Semantics and Expressive Power of Subqueries and Aggregates in SPARQL 1.1

Kaminski, Mark; Kostylev, Egor V.; Grau, Bernardo Cuenca

doi:10.1145/2872427.2883022

Cited by 20 publications

(16 citation statements)

References 38 publications

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“…This paper extends a conference publication [34] by providing extended discussions and examples as well as the following major technical contributions: (i) complete proofs of all our results; (ii) a principled fix to the normative semantics of exists expressions, and a comprehensive analysis of the impact of this new semantics on the expressive power of query nesting and aggregation 3. Main expressivity results of this paper.…”

Section: Related Workmentioning

confidence: 80%

Query Nesting, Assignment, and Aggregation in SPARQL 1.1

Kaminski

Kostylev

Grau

2017

ACM Trans. Database Syst.

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Answering aggregate queries is a key requirement of emerging applications of Semantic Technologies, such as data warehousing, business intelligence and sensor networks. In order to fulfil the requirements of such applications, the standardisation of SPARQL 1.1 led to the introduction of a wide range of constructs that enable value computation, aggregation, and query nesting. In this paper we provide an in-depth formal analysis of the semantics and expressive power of these new constructs as defined in the SPARQL 1.1 specification, and hence lay the necessary foundations for the development of robust, scalable and extensible query engines supporting complex numerical and analytics tasks.

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Section: Related Workmentioning

confidence: 80%

Query Nesting, Assignment, and Aggregation in SPARQL 1.1

Kaminski

Kostylev

Grau

2017

ACM Trans. Database Syst.

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“…Aggregate atoms in our language provide a restricted form of aggregation over what is available in standard query languages such as SPARQL 1.1 [13,18]. An important restriction is that the value computed by the corresponding aggregate function is immediately compared to a constant and thus the atom is evaluated to either true or false in any given graph and valuation.…”

Section: Extended Faceted Queriesmentioning

confidence: 99%

“…It is known that core faceted queries are expressible in the standard RDF query language SPARQL [1]. Similarly, extended faceted queries allow for a direct translation to the current version of this language, SPARQL 1.1 [13,18]. In particular, it has aggregation functionality, which captures aggregate atoms in faceted queries, and property paths, which capture reachability atoms.…”

Section: Definition 5 a Reachability Atom Is An Expression Of The Formmentioning

confidence: 99%

Semantic Faceted Search with Aggregation and Recursion

Sherkhonov

Grau

Kharlamov

et al. 2017

Lecture Notes in Computer Science

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Faceted search is the de facto approach for exploration of data in e-commerce: it allows users to construct queries in an intuitive way without a prior knowledge of formal query languages. This approach has been recently adapted to the context of RDF. Existing faceted search systems however do not allow users to construct queries with aggregation and recursion which poses limitations in practice. In this work we extend faceted search over RDF with these functionalities and study the corresponding query language. In particular, we investigate complexity of the query answering and query containment problems.:Samsung S8

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“…For example, Leo Kant is a jobseeker registered with us, "Leo_Kant" becomes an entity with attribute values (jobseeker ID 80108, first name "Leo", last name "Kant", salutation "Mr", login "lkant", etc.) From Leo"s profile documents (resume and cover letter) that are either in HTML or MS Word file format, we have to transform these raw content into semantic metadata in resource description framework (RDF) format; RDF [46,12,24] includes entities, events and facts with entities scores [2,11,14,32,40,48]. In this Leo Kant"s documents, there are 2 countries "India" occurring 6 times and "Canada" appearing 4 times, many IT skills are detected "ms sql server" and "visual basic", etc.…”

Section: Related Workmentioning

confidence: 99%

Semantic Search on Applicant Tracking System

Ha¹,

Rahman²

2017

International Journal of Advanced Research in Computer and Comm

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Web search engines often federate many user queries to relevant structured databases. For example, a recruitment-related query might be federated to a jobseekers-and-employers database containing their resumes and skills. The relevant structured data items are then returned to the user along with web search results. Though each structured database is searched in isolation, the search often produces empty / incomplete results as the database may not contain the required information to answer the query. Starting from our Applicant Tracking System (ATS), we have 16 development databases of over 650,000 profile documents of resumes / cover letters / skills. There are on average 238 keywords per document. In fact, per minute there can be up to 200,000 transactions within all these databases. Our existing traditional database search technique (by full-text keyword PostgreSQL search) can be frozen or taking very long to respond unless if we cut off / only search from top profiles, we will not have the search results ready by thirty seconds, but this cut-off limitation returned incorrect results; for example for a query "Jet fuel Thermal Oxidation" to request information about job seekers whose resumes contain skills in Oil and Gas industry, in the top ten results there was a conflict in relevance ranking. In order to research a more suitable full-text keyword search technique better than the existing database search, we considered employment of semantic search models. Our semantic search technique has 88% -91.22% accuracy with very much quicker queries that can help users to make a search of 4 keywords of skills completed from 1 second to 28 seconds. Furthermore, the semantic search engine becomes very strong that users can search by entering a whole text paragraph. We designed a combination of semantic search that look for web pages per search and database search.

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Semantics and Expressive Power of Subqueries and Aggregates in SPARQL 1.1

Cited by 20 publications

References 38 publications

Query Nesting, Assignment, and Aggregation in SPARQL 1.1

Query Nesting, Assignment, and Aggregation in SPARQL 1.1

Semantic Faceted Search with Aggregation and Recursion

Semantic Search on Applicant Tracking System

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