=Paper=
{{Paper
|id=Vol-1383/paper20
|storemode=property
|title=Traffic Management Using RTEC in OWL 2 RL
|pdfUrl=https://ceur-ws.org/Vol-1383/paper20.pdf
|volume=Vol-1383
|dblpUrl=https://dblp.org/rec/conf/semweb/GormanMY14
}}
==Traffic Management Using RTEC in OWL 2 RL==
https://ceur-ws.org/Vol-1383/paper20.pdf
Traffic Management
using RTEC in OWL 2 RL
Bernard Gorman Jakub Marecek Jia Yuan Yu
IBM Research, Dublin IBM Research, Dublin IBM Research, Dublin
berngorm@ie.ibm.com jakub.marecek@ie.ibm.com jy@osore.ca
Introduction. In a number of domains, including traffic ticular bus B1 is running on a particular line L1 with a
management, event processing and situational reporting are delay of 400 seconds, as operated by operator O7. Similarly,
particularly demanding. This is due to the volumes and re- gps(B1, 53.31, -6.23, 0, 1) means that the bus B1 is at
aliability of streamed spatio-temporal data involved, ranging the given, its direction is forwards (0) and there is conges-
from sensor readings, news-wire reports, police reports, to tion (1). Based on such facts, one formulates rules, i.e. Horn
social media, as well as the complexity of the reasoning re- clauses with m > 0 and n = 1, for the processing of instan-
quired. Human, rather than artificial, intelligence is hence taneous events or non-instantaneous fluents.The occurrence
still used to an overwhelming extent. of an event E, which is an inferred Horn clause with m > 0
and n = 1, at a fixed time T , is given by rules using hap-
A number of specialised event-processing languages and rea- pensAt(E, T). The occurrence of a fluent F is at a finite
soners have been proposed, extending RDF and SPARQL. list I of intervals, is given using holdsFor(F=V, I). Simple
These include SPARQL-ST [11], Temporal RDF [14] and T- fluents, which hold in a single interval, are given by initi-
SPARQL [7], Spatio-temporal RDF and stSPARQL [9]. For atedAt(E, T) and terminatedAt(E, T). For an overview of
even more elaborate extensions, see e.g. [12, 2, 10]. Often, the predicates, please see Table 1.
these extensions rely on custom parsers for the languages
and on custom Prolog-based implementations of reasoners. Notice that Horn clauses can be used to define complex
Yet, none of these extensions has gained a wide adoption. events, such as the sharp increase in the delay of a bus
parametrised by thresholds t, d for time and delay:
We argue that such specific languages and reasoners go against
the principle of a general-purpose description logics and general- happensAt(delayIncrease(Bus, X, Y, Lon, Lat), T)
purpose reasoners [3]. We propose a rewriting of RTEC, the :- happensAt(move(Bus, _, _, Delay0), T0),
event processing calculus [2], from Prolog to OWL 2 RL [8], holdsAt(gps(Bus, X, Y, _, _)=true, T0),
which is the only profile of the Web Ontology Language, for happensAt(move(Bus, _, _, Delay), T),
which there exist very efficient reasoners. holdsAt(gps(Bus, Lon, Lat, _, _)=true, T),
Delay - Delay0 > d,
RTEC. Artikis et al. [2] proposed Event Calculus for Run- 0 < T - T0 < t
Time reasoning (RTEC) as a calculus for event process-
ing. Prolog-based implementations, where event processing
is triggered asynchronously and the derived events are pro- where comma denotes conjunction, _ is the anonymous vari-
duced in a streaming fashion, are readily available [1]. In able, and :- denotes implication.
order to make this paper self-contained, we summarise its
principles beyond the very basics [6]. The complex events can be processed in a custom Prolog-
based implementation [1], or as we show later, a OWL 2
Time is assumed to be discretised and space is represented RL reasoner [16]. In the Prolog-based implementation, one
by GPS coordinates. All predicates in RTEC are defined rewrites the inputs as facts, and leaves the reasoning about
by Horn clauses [6], which are the implications of a head delayIncrease up to a Prolog interpreter. The resulting
from a body, h1 , . . . , hn ← b1 , . . . , bm , where 0 ≤ n ≤ 1 and interactions between the ontology tools, Prolog interpreter,
m ≥ 0. All facts are predicates with m = 0 and n = 1, and rewriting among them are frail and challenging to de-
such as move(B1, L1, O7, 400), which means that a par- bug, though.
RTEC in OWL 2 RL. It has long been known that Horn
clauses can be rewritten into and queried in OWL 2. Re-
cently, it has been shown [15] that Horn clauses can be
rewritten in OWL 2 RL, a tractable profile of OWL. This
rewriting allows for sound and complete reasoning, c.f. The-
orem 1 of [16]. Moreover, the reasoning is very efficient,
empirically.
The rewriting of Zhou et al. [16] proceeds via Datalog±,∨
Table 1: Main predicates of RTEC. Cited loosely from [1].
Predicate Meaning
happensAt(E, T) Event E occur s at time T
holdsAt(F=V, T) The value of fluent F is V at time T
holdsFor(F=V, I) The list I of intervals for which F = V holds
initiatedAt(F=V, T) Fluent F = V is initiated at T
terminatedAt(F=V, T) Fluent F = V is terminated at T
relative_complement_all (I0, L, I) The list I of intervals is obtained by complementing i ∈ I0 within ground set L
union_all(L, I ) The list I of intervals is the union of those in L
intersect_all(L, I ) The list I of intervals is the intersection of those in L
[4] and Datalog [6] proper into OWL 2 RL. Instead of goals ontology languages for the semantic web. In Mechanizing
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