LUBM: A benchmark for OWL knowledge base systems

Guo, Yuanbo; Pan, Zhengxiang; Heflin, Jeff

doi:10.1016/j.websem.2005.06.005

Cited by 969 publications

(202 citation statements)

References 11 publications

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“…Request permissions from permissions@acm.org. Recent graph benchmarking initiatives focus on three key areas: (1) transactional workloads consisting of interactive read and update queries [4,6,12], (2) graph analysis algorithms [5,11,19,23], and (3) inferencing/matching on semantic data [1,17,22,30,32].…”

Section: Introductionmentioning

confidence: 99%

An early look at the LDBC social network benchmark's business intelligence workload

Szárnyas

Prat-Pérez²,

Averbuch

et al. 2018

Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

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In this short paper, we provide an early look at the LDBC Social Network Benchmark's Business Intelligence (BI) workload which tests graph data management systems on a graph business analytics workload. Its queries involve complex aggregations and navigations (joins) that touch large data volumes, which is typical in BI workloads, yet they depend heavily on graph functionality such as connectivity tests and path finding. We outline the motivation for this new benchmark, which we derived from many interactions with the graph database industry and its users, and situate it in a scenario of social network analysis. The workload was designed by taking into account technical "chokepoints" identified by database system architects from academia and industry, which we also describe and map to the queries. We present reference implementations in openCypher, PGQL, SPARQL, and SQL, and preliminary results of SNB BI on a number of graph data management systems.

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Section: Introductionmentioning

confidence: 99%

An early look at the LDBC social network benchmark's business intelligence workload

Szárnyas

Prat-Pérez²,

Averbuch

et al. 2018

Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

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“…Table 2 shows the comparison of response times for LUBM query 2 [13] for these three algorithms after adding an existing student as a member of an existing department to the knowledge base. Table 3 shows the comparison of response times for LUBM query 6 [13] for these three algorithms after adding a new undergraduate student.…”

Section: Methodsmentioning

confidence: 99%

“…For example, consider a scenario based on LUBM [13]. A university, University0 has hired professor, Fullprofessor0.…”

Section: Conservative Trust Assessmentmentioning

confidence: 99%

Trust and hybrid reasoning for ontological knowledge bases

Shi

Maly

Zeil

2014

Proceedings of the 23rd International Conference on World Wide Web

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Projects such as Libra and Cimple have built systems to capture knowledge in a research community and to respond to semantic queries. However, they lack the support for a knowledge base that can evolve over time while responding to queries requiring reasoning. We consider a semantic web that covers linked data about science research that are being harvested from the Web and are supplemented and edited by community members. We use ontologies to incorporate semantics to detect conflicts and resolve inconsistencies, and to infer new relations or proof statements with a reasoning engine. We consider a semantic web subject to changes in the knowledge base, the underlying ontology or the rule set that governs the reasoning. In this paper we explore the idea of trust where each change to the knowledge base is analyzed as to what subset of the knowledge base can still be trusted. We present algorithms that adapt the reasoner such that, when proving a goal, it does a simple retrieval when it encounters trusted items and backward chaining over untrusted items. We provide an evaluation of our proposed modifications that show that our algorithm is conservative and that it provides significant gains in performance for certain queries.

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“…The VIVO-ISF (Integrated Semantic Framework) ontology [25] includes research concepts and relationships, as well as several basic educational concepts, such as the actors involved in education (professors), and the educational products (course, workshop, etc.). The HERO (Higher Education Reference Ontology) models the characteristics of university domain [26], while the Univ-Bench represents the university domain and facilitates the evaluation of Semantic Web repositories [27].…”

Section: Modeling Academic Activitymentioning

confidence: 99%

Creating the Context for Exploiting Linked Open Data in Multidimensional Academic Ranking

Triperina

Sgouropoulou

Xydas

et al. 2015

Int. J. Recent Contrib. Eng. Sci. IT

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Abstract-Academia is a complex socio-technical system with multiple aspects and constituents that involve various stakeholders. In order to address stakeholders' needs and to assist the institutional accountability, this complexity should be considered during the development of academic services. We have designed a dynamic multidimensional ranking approach, easily modifiable to address user requirements, so as to assess and compare the university performance with a clear view to the support of effective institutional strategic planning and policy making. Our approach comprises the following components: the AcademIS ontology to model the academic domain and its multiple dimensions, the AcademIS Information System to manage and display the academic information, published in Linked Open Data format and the visual-aided Multiple Criteria Decision Making component, to evaluate and rank the performance of the academic units. The data are aggregated from several sources, in different formats, LODified by our system, and presented to the user by the interface to ultimately assist the decision making process.

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LUBM: A benchmark for OWL knowledge base systems

Cited by 969 publications

References 11 publications

An early look at the LDBC social network benchmark's business intelligence workload

An early look at the LDBC social network benchmark's business intelligence workload

Trust and hybrid reasoning for ontological knowledge bases

Creating the Context for Exploiting Linked Open Data in Multidimensional Academic Ranking

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