RDF analytics

Colazzo, Dario; Goasdoué, François; Manolescu, Ioana; Roatiş, Alexandra

doi:10.1145/2566486.2567982

Cited by 29 publications

(4 citation statements)

References 27 publications

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“…These approaches use several techniques for retrieving the best match results, by exploiting Information Retrieval (IR) techniques [18,19], and/or by adapting existing IR systems, like Elasticsearch, to the needs of RDF, for example, see [1,2,20] and others. As regards (c), there are several interactive information access systems, including browsing systems, such as [3,4,21] and also systems that can aid users that are not familiar with query languages to access the RDF knowledge base, for example, faceted search [6,7,13], interactive analytics services [22,23] and also systems for assisting the query building process, such as the system A-Qub [8]. Finally, regarding (d) Natural Language interface systems [24], where the input and output is given in natural language, and it returns short and precise answers, that is, through conversational access and Question Answering systems [25][26][27][28].…”

Section: Access Systems Over Rdfmentioning

confidence: 99%

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

2021

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Browsing has been the core access method for the Web from its beginning. Analogously, one good practice for publishing data on the Web is to support dereferenceable URIs, to also enable plain web browsing by users. The information about one URI is usually presented through HTML tables (such as DBpedia and Wikidata pages) and graph representations (by using tools such as LODLive and LODMilla). In most cases, for an entity, the user gets all triples that have that entity as subject or as object. However, sometimes the number of triples is numerous. To tackle this issue, and to reveal similarity (and thus facilitate browsing), in this article we introduce an interactive similarity-based browsing system, called RDFsim, that offers “Parallel Browsing”, that is, it enables the user to see and browse not only the original data of the entity in focus, but also the K most similar entities of the focal entity. The similarity of entities is founded on knowledge graph embeddings; however, the indexes that we introduce for enabling real-time interaction do not depend on the particular method for computing similarity. We detail an implementation of the approach over specific subsets of DBpedia (movies, philosophers and others) and we showcase the benefits of the approach. Finally, we report detailed performance results and we describe several use cases of RDFsim.

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Section: Access Systems Over Rdfmentioning

confidence: 99%

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

2021

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“…Such statistical linked data are just different serialization and publication formats of traditional OLAP cubes. Other work has suggested "lenses" over RDF data [20] for the purpose of RDF data analysis, i.e., analytical schemas which can be used for OLAP queries on RDF data. Similarly, superimposed multidimensional schemas [37] define a mapping between a multidimensional model and a KG in order to allow for the formulation of OLAP queries.…”

Section: Olap and Semantic Web Technologiesmentioning

confidence: 99%

Knowledge Graph OLAP

Schuetz

Bozzato

Neumayr

et al. 2021

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A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

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“…We identified 10 studies that used intent concepts. 8 of them are used to express requirements at the execution level such as the Analytica Queries (AnQ) model in [8] that facilitates expressing user queries that need to be performed on data. Two studies were focusing on representing the high-level goals of the analysts: the Scientist's Intent Ontology [10] and the Goal Oriented Model [6].…”

Section: Role Of Requirement Models In Engineering Analytics Processesmentioning

confidence: 99%

From Requirements to Data Analytics Process: An Ontology-Based Approach

Bandara

Behnaz

Rabhi

et al. 2019

Business Process Management Workshops

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Comprehensively describing data analytics requirements is becoming an integral part of developing enterprise information systems. It is a challenging task for analysts to completely elicit all requirements shared by the organization's decision makers. With a multitude of data available from e-commerce sites, social media and data warehouses selecting the correct set of data and suitable techniques for an analysis itself is difficult and time-consuming. The reason is that analysts have to comprehend multiple dimensions such as existing analytics techniques, background knowledge in the domain of interest and the quality of available data. In this paper, we propose to use semantic models to represent different spheres of knowledge related to data analytics space and use them to assist in analytics requirements definition. By following this approach users can create a sound analytics requirements specification, linked with concepts from the operation domain, available data, analytics techniques and their implementations. Such requirements specifications can be used to drive the creation and management of analytics solutions, well aligned with organizational objectives. We demonstrate the capabilities of the proposed method by applying on a data analytics project for house price prediction.

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RDF analytics

Cited by 29 publications

References 27 publications

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

RDFsim: Similarity-Based Browsing over DBpedia Using Embeddings

Knowledge Graph OLAP

From Requirements to Data Analytics Process: An Ontology-Based Approach

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