniques in the field of statistics or machine learning and building specific models as well as the rationale for the techniques and models selected.…”

Section: Semantic Web Technologies For Kddmentioning

confidence: 99%

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Esnaola-Gonzalez

Bermúdez

Fernández

et al. 2018

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Fulfilling occupants' comfort whilst reducing energy consumption is still an unsolved problem in most of tertiary buildings. However, the expansion of the Internet of Things (IoT) and Knowledge Discovery in Databases (KDD) techniques lead to research this matter. In this paper the EEPSA (Energy Efficiency Prediction Semantic Assistant) process is presented, which leverages the Semantic Web Technologies (SWT) to enhance the KDD process for achieving energy efficiency in tertiary buildings while maintaining comfort levels. This process guides the data analyst through the different KDD phases in a semiautomatic manner and supports prescriptive HVAC control strategies. That is, temperature of a space is predicted simulating the activation of HVAC systems at different times and intensities, so that the facility manager can choose the strategy that best fits both the user's comfort needs and energy efficiency. Furthermore, results show that the proposed solution improves the accuracy of predictions.

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Mining the Web of Linked Data with RapidMiner

Cited by 67 publications

References 27 publications

RDF2Vec: RDF graph embeddings and their applications

RDF2Vec: RDF graph embeddings and their applications

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

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