Top PDF A Generic Mapping-Based Query Translation from SPARQL to Various Target Database Query Languages

A Generic Mapping-Based Query Translation from SPARQL to Various Target Database Query Languages

A Generic Mapping-Based Query Translation from SPARQL to Various Target Database Query Languages

Unbehauen et al. (Unbehauen et al., 2013a) de- fine the concept of compatibility between the RDF terms of a triple pattern and R2RML term maps. This more general approach effectively manages vari- able predicate maps, which clears the first aforemen- tioned limitation. Furthermore, they reduce the num- ber of candidate triples maps for each triple pattern by pre-checking join constraints implied by shared vari- ables. This clears the second aforementioned limita- tion. Yet, two limitations can be noticed: (i) R2RML referencing object maps are not considered, there- fore joins implied by shared variables are dealt with but joins declared in the mapping graph are ignored. (ii) The rewriting maps each term map to a set of columns, called column group, that enables filtering, join and data type compatibility checks. This relies on SQL capabilities (CASE, CAST, string concate- nation, etc.), making it hardly applicable out of the scope of SQL-based systems.
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Mapping-based SPARQL access to a MongoDB database

Mapping-based SPARQL access to a MongoDB database

3.1 R2RML-based SPARQL-to-SQL methods Various methods have been defined to translate SPARQL queries into another query language, which are generally tailored to the expressiveness of the target query language. For instance, SPARQL-to-SQL methods harness the ability of SQL to support joins, unions, nested queries and various string manipulation functions, to translate a SPARQL query into a single, possibly deeply nested SQL query. Some of them rely on modern RDBs optimization engines to rewrite the query in a more efficient way, although this is often not sufficient as attested by the focus on the generation of pre-optimized queries e.g. using self-join elimination or by pushing down projections and selections [8,16,18,21]. A conjunction of two basic graph patterns (BGP) generally results in the inner join of their respective translations; their union results in an SQL UNION ALL clause; the SPARQL OPTIONAL keyword between two BGPs results in a left outer join, and a SPARQL FILTER results in an encapsulating SQL SELECT in which the filter is translated into an equivalent SQL WHERE clause. Similarly, the SPARQL-to-XQuery method proposed in [1] relies on the ability of XQuery to support the same features. For instance, a SPARQL FILTER is translated into an XPath condition and/or an encapsulating XQuery For-Let-Where clause.
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Bridging the Semantic Web and NoSQL Worlds: Generic SPARQL Query Translation and Application to MongoDB

Bridging the Semantic Web and NoSQL Worlds: Generic SPARQL Query Translation and Application to MongoDB

Universit´ e Cˆ ote d’Azur, Inria, CNRS, I3S, France franck.michel@cnrs.fr, faron@i3s.unice.fr, johan.montagnat@cnrs.fr Abstract. RDF-based data integration is often hampered by the lack of methods to translate data locked in heterogeneous silos into RDF representations. In this paper, we tackle the challenge of bridging the gap between the Semantic Web and NoSQL worlds, by fostering the de- velopment of SPARQL interfaces to heterogeneous databases. To avoid defining yet another SPARQL translation method for each and every database, we propose a two-phase method. Firstly, a SPARQL query is translated into a pivot abstract query. This phase achieves as much of the translation process as possible regardless of the database. We show how optimizations at this abstract level can save subsequent work at the level of a target database query language. Secondly, the abstract query is translated into the query language of a target database, taking into ac- count the specific database capabilities and constraints. We demonstrate the effectiveness of our method with the MongoDB NoSQL document store, such that arbitrary MongoDB documents can be aligned on exist- ing domain ontologies and accessed with SPARQL. Finally, we draw on a real-world use case to report experimental results with respect to the effectiveness and performance of our approach.
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A Mapping-based Method to Query MongoDB Documents with SPARQL

A Mapping-based Method to Query MongoDB Documents with SPARQL

3 Translating SPARQL Queries into Abstract Queries under xR2RML Mappings Various methods have been defined to translate SPARQL queries into another query language, that are generally tailored to the expressiveness of the target query language. Notably, the rich expressiveness of SQL and XQuery makes it possible to define semantics-preserving SPARQL rewriting methods [8,2]. By contrast, NoSQL databases typically trade off expressiveness for scalability and fast retrieval of denormalised data. For instance, many of them hardly support joins. Therefore, to envisage the translation of SPARQL queries in the general case, we propose a two-step method. Firstly, a SPARQL query is rewritten into a pivot abstract query under xR2RML mappings, independently of any tar- get database (illustrated by step 1 in Figure 1). Secondly, the pivot query is translated into concrete database queries based on the specific target database capabilities and constraints. In this paper we focus on the application of the sec- ond step to the specific case of MongoDB. The rest of this section summarizes the first step to provide the reader with appropriate background. A complete description is provided in [16].
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SPARQL Query Rewriting with Paths [Master's Thesis]

SPARQL Query Rewriting with Paths [Master's Thesis]

Chapter 1 Introduction The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. Its main purpose is driving the evolution of the current Web by enabling users to find, share, and com- bine information more easily. It involves publishing in data/knowledge representation languages designed for the web: Resource Description Framework (RDF), Web Ontol- ogy Language (OWL), and Extensible Markup Language (XML). These languages can describe arbitrary things such as people, meetings, or machine parts.
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Towards a Visual SPARQL-DL Query Builder

Towards a Visual SPARQL-DL Query Builder

Aiming at integrating SPARQL-DL in an ontology engineering environment, this paper introduces a graphical language that preserves similarities to UML for creating visual SPARQL-DL queries. Furthermore, it presents a modification of the crowd architecture that allows visual SPARQL-DL queries. Implementing a query language in an already developed tool for creating ontologies via visual languages is not only the next logical step, but also a relevant one as its approach to ontology engineers mean to understand and study their ontologies and also help to create queries that their users require for satisfying their information needs. Moreover, it is possible to provide more reasoning services like anti-pattern based searches, in order to look for modelling issues in the ontology by obtaining a subset of the input concepts and rules and displaying them visually. However, a tool that supports both scenarios, design and query in a graphical way, and an implementation of a SPARQL-DL-based visual language is not available to our best of our knowledge.
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Title: A SPARQL QUERY GENERATION MECHANISM ON RDF

Title: A SPARQL QUERY GENERATION MECHANISM ON RDF

containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of- magnitude performance improvement over existing approaches.
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SPARQL QUERY OPTIMIZATION
CATEGORIES: A BRIEF SURVEY

SPARQL QUERY OPTIMIZATION CATEGORIES: A BRIEF SURVEY

SPARQL allows you to query for triples from an RDF database (or triple store). Superficially it resembles the Structured Query Language (SQL) used to get data from a relational database. A relational database is table based, meaning that data is stored in fixed tables with a foreign key relationship that defines the relationship between rows in the tables. A triple store stores only triples, and you can pile the triples as high as you like while describing a thing. With relational databases you are confined to the layout of the database. RDF doesn't use foreign and primary keys either. It uses URIs, the standard reference format for the World Wide Web. By using URIs, a triple store immediately has the potential to link to any other data in any triple store. That plays to the distributed strengths of the Web. Because triple stores are large amorphous collections of triples, SPARQL queries by defining a template for matching triples, called a Graph Pattern[24]. To get data out of the triple store using SPARQL, you need to define a pattern that matches the statements in the graph.
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Multimodal Database Query

Multimodal Database Query

Multimodal Database Query M u l t i m o d a l D a t a b a s e Q u e r y N i c h o l a s J H a d d o c k t t e w l e t t P a c k a r d L a b o r a t o r i e s F i l t o n R o a d , S t o k e G i f f o[.]

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Sempala: Interactive SPARQL Query Processing on Hadoop

Sempala: Interactive SPARQL Query Processing on Hadoop

In our view, using a dedicated infrastructure for semantic data processing solely would abandon all potential synergy benefits of a common data pool among various applications. Therefore, we believe that following the trend to reuse existing Big Data infrastructures is superior to a specialized infrastructure in terms of cost-benefit ratio. Consequently, there has been a lot of work done on processing RDF and SPARQL, the core components of the Semantic Web stack, based on Hadoop (MapReduce), e.g. [14, 22, 23, 25, 28]. These approaches scale very well but exhibit pretty high runtimes (several minutes to hours) due to the underlying batch processing framework. This is acceptable for ETL like work- loads and unselective queries, both in terms of input and output size. However, the majority of SPARQL queries exhibit an explorative ad-hoc style, i.e. they are typically much more selective. There is currently an evolution of user expec- tations, demanding for interactive query runtimes regardless of data size, i.e. in the order of seconds to a few minutes. This is especially true for selective queries where runtimes in the order of several minutes or even more are not satisfying. This trend can be clearly observed in the SQL-on-Hadoop field where we cur- rently see several new systems for interactive SQL query processing coming up, e.g. Stinger initiative for Hive, Shark, Presto, Phoenix, Impala, etc. They all have in common that they store their data in HDFS, the distributed file system of Hadoop, while not using MapReduce as the underlying processing framework. Following this trend, we introduce Sempala, a SPARQL-over-SQL approach to provide interactive-time SPARQL query processing on Hadoop. We store RDF data in a columnar layout on HDFS and use Impala, a massive parallel processing (MPP) SQL query engine for Hadoop, as the execution layer on top of it. To the best of our knowledge, this is the first attempt to run SPARQL queries on Hadoop using a combination of columnar storage and an MPP SQL query engine. Just as Impala is meant to be a supplement to Hive [27], we see our approach as a supplement to existing SPARQL-on-Hadoop solutions for queries where interactive runtimes can be expected.
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Evaluation of SPARQL query generation from natural language questions

Evaluation of SPARQL query generation from natural language questions

they generated. No gold standard was prepared— the authors examined each query and determined whether or not it accurately represented the orig- inal natural language question. (Yahya et al., 2012) used two human judges to manually exam- ine the output of their system at three points— disambiguation, SPARQL query construction, and the answers returned. If the judges disagreed, a third judge examined the output. (McCarthy et al., 2012) does not have a formal evaluation, but rather gives two examples of the output of the SPARQL Assist system. (This is not a system for query generation from natural language ques- tions per se, but rather an application for assisting in query constructions through methods like auto- completion suggestions.) (Unger et al., 2012) is evaluated on the basis of a gold standard of an- swers from a static data set. It is not clear how (Lopez et al., 2007) is evaluated, although they give a nice classification of error types. Review- ing this body of work, the trends that have char- acterized most past work are that either systems are not formally evaluated, or they are evaluated in a functional, black-box fashion, examining the mapping between inputs and one of two types of outputs—either the SPARQL queries themselves, or the answers returned by the SPARQL queries. The significance of the work reported here is that it attempts to develop a unified methodology for evaluating systems for SPARQL query generation from natural language questions that meets a vari- ety of desiderata for such a methodology and that is generalizable to other systems besides our own. In the development of our system for SPARQL query generation from natural language questions, it became clear that we needed a robust approach to system evaluation. The approach needed to meet a number of desiderata:
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Design Enrichment of Query Forms For Database Query

Design Enrichment of Query Forms For Database Query

To decide whether a query forms is right or not, a user does not have time to go over each data step in the query outputs. In many database queries output a large amount of data instances. In series to avoid this “Multiple-Answer” problem, we only output a compressed result table to show a higher level view of the query outputs first. Each instance in the compressed table represents a cluster of actual data instances. The user can check through interested clusters to show the detailed data instances. Figure 2 shows the flow of user actions. The compact upper-level view of query outputs is proposed in. There are many one-pass clustering algorithms for generating the compressed view efficiently Certainly, different data clustering methods would have different compressed views for the users. Different clustering methods are preferable to different data types. Clustering is just to provide a goodness view of the query outputs for the user. The system developers can select a different clustering algorithm if needed.
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Runtime Database Query Recommender For Dynamic Query Forms

Runtime Database Query Recommender For Dynamic Query Forms

ABSTRACT: At present there has been massive amount of growth in the number of users who needs to access online databases. Many of them do not have detailed knowledge of query languages. But to query structured database user either must have knowledge of database schema or there must be a system which is capable of generating query forms dynamically according to user‟s desire. So this paper proposes DQF dynamic query form, a novel query form interface which can dynamically generate query forms for users. Implication of DQF is to capture user‟s preference and rank query form components. User can refine his query form until he gets satisfied with the query results. Also this paper proposes NLP, natural language processing module for users who are comfortable with natural language query. KEYWORDS: Query Form, Query Form interface, Database schema, Natural Language Processing.
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Resolution of Ambiguities in Query Interpretation for Geographical Pictorial Query Languages

Resolution of Ambiguities in Query Interpretation for Geographical Pictorial Query Languages

For example, suppose the user wants to formu- late the following query: “Find all the regions which are passed-through by a river and overlap a forest”. In this query the user is not interested in the relationship between the river and the forest. However, when the user draws a shape representing a region and another representing a river, he cannot avoid representing a spatial re- lationship between them, so any representation considering a specific relationship between the two features can be considered as valid. Dif- ferent pictorial queries can thus represent the previous query in natural language. In parti- cular, in Figure 1-a the forest and the river are “Disjointed”, in 1-b the river touches the forest and in 1-c the river passes through the forest. It is also possible to consider other visual queries representing the same query. When a parser processes the queries in Figure 1, it should con- sider all constraints represented in the pictorial representation. For this reason, the three rep- resentations should be interpreted as three dif- ferent queries, each having a different meaning from that of the original in natural language. Different approaches have been proposed to re- move undesired constraints.
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A computer based environment for the study of relational query languages

A computer based environment for the study of relational query languages

Experimentation with the Uneddifying Interface shows how subverting the mathematical model in EDDI complicates the semantics of relational queries, and leads to violations of the laws of relational algebra. Further analysis shows that the poor design of standard SQL also makes it harder for the software designer to implement the query processing. For example, when the EDDI interpreter returns “unnatural” join, the interpretation of the query ?allfruits*apple%name; becomes obscure because of the hidden and context-dependent conventions for renaming common attributes. This problem is compounded in more complicated joins. To solve this we have to make the renaming conventions more transparent and use natural join with appropriate renaming to emulate “unnatural” join. This shows that changing the evaluation strategy alone is not sufficient to transform SQLZERO into standard SQL.
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Rewriting Declarative Query Languages

Rewriting Declarative Query Languages

The very thorough paper by Hidders et al. [60] has a similar aim, but translates a fragment of XQuery directly into tree patterns without an intermediate algebraic phase. In a first phase, the queries are annotated with properties such as result car- dinality, ordering, and occurrence of duplicates. These properties are then used to control a rewriting of the query into the Tree Pattern Normal Form (TPNF), which is always possible for the language fragment under consideration. For TPNF, a direct mapping onto tree patterns is then described. Unfortunately, the language fragment does not cover important XQuery constructs, such as value-based predicates. An- other problem is that the rewrite rules are based on XQuery Core, which is unsuit- able as a plan generator input, for example because the absence of a where clause makes it difficult to identify applicable join conditions. However, the property anno- tations are not only useful for TPNF rewriting and can be used when implementing our rewrite toolkit. Further, the TPNF technique may be used by plan generators to identify parts of the query that can be evaluated using pattern matching.
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Is query translation a distinct task from search?

Is query translation a distinct task from search?

As discussed above, the Clarity user interface was designed for a target user class of people who know the languages they are searching. This implies that mechanisms like document or title translation are not needed. One of the iCLEF requirements was, however, to recruit users who do not know the language they are searching. Thus the user interface was slightly modified to support this type of user. The first change was to include under the original title in Finnish, a translation in English. The translation used a word-by-word mechanism; multiple translations for a single word were displayed in sequence separated by commas. In our standard interface no document or summary translation was offered to the users, for iCLEF, however, some other feedback on the document content had to be provided in English. It was decided to list extracted document keywords in Finnish with a corresponding translated set and a list of proper names. Both these facilities were provided on the retrieved documents. Despite the fact that the mechanisms used were sometimes very weak (e.g. effectiveness of keywords extraction strongly depended on the relevance of the retrieved documents) it was considered important to test the two features to get an idea on their effectiveness.
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DATA MINING QUERY LANGUAGES

DATA MINING QUERY LANGUAGES

More precisely, a Data Mining query language, should provide primitives to (1) select the data to be mined and pre-process these data, (2) specify the kind of patterns to be mined[r]

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How To Check If A Query Is Consistent In A Query In A Database In Arowman (Sql)

How To Check If A Query Is Consistent In A Query In A Database In Arowman (Sql)

Of course, for the problem of detecting inconsistent conditions, a large body of work exists in the litera- ture. In general, all work in automated theorem prov- ing can be applied (see, e.g., [4]). The problem whether there exists a contradiction in a conjunction of inequal- ities is very relevant for many database problems and has been intensively studied in the literature. Klug’s classic paper [10] checks for such inconsistencies but does not treat subqueries and assumes dense domains for the attributes. The algorithm in [9] can handle recur- sion, but only negations of EDB predicates, not general NOT EXISTS subqueries. A very efficient method has been proposed by Guo, Sun, Weiss [8]. We use it here as a subroutine. Our main contribution is the way we treat subqueries. Although this uses ideas known from Skolemization, the way we apply it combined with an algorithm like [8], apply the relations as sorts, and de- tect equal terms in the Herbrand universe seems new. We also can handle null values. Consistency checking in databases has also been applied for testing whether a set of constraints is satisfiable. A classic paper about this problem is [3]. They give an algorithm which terminates if the constraints are finitely satisfiable or if they are un- satisfiable, which is the best one can do. However, the approach presented here can immediately tell whether it can handle the given query and constraints. Also in the field of description logics, decidable fragments of first order logic are used. Recently Minock [11] defined a logic that is more restricted than ours, but is closed un- der syntactic query difference.
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Peer based query rewriting in SPARQL for semantic integration of linked data

Peer based query rewriting in SPARQL for semantic integration of linked data

To address Research Question RQ2, we plan to devise a query rewriting algorithm that exploits the full semantics of our system. Firstly, we intend to investigate the possi- bility of adopting a combined approach, where the sources are partially materialised and queries are rewritten according to some of the dependencies only. To compute the per- fect rewriting, another possible approach is to devise a rewriting algorithm that produces rewritten queries in a language more expressive than FO-queries, for instance Datalog, similarly to the approach in [4] which leverages new semantics for peer-to-peer systems based on epistemic logic. Another possible target language for the rewriting algorithm is SPARQL 1.1 with property paths; the idea is to leverage the expressive power of reg- ular path queries in order to catch non-FO-rewritable constraints, such as the transitive closure of a relations. Two hypotheses follow from this approach: (a) it is possible to generate a SPARQL 1.1 query as a perfect rewriting of a conjunctive SPARQL query with respect to an RPS; (b) SPARQL 1.1 is not expressive enough: in this case we aim to characterise subsets of RPS mappings that are rewritable in SPARQL 1.1 with property paths.
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