SPARQL extensions with preferences: a survey
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paper_title: Quality assessment for Linked Data: A Survey

paper_content:

The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.
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paper_title: Database preference queries—a possibilistic logic approach with symbolic priorities

paper_content:

The paper presents a new approach to database preference queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. In this paper, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries.
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paper_title: Top-k selection queries over relational databases: Mapping strategies and performance evaluation

paper_content:

In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typically a rank of the "top k" tuples that best match the given attribute values. In this paper, we study the advantages and limitations of processing a top-k query by translating it into a single range query that a traditional relational database management system (RDBMS) can process efficiently. In particular, we study how to determine a range query to evaluate a top-k query by exploiting the statistics available to an RDBMS, and the impact of the quality of these statistics on the retrieval efficiency of the resulting scheme. We also report the first experimental evaluation of the mapping strategies over a real RDBMS, namely over Microsoft's SQL Server 7.0. The experiments show that our new techniques are robust and significantly more efficient than previously known strategies requiring at least one sequential scan of the data sets.
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paper_title: The Skyline operator

paper_content:

We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.
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paper_title: Querying with Intrinsic Preferences

paper_content:

The handling of user preferences is becoming an increasingly important issue in present-day information systems. Among others, preferences are used for information filtering and extraction to reduce the volume of data presented to the user. They are also used to keep track of user profiles and formulate policies to improve and automate decision making. We propose a logical framework for formulating preferences and its embedding into relational query languages. The framework is simple, and entirely neutral with respect to the properties of preferences. It makes it possible to formulate different kinds of preferences and to use preferences in querying databases. We demonstrate the usefulness of the framework through numerous examples.
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paper_title: A conceptual framework for fuzzy query processing—A step toward very intelligent database systems☆

paper_content:

Abstract This paper is concerned with techniques for fuzzy query processing in a database system. By a fuzzy query we mean a query which uses imprecise or fuzzy predicates (e.g. AGE = “VERY YOUNG”, SALARY = “MORE OR LESS HIGH”, YEAR-OF-EMPLOYMENT = “RECENT”, SALARY ⪢ 20,000, etc.). As a basis for fuzzy query processing, a fuzzy retrieval system based on the theory of fuzzy sets and linguistic variables is introduced. In our system model, the first step in processing fuzzy queries consists of assigning meaning to fuzzy terms (linguistic values), of a term-set, used for the formulation of a query. The meaning of a fuzzy term is defined as a fuzzy set in a universe of discourse which contains the numerical values of a domain of a relation in the system database. The fuzzy retrieval system developed is a high level model for the techniques which may be used in a database system. The feasibility of implementing such techniques in a real environment is studied. Specifically, within this context, techniques for processing simple fuzzy queries expressed in the relational query language SEQUEL are introduced.
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paper_title: Foundations of Preferences in Database Systems

paper_content:

Personalization of e-services poses new challenges to database technology, demanding a powerful and flexible modeling technique for complex preferences. Preference queries have to be answered cooperatively by treating preferences as soft constraints, attempting a best possible match-making. We propose a strict partial order semantics for preferences, which closely matches people's intuition. A variety of natural and of sophisticated preferences are covered by this model. We show how to inductively construct complex preferences by means of various preference constructors. This model is the key to a new discipline called preference engineering and to a preference algebra. Given the Best-Matches-Only (BMO) query model we investigate how complex preference queries can be decomposed into simpler ones, preparing the ground for divide & conquer algorithms. Standard SQL and XPATH can be extended seamlessly by such preferences (presented in detail in the companion paper [15]). We believe that this model is appropriate to extend database technology towards effective support of personalization.
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paper_title: Fuzzy Preference Queries To Relational Databases

paper_content:

fuzzy preference queries to relational databases is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the fuzzy preference queries to relational databases is universally compatible with any devices to read.
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paper_title: f-SPARQL: a flexible extension of SPARQL

paper_content:

We propose an flexible extension of SPARQL by introducing fuzzy set theory and the α-cut operation of fuzzy numbers into SPARQL. We show how to efficiently compute the top-k answers of flexible SPARQL queries with regard to membership degrees and user-defined weights. Based on our method, a flexible query service is implemented and evaluated on the LUBM platform. Results of a preliminary user study demonstrate that the method for construction and translation of flexible queries and results ranking can capture the users's intension effectively.
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paper_title: fp-Sparql: An RDF fuzzy retrieval mechanism supporting user preference

paper_content:

RDF fuzzy retrieval is an important module for realizing intelligent retrieval of Web semantics. In this paper, Zadeh's type-II fuzzy set theory, as well as the concepts of α-cut set and linguistic variable is adopted to put forward the RDF fuzzy retrieval mechanism supporting user preference, which extends SPARQL language for further realizing fuzzy and preferred expression. Moreover, ordered sub-domain table of linguistic values is constructed, to realize the projection from the fuzzy values to relayed sub-domains in the table, so as to figure out the interval of membership. On this basis, it will then be converted into the standard SPARQL by drawing support from the defuzzification rules, so as to achieve fuzzy retrieval operations. In order to test the ideology proposed in this paper, the fp-SPARQL (fuzzy and preference SPARQL) retrieval system is developed. The system has extended univ_Bench main body, with plenty of retrieval instances constructed. On this basis, visual methods will be employed to realize a fuzzy and preferred retrieval on RDF. According to the result of this experiment, our method has improved the performance of RDF fuzzy retrieval, and correspondingly, users' satisfaction rate on the retrieval results is also enhanced. Besides, the retrieval method complies with users' habit, endowing the system with higher flexibility nature.
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paper_title: SQLf Query Functionality on Top of a Regular Relational Database Management System

paper_content:

This paper is mainly concerned with the extension of database management systems querying capabilities, so that users may address queries involving preferences and get discriminated answers. The use of flexible (gradual) predicates interpreted in the framework of the fuzzy set theory is advocated for defining a query language, called SQLf. This language enlarges the functionalities offered by SQL and it is considered here from a query processing point of view.
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paper_title: f-SPARQL: a flexible extension of SPARQL

paper_content:

We propose an flexible extension of SPARQL by introducing fuzzy set theory and the α-cut operation of fuzzy numbers into SPARQL. We show how to efficiently compute the top-k answers of flexible SPARQL queries with regard to membership degrees and user-defined weights. Based on our method, a flexible query service is implemented and evaluated on the LUBM platform. Results of a preliminary user study demonstrate that the method for construction and translation of flexible queries and results ranking can capture the users's intension effectively.
---
paper_title: Ranking RDF with provenance via preference aggregation

paper_content:

Information retrieval on RDF data benefits greatly from additional provenance information attached to the individual pieces of information. Provenance information such as origin of data, certainty, and temporal information on RDF statements can be used to rank search results according to one of those dimensions. In this paper, we consider the problem of aggregating provenance information from different dimensions in order to obtain a joint ranking over all dimensions. We relate this to the problem of preference aggregation in social choice theory and translate different solutions for preference aggregation to the problem of aggregating provenance rankings. By exploiting the ranking orderings on the provenance dimensions, we characterize three different approaches for aggregating preferences, namely the lexicographical rule, the Borda rule and the plurality rule, in our framework of provenance aggregation.
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paper_title: Efficient execution of top-k SPARQL queries

paper_content:

Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The $\mathcal{S}$PARQL-$\mathcal{R}$ANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for $\mathcal{S}$PARQL-$\mathcal{R}$ANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a $\mathcal{S}$PARQL-$\mathcal{R}$ANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.
---
paper_title: Top-k selection queries over relational databases: Mapping strategies and performance evaluation

paper_content:

In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typically a rank of the "top k" tuples that best match the given attribute values. In this paper, we study the advantages and limitations of processing a top-k query by translating it into a single range query that a traditional relational database management system (RDBMS) can process efficiently. In particular, we study how to determine a range query to evaluate a top-k query by exploiting the statistics available to an RDBMS, and the impact of the quality of these statistics on the retrieval efficiency of the resulting scheme. We also report the first experimental evaluation of the mapping strategies over a real RDBMS, namely over Microsoft's SQL Server 7.0. The experiments show that our new techniques are robust and significantly more efficient than previously known strategies requiring at least one sequential scan of the data sets.
---
paper_title: Extending SPARQL Algebra to Support Efficient Evaluation of Top-K SPARQL Queries

paper_content:

With the widespread adoption of Linked Data, the efficient processing of SPARQL queries gains importance. A crucial category of queries that is prone to optimization is "top-k" queries, i.e. queries returning the top k results ordered by a specified ranking function. Top-k queries can be expressed in SPARQL by appending to a SELECT query the ORDER BY and LIMIT clauses, which impose a sorting order on the result set, and limit the number of results. However, the ORDER BY and LIMIT clauses in SPARQL algebra are result modifiers, i.e. their evaluation is performed only after the evaluation of the other query clauses. The evaluation of ORDER BY and LIMIT clauses in SPARQL engines typically requires the process of all the matching solutions (possibly thousands), followed by a monolithically computation of the ranking function for each solution, even if only a limited number (e.g. K=10) of them were requested, thus leading to poor performance. ::: ::: In this paper, we present $\mathcal{S}$PARQL-$\mathcal{R}{\rm ANK}$, an extension of the SPARQL algebra and execution model that supports ranking as a first-class SPAR-QL construct. The new algebra and execution model allow for splitting the ranking function and interleaving it with other operations. We also provide a prototypal open source implementation of $\mathcal{S}$PARQL-$\mathcal{R}{\rm ANK}$ based on ARQ, and we carry out a series of preliminary experiments.
---
paper_title: Ranking RDF with provenance via preference aggregation

paper_content:

Information retrieval on RDF data benefits greatly from additional provenance information attached to the individual pieces of information. Provenance information such as origin of data, certainty, and temporal information on RDF statements can be used to rank search results according to one of those dimensions. In this paper, we consider the problem of aggregating provenance information from different dimensions in order to obtain a joint ranking over all dimensions. We relate this to the problem of preference aggregation in social choice theory and translate different solutions for preference aggregation to the problem of aggregating provenance rankings. By exploiting the ranking orderings on the provenance dimensions, we characterize three different approaches for aggregating preferences, namely the lexicographical rule, the Borda rule and the plurality rule, in our framework of provenance aggregation.
---
paper_title: Efficient execution of top-k SPARQL queries

paper_content:

Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The $\mathcal{S}$PARQL-$\mathcal{R}$ANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for $\mathcal{S}$PARQL-$\mathcal{R}$ANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a $\mathcal{S}$PARQL-$\mathcal{R}$ANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.
---
paper_title: Extending SPARQL Algebra to Support Efficient Evaluation of Top-K SPARQL Queries

paper_content:

With the widespread adoption of Linked Data, the efficient processing of SPARQL queries gains importance. A crucial category of queries that is prone to optimization is "top-k" queries, i.e. queries returning the top k results ordered by a specified ranking function. Top-k queries can be expressed in SPARQL by appending to a SELECT query the ORDER BY and LIMIT clauses, which impose a sorting order on the result set, and limit the number of results. However, the ORDER BY and LIMIT clauses in SPARQL algebra are result modifiers, i.e. their evaluation is performed only after the evaluation of the other query clauses. The evaluation of ORDER BY and LIMIT clauses in SPARQL engines typically requires the process of all the matching solutions (possibly thousands), followed by a monolithically computation of the ranking function for each solution, even if only a limited number (e.g. K=10) of them were requested, thus leading to poor performance. ::: ::: In this paper, we present $\mathcal{S}$PARQL-$\mathcal{R}{\rm ANK}$, an extension of the SPARQL algebra and execution model that supports ranking as a first-class SPAR-QL construct. The new algebra and execution model allow for splitting the ranking function and interleaving it with other operations. We also provide a prototypal open source implementation of $\mathcal{S}$PARQL-$\mathcal{R}{\rm ANK}$ based on ARQ, and we carry out a series of preliminary experiments.
---
paper_title: Querying the semantic web with preferences

paper_content:

Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. Users state which kind of answers they ‘prefer’ by adding soft constraints to their queries. ::: ::: Current approaches in the Semantic Web offer only limited facilities for specification of scoring and result ordering. There is no common language element to express and formalize ranking and preferences. We present a comprehensive extension of SPARQL which directly supports the expression of preferences. This includes formal syntax and semantics of preference expressions for SPARQL. Additionally, we report our implementation of preference query processing, which is based on the ARQ query engine.
---
paper_title: The Skyline operator

paper_content:

We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.
---
paper_title: Efficiently evaluating skyline queries on RDF databases

paper_content:

Skyline queries are a class of preference queries that compute the pareto-optimal tuples from a set of tuples and are valuable for multicriteria decision making scenarios. While this problem has received significant attention in the context of single relational table, skyline queries over joins of multiple tables that are typical of storage models for RDF data has received much less attention. A naive approach such as a join-first-skyline-later strategy splits the join and skyline computation phases which limit opportunities for optimization. Other existing techniques for multi-relational skyline queries assume storage and indexing techniques that are not typically used with RDF which would require a preprocessing step for data transformation. In this paper, we present an approach for optimizing skyline queries over RDF data stored using a vertically partitioned schema model. It is based on the concept of a "Header Point" which maintains a concise summary of the already visited regions of the data space. This summary allows some fraction of nonskyline tuples to be pruned from advancing to the skyline processing phase, thus reducing the overall cost of expensive dominance checks required in the skyline phase. We further present more aggressive pruning rules that result in the computation of near-complete skylines in significantly less time than the complete algorithm. A comprehensive performance evaluation of different algorithms is presented using datasets with different types of data distributions generated by a benchmark data generator.
---
paper_title: SPARQL with Qualitative and Quantitative Preferences

paper_content:

The volume and diversity of data that is queriable via SPARQL and its increasing integration motivate the desire to query SPARQL information sources via the specification of preferred query outcomes. Such preference-based queries support the ordering of query outcomes with respect to a user's measure of the quality of the response. In this position paper we argue for the incorporation of preference queries into SPARQL. We propose an extension to the SPARQL query language that supports the specification of qualitative and quantitative preferences over query outcomes and examine the realization of the resulting preference-based queries via off-the-shelf SPARQL engines.
---
paper_title: Ranking RDF with provenance via preference aggregation

paper_content:

Information retrieval on RDF data benefits greatly from additional provenance information attached to the individual pieces of information. Provenance information such as origin of data, certainty, and temporal information on RDF statements can be used to rank search results according to one of those dimensions. In this paper, we consider the problem of aggregating provenance information from different dimensions in order to obtain a joint ranking over all dimensions. We relate this to the problem of preference aggregation in social choice theory and translate different solutions for preference aggregation to the problem of aggregating provenance rankings. By exploiting the ranking orderings on the provenance dimensions, we characterize three different approaches for aggregating preferences, namely the lexicographical rule, the Borda rule and the plurality rule, in our framework of provenance aggregation.
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paper_title: Querying Trust in RDF Data with tSPARQL

paper_content:

Today a large amount of RDF data is published on the Web. However, the openness of the Web and the ease to combine RDF data from different sources creates new challenges. The Web of data is missing a uniform way to assess and to query the trustworthiness of information. In this paper we present tSPARQL, a trust-aware extension to SPARQL. Two additional keywords enable users to describe trust requirements and to query the trustworthiness of RDF data. Hence, tSPARQL allows adding trust to RDF-based applications in an easy manner. As the foundation we propose a trust model that associates RDF statements with trust values and we extend the SPARQL semantics to access these trust values in tSPARQL. Furthermore, we discuss opportunities to optimize the execution of tSPARQL queries.
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paper_title: R2DF framework for ranked path queries over weighted RDF graphs

paper_content:

Resource Description Framework (RDF) is a semantic web specification that aims to support conceptual modeling of information about resources in the form of a triples of facts. In this paper, we note that, although RDF provides mechanisms to encode meta-information (such as source, trust, or certainty) about facts recorded in the knowledge base, existing RDF query languages and RDF stores fail to support key primitives needed in a large class of knowledge applications which associate utilities or costs on the available knowledge statements. To address this shortcoming, we propose a novel R2DF framework for utility ranked resource descriptions. We first propose a simple ranked RDF (R2DF) specification to enhance RDF triples with an application specific weight (e.g. cost). We then propose a SPARankQL query language specification, which includes a set of novel primitives on top of the SPARQL language to express top-k queries using traditional query patterns as well as novel flexible path predicates. An extended query processor engine, AR2Q, leverages novel index structures to support efficient ranked path search and includes query optimization strategies based on two key metrics: (a) proximity and (b) sub-result inter-arrival time. Experiments show that the use of these two metrics has significant impacts on the performance of top-k queries over R2DF graphs: in particular, the proximity measure helps reduce the number of path matches that need to be considered, whereas the inter-arrival measure reduces the overall execution time significantly especially when used along with proximity. The proposed strategies help obtain query plans close to optimal.
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paper_title: On a Fuzzy Algebra for Querying Graph Databases

paper_content:

This paper proposes a notion of fuzzy graph database and describes a fuzzy query algebra that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. The algebra, based on fuzzy set theory and the concept of a fuzzy graph, is composed of a set of operators that can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. In a similar way as relational algebra constitutes the basis of SQL, the fuzzy algebra proposed here underlies a user-oriented query language and an associated tool implementing this language that are also presented in the paper.
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paper_title: Flexible Querying of Fuzzy RDF Annotations Using Fuzzy Conceptual Graphs

paper_content:

This paper presents a flexible querying system of fuzzy RDF annotations which consists in translating fuzzy RDF annotations into fuzzy conceptual graphs and using an "approximate"-projection operation in order to compare fuzzy query graphs with fuzzy annotation graphs. The fuzzy sets in the query graphs having a semantic of preferences are compared with the fuzzy sets in the annotation graphs having a semantic of similarity or imprecision. These comparisons deliver several scores which are used by our flexible querying system to sort the answers according to a total order even if these scores are not commensurable.
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paper_title: nSPARQL: A navigational language for RDF

paper_content:

Navigational features have been largely recognized as fundamental for graph database query languages. This fact has motivated several authors to propose RDF query languages with navigational capabilities. In particular, we have argued in a previous paper that nested regular expressions are appropriate to navigate RDF data, and we have proposed the nSPARQL query language for RDF, that uses nested regular expressions as building blocks. In this paper, we study some of the fundamental properties of nSPARQL concerning expressiveness and complexity of evaluation. Regarding expressiveness, we show that nSPARQL is expressive enough to answer queries considering the semantics of the RDFS vocabulary by directly traversing the input graph. We also show that nesting is necessary to obtain this last result, and we study the expressiveness of the combination of nested regular expressions and SPARQL operators. Regarding complexity of evaluation, we prove that the evaluation of a nested regular expression E over an RDF graph G can be computed in time O (|G |·|E |).
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paper_title: Querying the semantic web with preferences

paper_content:

Ranking is an important concept to avoid empty or overfull and unordered result sets. However, such scoring can only express total orders, which restricts its usefulness when several factors influence result relevance. A more flexible way to express relevance is the notion of preferences. Users state which kind of answers they ‘prefer’ by adding soft constraints to their queries. ::: ::: Current approaches in the Semantic Web offer only limited facilities for specification of scoring and result ordering. There is no common language element to express and formalize ranking and preferences. We present a comprehensive extension of SPARQL which directly supports the expression of preferences. This includes formal syntax and semantics of preference expressions for SPARQL. Additionally, we report our implementation of preference query processing, which is based on the ARQ query engine.
---
paper_title: Efficiently evaluating skyline queries on RDF databases

paper_content:

Skyline queries are a class of preference queries that compute the pareto-optimal tuples from a set of tuples and are valuable for multicriteria decision making scenarios. While this problem has received significant attention in the context of single relational table, skyline queries over joins of multiple tables that are typical of storage models for RDF data has received much less attention. A naive approach such as a join-first-skyline-later strategy splits the join and skyline computation phases which limit opportunities for optimization. Other existing techniques for multi-relational skyline queries assume storage and indexing techniques that are not typically used with RDF which would require a preprocessing step for data transformation. In this paper, we present an approach for optimizing skyline queries over RDF data stored using a vertically partitioned schema model. It is based on the concept of a "Header Point" which maintains a concise summary of the already visited regions of the data space. This summary allows some fraction of nonskyline tuples to be pruned from advancing to the skyline processing phase, thus reducing the overall cost of expensive dominance checks required in the skyline phase. We further present more aggressive pruning rules that result in the computation of near-complete skylines in significantly less time than the complete algorithm. A comprehensive performance evaluation of different algorithms is presented using datasets with different types of data distributions generated by a benchmark data generator.
---
paper_title: SPARQL with Qualitative and Quantitative Preferences

paper_content:

The volume and diversity of data that is queriable via SPARQL and its increasing integration motivate the desire to query SPARQL information sources via the specification of preferred query outcomes. Such preference-based queries support the ordering of query outcomes with respect to a user's measure of the quality of the response. In this position paper we argue for the incorporation of preference queries into SPARQL. We propose an extension to the SPARQL query language that supports the specification of qualitative and quantitative preferences over query outcomes and examine the realization of the resulting preference-based queries via off-the-shelf SPARQL engines.
---

Title: SPARQL extensions with preferences: a survey

Section 1: INTRODUCTION
Description 1: This section provides an overview of the importance of RDF and SPARQL in the context of web data representation and the need for integrating user preferences into SPARQL queries.

Section 2: RDF DATA
Description 2: This section explains the RDF data model, including the structure of RDF triples, the concept of RDF Schema, and the properties of RDF graphs.

Section 3: SPARQL
Description 3: This section describes the SPARQL query language, its structure, and its capabilities for querying RDF data.

Section 4: PREFERENCE QUERIES ON RDF DATA
Description 4: This section discusses the importance of integrating preferences into SPARQL queries and classifies the approaches into quantitative and qualitative methods.

Section 5: Quantitative approaches
Description 5: This subsection presents various quantitative approaches for integrating preferences into SPARQL queries, including fuzzy extensions and top-k queries.

Section 6: Fuzzy Extensions of SPARQL
Description 6: This subsection details the fuzzy extensions to SPARQL, emphasizing the ability to handle fuzzy conditions and user queries using membership functions and fuzzy terms.

Section 7: Top-k Queries
Description 7: This subsection covers the concept of top-k queries in SPARQL, illustrating how to return the most relevant results based on user preferences.

Section 8: Qualitative approaches
Description 8: This subsection addresses qualitative methods for preference queries such as skyline queries, focusing on non-dominated answers using binary preference relations.

Section 9: DISCUSSION
Description 9: This section summarizes the surveyed approaches for extending SPARQL with preferences, highlighting their limitations and proposing future research directions.