User preferences in the web of data

Polo, Luis; Mínguez, Iván; Berrueta, Diego; Ruíz, Carlos; Gómez, José Manuel

doi:10.3233/sw-2012-0080

Cited by 7 publications

(4 citation statements)

References 15 publications

(18 reference statements)

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“…This will also help us further develop the language, identifying additional 'syntactic sugar' constructs that can hint at optimizations targeting intransitive relations that fall outside the scope of BNL. Further extensions could allow the client to refer to preferences and preference-related metadata within the knowledge base itself [14,18,19].…”

Section: Discussionmentioning

confidence: 99%

An Extension of SPARQL for Expressing Qualitative Preferences

Troumpoukis

Konstantopoulos

Charalambidis

2017

Lecture Notes in Computer Science

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In this paper we present SPREFQL, an extension of the SPARQL language that allows appending a "PREFER" clause that expresses 'soft' preferences over the query results obtained by the main body of the query. The extension does not add expressivity and any SPREFQL query can be transformed to an equivalent standard SPARQL query. However, clearly separating preferences from the 'hard' patterns and filters in the "WHERE" clause gives queries where the intention of the client is more cleanly expressed, an advantage for both human readability and machine optimization. In the paper we formally define the syntax and the semantics of the extension and we also provide empirical evidence that optimizations specific to SPREFQL improve run-time efficiency by comparison to the usually applied optimizations on the equivalent standard SPARQL query.This general preference relation is restricted into intrinsic preference formulas that do not rely on external information to compare two objects:

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Section: Discussionmentioning

confidence: 99%

An Extension of SPARQL for Expressing Qualitative Preferences

Troumpoukis

Konstantopoulos

Charalambidis

2017

Lecture Notes in Computer Science

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“…In the literature, binary preferences have been studied in recommender systems under a twofold perspective: quantitative [33][34][35], relying upon a scoring function to determine a total order of results; qualitative [13,36,37], using binary relations to express a (strict) partial order of results. In the scope of this paper, we focused on qualitative preferences, yielding a higher expressiveness with respect to quantitative ones.…”

Section: Preference-based Recommender Systems For Data Explorationmentioning

confidence: 99%

“…In [13] a preferencebased framework is implemented in a Data Warehouse, where preferences are formulated over aggregation levels of facts. In [33], preferences are formalised as constraints on semantic resources, mixing both quantitative and qualitative aspects, whereas [36] devises an extension of SPARQL query language for expressing preferences on semantic data. Beyond the qualitative and quantitative aspects, contextual preferences have been studied, to take into account different situations users are involved in.…”

Section: Preference-based Recommender Systems For Data Explorationmentioning

confidence: 99%

“…Exploration contexts gather information characterising the situations under which the user explores indicators, influenced by both his/her roles and goals. With respect to the aforementioned preferencebased frameworks for handling multi-dimensional data [13,33,36], PERSEUS fosters preferences to rank indicators based on their semantic definition and not on their actual values. In fact, preferences allow to identify in advance indicators of interest for the user, and only at a later time indicators values are inspected, thus saving cost and resources required to query Data Lake sources.…”

Section: Novel Contributionsmentioning

confidence: 99%

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A semantics-enabled approach for personalised Data Lake exploration

Bianchini,

De Antonellis,

Garda

2023

Knowl Inf Syst

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The increasing availability of Big Data is changing the way data exploration for Business Intelligence is performed, due to the volume, velocity and uncontrolled variety of data on which exploration relies. In particular, data exploration is required in Data Lakes that have been proposed to host heterogeneous data sources, given their flexibility to cope with cumbersome properties of Big Data. However, as data grows, new methods and techniques are required for extracting value and knowledge from data stored within Data Lakes, aggregating data into indicators according to multiple analysis dimensions, to enable a large number of users with different roles and competencies to capitalise on available information. In this paper, we propose PERSEUS (PERSonalised Exploration by User Support), a computer-aided approach for data exploration on top of a Data Lake, structured over three phases: (1) the construction of a semantic metadata catalog on top of the Data Lake, leveraging tools and metrics to ease the annotation of the Data Lake metadata; (2) modelling of indicators and analysis dimensions, guided by an openly available Multi-Dimensional Ontology to enable conformance checking of indicators and let users explore Data Lake contents; (3) enrichment of the definition of indicators with personalisation aspects, based on users’ profiles and preferences, to make easier and more usable the exploration of data for a large number of users. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.

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