OWLIM: A family of scalable semantic repositories

Bishop, Barry; Kiryakov, Atanas; Ognyanoff, Damyan; Peikov, Ivan; Tashev, Zdravko; Velkov, Ruslan

doi:10.3233/sw-2011-0026

Cited by 152 publications

(114 citation statements)

References 14 publications

(8 reference statements)

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…The most popular approach is to extensively materialize a dedicated index for each permutation. This was initially proposed by YARS [43], and further explored in RDF3X [10,12,13,26,63]. Also Hexastore [93] proposes a six-way permutation-based indexing, but implemented it using hierarchical in-memory Java hash maps.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Low-level Storage of Very Large Knowledge Graphs

Urbani

Jacobs

2020

Proceedings of the Web Conference 2020

View full text Add to dashboard Cite

The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 10 11 edges using inexpensive hardware, delivering competitive performance on multiple workloads.

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive Low-level Storage of Very Large Knowledge Graphs

Urbani

Jacobs

2020

Proceedings of the Web Conference 2020

View full text Add to dashboard Cite

show abstract

“…Second Round. Set ∆ does not contain P or R meta-facts, so rule (5) is not matched in the second round. However, in rule (6), we can match S(x, ) to S(h, j) in ∆, and we can match T ( , z) to T (e, f).…”

Section: Our Approachmentioning

confidence: 99%

Datalog Reasoning over Compressed RDF Knowledge Bases

Pan

Urbani

Motik

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems. CCS CONCEPTS• Information systems → Data management systems.

show abstract

“…DS rules are mainly used for reasoning assertional data, but RS rules can reason both terminological and assertional data. Ruleentailment reasoners using RS rules include BaseVISor [28], OWLIM [9], Bossam [24], MiRE4OWL [26], the Jena forward chaining engine [12] and EYE 1 .…”

Section: Semantic Reasoning Approachesmentioning

confidence: 99%

“…It is often deemed that the transitive, reflexive and symmetric semantics of owl:sameAs can cause considerable inflation in the number of inferred triples [9,61]. One approach to such an issue is to use a single entity as a representative of a group of identical entities [9]. The research in [61] uses an identify-and-merge approach to build large sameAs cliques in a (comparatively) small memory.…”

Section: Scalable Rule-based Ontology Reasoningmentioning

confidence: 99%

See 1 more Smart Citation

Resource-constrained reasoning using a reasoner composition approach

2015

View full text Add to dashboard Cite

To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resourceconstrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a "well-suited" amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices.

show abstract

OWLIM: A family of scalable semantic repositories

Cited by 152 publications

References 14 publications

Adaptive Low-level Storage of Very Large Knowledge Graphs

Adaptive Low-level Storage of Very Large Knowledge Graphs

Datalog Reasoning over Compressed RDF Knowledge Bases

Resource-constrained reasoning using a reasoner composition approach

Contact Info

Product

Resources

About