C-ASP: Continuous ASP-Based Reasoning over RDF Streams

We introduce a novel logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the $${{\mathcal I}^2}$$ -DLV system. The architecture allows to take advantage from both the powerful distributed stream processing capabilities of Flink and the incremental reasoning capabilities of $${{\mathcal I}^2}$$ -DLV, based on overgrounding techniques. Besides the system architecture, we illustrate the supported input language and its modeling capabilities, and discuss the results of an experimental activity aimed at assessing the viability of the approach.

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“…2010;Phuoc et al . 2011;Pham et al . 2019;Hoeksema and Kotoulas 2011), the proposals that relate most with ours are the ones based on ASP.…”

Section: Related Workmentioning

confidence: 99%

“…2010;Phuoc et al . 2011;Hoeksema and Kotoulas 2011;Pham et al . 2019) in contexts such as Complex Event Processing (CEP), Semantic Web and Knowledge Representation and Reasoning (KRR).…”

Section: Introductionmentioning

confidence: 99%

I-DLV-sr: A Stream Reasoning System based on I-DLV

Calimeri¹,

Manna²,

Mastria³

et al. 2021

Theory and Practice of Logic Programming

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“…deciding model existence goes from NP to PSPACE, depending on the setting). On a similar line, i.e., using rule languages on top of RDFS, and borrowing non-monotone semantics developed within rules languages, are also all other approaches we are aware of, such as [8,11,12,14,16,17,18,54,55,65] and practical large scale solutions such as [9,10,53,73,72,77,78]. Let us also note that there are more general solutions in providing a rule layer on top of ontology languages such as RDFS and OWL, which can be found in e.g., [32,33,34,35,36].…”

Section: Structural Propertiesmentioning

confidence: 99%

Defeasible RDFS via Rational Closure

Casini¹,

Straccia²

2020

Preprint

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In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a prominent approach. In recent years, RC has gained even more popularity in the context of Description Logics (DLs), the logic underpinning the semantic web standard ontology language OWL 2, whose main ingredients are classes and roles.In this work, we show how to integrate RC within the triple language RDFS, which together with OWL 2 are the two major standard semantic web ontology languages. To do so, we start from ρdf, which is the logic behind RDFS, and then extend it to ρdf ⊥ , allowing to state that two entities are incompatible. Eventually, we propose defeasible ρdf ⊥ via a typical RC construction.The main features of our approach are: (i) unlike most other approaches that add an extra nonmonotone rule layer on top of monotone RDFS, defeasible ρdf ⊥ remains syntactically a triple language and is a simple extension of ρdf by introducing some new predicate symbols with specific semantics. In particular, any RDFS reasoner/store may handle them as ordinary terms if it does not want to take account for the extra semantics of the new predicate symbols; (ii) the defeasible ρdf ⊥ entailment decision procedure is build on top of the ρdf ⊥ entailment decision procedure, which in turn is an extension of the one for ρdf via some additional inference rules favouring an potential implementation; and (iii) defeasible ρdf ⊥ entailment can be decided in polynomial time. SyntaxWe rely on a fragment of RDFS, called minimal ρdf [63, Def. 15], that covers essential features of RDFS. Specifically, minimal ρdf is defined as the following subset of the RDFS vocabulary: ρdf = {sp, sc, type, dom, range} .Moreover, it does not consider so-called blank nodes and, thus, in what follows, triples and graphs will be ground. In fact, minimal ρdf suffices to illustrate the main concepts and algorithms we will consider in this work and ease the presentation. To avoid unnecessary redundancy, we will just drop the term 'minimal' in what follows.So, consider pairwise disjoint alphabets U and L denoting, respectively, URI references and literals. 2 We assume that U contains the ρdf vocabulary. A literal may be a plain literal (e.g., a string) or a typed literal (e.g., a boolean value) [61]. We call the elements in UL terms. Terms are denoted with lower case letters a, b, . . . with optional super/lower script.A triple is of the form τ = (s, p, o) ∈ UL × U × UL, 3 where s, o / ∈ ρdf. We call s the subject, p the predicate, and o the object.A graph G is a set of triples, the universe of G, denoted uni(G), is the set of terms in UL that occur in the triples of G.We recall that informally (i) (p, sp, q) means that property p is a subproperty of property q; (ii) (c, sc, d) means that class c is a subclass of class d; (iii) (a, type, b) means that a is of type b; (iv) (p, dom, c) means that the domain of property p is c; and (v) (p, range, c) means that the range of property p is c.We extend the vocabulary of ρdf with a new pair of predicates, ⊥ c and ⊥ p , ...

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“…Over the past few years, different approaches to SR have been proposed, based on Complex Event Processing (CEP), Semantic Web or Knowledge Representation and Reasoning (KRR) [3][4][5][6]. However, to date, there are no standardized formalisms nor techniques for SR; this complicates the comparison between different approaches that are based on different languages, semantics and technologies.…”

Section: Introductionmentioning

confidence: 99%

EnviroStream: A Stream Reasoning Benchmark for Environmental and Climate Monitoring

Mastria,

Pacenza,

Zangari

et al. 2023

BDCC

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Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application scenarios such as IoT, Smart Cities, Emergency Management, and Healthcare, despite being a relatively new field of research. The current lack of standardized formalisms and benchmarks has been hindering the comparison between different SR approaches. We proposed a new benchmark, called EnviroStream, for evaluating SR systems on weather and environmental data. The benchmark includes queries and datasets of different sizes. We adopted I-DLV-sr, a recently released SR system based on Answer Set Programming, as a baseline for query modelling and experimentation. We also showcased continuous online reasoning via a web application.

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C-ASP: Continuous ASP-Based Reasoning over RDF Streams

Cited by 5 publications

References 8 publications

I-DLV-sr: A Stream Reasoning System based on I-DLV

I-DLV-sr: A Stream Reasoning System based on I-DLV

Defeasible RDFS via Rational Closure

EnviroStream: A Stream Reasoning Benchmark for Environmental and Climate Monitoring

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