A WEIGHTED APPROACH FOR OPTIMISED REASONING FOR

PERVASIVE SERVICE DISCOVERY USING SEMANTICS AND

CONTEXT

Luke Steller, Shonali Krishnaswamy, Simon Cuce, Jan Newmarch

Faculty of Information Technology, Monash University, 900 Dandenong Rd, Melbourne, Australia

Seng Loke

Computer Science & Computer Engineering, La Trobe University, Bundoora, Melbourne, Australia

Keywords: Scalable semantic reasoning, context-aware discovery, service-oriented pervasive discovery architecture.

Abstract: There is an increased imperative for pervasive service discovery architectures, to aid mobile users for time

critical tasks in service rich ad-hoc environments. As such, there is a need for an innovative technology for

semantically-driven pervasive service discovery by enabling semantic reasoning engines to function in an

effective and efficient manner on resource-constrained mobile devices and by incorporating context-

awareness to provide relevant services to the user. We outline several optimisation and branch ranking

strategies for pervasive reasoning to meet this goal and provide a performance evaluation of our approach.

1 INTRODUCTION

Studies such as (Roto & Oulasvirta, 2005) have

established that mobile users typically have a

tolerance threshold of about 5 to 15 seconds in terms

of response time, before their attention shifts

elsewhere, depending on their environment. Thus,

service discovery architectures that operate in

mobile environments must cope with the very

significant challenges of not merely finding relevant

services, but being able to do so rapidly in a highly

dynamic and varying context.

The limitations of syntactic, string-based

matching for web service discovery coupled with the

emergence of the semantic web implies that next

generation web services will be matched based on

semantically equivalent meaning, even when they

are described differently (Broens, 2004), using

OWL-DL which is based on Description Logic

(DL) (Baader, Calvanese, McGuinness, Nardi &

Patel-Schneider, 2003). While current service

discovery architectures such as Jini (Arnold,

O'Sullivan, Scheifler, Waldo & Woolrath, 1999) and

UPnP (UPnP, 2007) use either interface or string

based syntactic matching, there is a growing

emergence of OWL-S semantic matchmakers such

as CMU Matchmaker (Srinivasan, Paolucci &

Sycara, 2005), LARKS (Sycara, Widoff, Klusch &

Lu, 2002), IRS-III (Cabral, Domingue, Galizia,

Gugliotta, Tanasescu et al., 2006) and DIANE

(Küster, König-Ries & Klein, 2006) which support

varying levels of semantic reasoning and

approximate matching. However, they all operate on

the basis of a centralised high-end node to perform

reasoning. There are in addition, architectures

developed specifically for the pervasive service

discovery domain which are driven by context, such

as MobiShare (Doulkeridis, Loutas & Vazirgiannis,

2005), COSS (Broens, 2004) and CASE (Sycara et

al., 2002) which are either syntactic or require the

presence of a centralised high-end node.

This reliance on a high-end, centralised node for

performing semantically driven pervasive service

discovery can clearly be attributed to the fact that

semantic reasoners used by these architectures (such

as FaCT++ (FaCT++, 2007), RacerPro (RacerPro,

2007) and KAON2 (KAON2, 2007)) are all resource

intensive. Therefore, they are unsuitable for

deployment on small resource constrained devices,

such as PDAs and mobile phones. These small

devices which are typical in the context of mobile

service discovery are quickly overwhelmed when

the search space in terms of ontology size and

reasoning complexity increases. Alternatively,

113

Steller L., Krishnaswamy S., Cuce S., Newmarch J. and Loke S. (2008).

A WEIGHTED APPROACH FOR OPTIMISED REASONING FOR PERVASIVE SERVICE DISCOVERY USING SEMANTICS AND CONTEXT.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - SAIC, pages 113-118

DOI: 10.5220/0001694401130118

 SciTePress

KRHyper (Kleemann, 2006) is a First Order Logic

(FOL) reasoner which implements FOL counterparts

of standard DL optimisations (Horrocks & Patel-

Schneider, 1999) and functions on a small device.

However, it too, suffers from out of memory

exceptions when the reasoning task is too large and

no response is given. Clearly, this shows that

existing reasoning approaches cannot be directly

ported to a mobile device in their current form.

The reality of mobile environments is a world

characterised by ad-hoc an intermittent connectivity

where such reliance on remote/centralised

processing (and continuous interaction) may not

always be possible or desirable given the need for

rapid processing and dynamically changing context.

Pervasive service discovery has to necessarily be

under-pinned by the current context to meet the all-

important criteria of relevance in constantly

changing situations. The communication overhead

(not to mention the infeasibility/impracticability) of

constantly relaying contextual and situational

changes of the user/device to a central server will

lead to inevitable delays. Furthermore, reasoning on

a central server about sensitive historical user

profiling data, used to select the best service for the

user, raises privacy concerns (Kleemann, 2006).

Thus there is a clear imperative that for

semantically driven pervasive service discovery to

meet the very real response-time challenges of a

mobile environment, the capacity to perform

matching and reasoning must occur on the resource

limited device itself. Therefore, there is a need for a

pervasive service discovery architecture, which

more flexibly manages the trade-off between

computation time and precision of results, depending

on the available resources on the device.

The remainder of the paper is structured as

follows. In section 2 we outline our strategies for

optimised reasoning and formally describe these in

section 3. Section 4 provides an implementation and

performance evaluation and conclude in section 5.

2 APPROACH – REASONING

FOR PERVASIVE SERVICE

DISCOVERY

In this section we discuss current Tableaux semantic

reasoners and present our optimisations and ranking

algorithms, for the Tableaux algorithm.

2.1 Semantic Reasoners

The effective employment of semantic languages

such as OWL requires the use of semantic reasoners

such as Pellet (Pellet, 2007), FaCT++ (FaCT++,

2007), RacerPro (RacerPro, 2007) and KAON2

(KAON2, 2007). Most of these reasoners utilise the

Tableaux (Horrocks & Sattler, 2005) algorithm with

standard DL optimisations (Horrocks et al., 1999),

due to its efficiency. Tableaux reasoners reduce all

reasoning tasks to a consistency check, in which

disjunctions form combinations of branches in the

reasoner. If all branches contain a clash, where a fact

and its negation are both asserted, then clash is

proven for all models of the knowledge base.

Tableaux can be used to check whether an individual

I representing a service, matches a request RQ (ie.

∈RQ), by negating RQ. If the clash is proven for

all models then I

∈RQ membership, is proven. In

the next section we provide a case study to motivate

the need for reasoning in pervasive discovery.

2.2 Case Study – Searching for a

Printer

Bob wishes to print a document from his PDA and

issues a service request to find a black and white,

laser printer which supports a wireless network

protocol such as Bluetooth, WiFi or IrDA. This is an

extension of (Steller, Krishnaswamy & Newmarch,

2006). Equations 2-3 show Bob’s request in DL

form, while equation 4 presents a matching printer.

Request ≡ WNet

∩ ∃ hasColour.{Black}

∩

LaserPrinterOperational

(1)

WNet ≡

∃ hasComm.{BT} ∪

∃ hasComm.{WiFi}

∪

∃ hasComm.{IrDA}

(2)

LaserPrinterOperational ≡ Printer

∩

∃ hasCartridge. {Toner} ∩

≥ 1 hasOperationContext

(3)

Printer(LaserPrinter1),

hasColour (LaserPrinter1, Black),

hasCartridge(LaserPrinter1, Toner),

hasComm(LaserPrinter1, BT),

hasOperationContext(LaserPrinter1, Ready)

(4)

Equation 1 defines three attributes in the request,

the first is unfolded into equation 2, requiring

support for either Bluetooth, WiFi or IrDA, the

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114

second attribute specifies a black and white

requirement and the third is unfolded into equation

3, specifying a printer which has a toner cartridge

and at least one operational context. Equation 4

fragment defines the LaserPrinter1 individual as

meeting the service request. A DL Tableaux excerpt

proving the truth of LaserPrinter1

∈ Request, shows

a clash for all branches, as follows (only two request

attributes included for briefity):

Add: ⌐Request to Individual:

LaserPrinter1

⌐Request ≡ ⌐WNet

∪

⌐

∃ hasColour.{Black}

Apply Disjunction Element:

⌐WNet ≡ ∀ hasComm.{⌐BT} ∩

∀

hasComm.{⌐WiFi}

∩

∀ hasComm.{⌐IrDA}

Add: ⌐BT to Nominal: BT, CLASH

Apply Disjunction Element:

∀ hasColour.⌐{Black}

Add: ⌐{Black} to Nominal:

Black, CLASH

2.3 Optimisation Strategies

We observed that DL Tableaux reasoners leave

scope for further optimisation to enable reasoning on

small/resource constrained devices with a significant

improvement to response time and avoiding

situations such as “Out of Memory” errors

encountered in (Kleemann, 2006). Our algorithm

involves a range of optimisation strategies such as:

1. associating weight values with individuals and

disjunctions, 2. selective application of consistency

rules, 3. ranking disjunctions, 4. ranking individuals,

and 5. skipping disjunctions.

Weighted individuals and disjunctions can be

established using a weighted queue. Disjunctions

with the highest weight are branched on first.

Application of consistency rules to only a subset

CX of individuals, and only branching on

disjunctions related to those individuals, reduces the

size of the consistency problem. This subset can be

established using the universal quantifier construct

of the form

∀ R.C = { ∀ b.(a, b)∈ R →b∈ C}

(Baader et al., 2003), where R denotes a role relation

and C denotes a class concept. It implies that all

object fillers of role R, are of type C, resulting in

adding the role filler type C to all objects for the

given role R. Since this can give rise to an

inconsistency, we define the subset CX as limited to

the original individual I being checked for

membership to the request RQ and all those

individuals which relate to this individual I, via roles

R specified in universal quantifiers.

Disjunctions and individuals in weighted queues,

can be ranked by recursively checking an unapplied

disjunction or element for a potential future clash. If

a pathway to a clash is found, the weighted values of

all individuals and disjunctions involved in this path

are increased. Disjunctions can also be applied or

skipped, according to whether they relate to the

request type RQ, or not, respectively.

3 TABLEAUX OPTIMISATION

AND RANKING ALGORITHMS

In this section we formally describe each

optimisation strategy from the previous section.

3.1 Weighted Queue

A queue contains a weighted object and weight

value object value pair. Let WO

and WV

denote

these objects, where i denotes the current object. Let

Q denote the queue where Q = {WO

, WO

.. WO

Let WO

denote the next WO to be returned by the

queue. Each WV object is associated with an integer

weight value representing the current weight of the

object, let IV

denote this value. Let MV denote the

highest IV in the queue. MV = max(IV

[1..n]

) and IV

has the range 0 ≤ IV

[1..n]

≤ MV. Let NW denote a

normalised decimal weight value where 0 ≤ NW ≤ 1,

calculated on the fly, with: NW

= IV

/ MV.

Let Q

ind

denote the individual queue and let Q

disj

denote the disjunction queue. Let WO

ind

and WO

disj

denote individuals and disjunctions, respectively, as

weighted objects, such that Q

ind

= {WO

ind

, WO

ind

} and Q

disj

= {WO

disj

, WO

disj

.. WO

disj

}

where n may be different for each queue Q. Each

ind

contains a separate Q

disj

. Weighted objects

in any queue Q are ordered by their NW

descending order [1..0] and several WO

objects can

have the same NW

. When a WO

disj

is applied it is

removed from the queue and there are no more

disjunctions to apply when Q

disj

≡ {}. In a Q

disj

the

next disjunction is WO

disj

= WO

disj

The individual queue Q

ind

has a floating weight

threshold value, let WT denote this value. WT is

initially set to the highest weight in the queue, WT =

max(NW

[1..n]

). Let WTS denote the set of individuals

ind

, with NW

≥ WT. The next individual

ind

is the next element in WTS, which is

repeatedly iterated over. When all WO

ind

elements

in WTS contain a Q

disj

≡ {}, WT is set to the next

A WEIGHTED APPROACH FOR OPTIMISED REASONING FOR PERVASIVE SERVICE DISCOVERY USING

SEMANTICS AND CONTEXT

115

highest weight NW, in Q

ind

, NW = max(NW

[1..n]

) <

WT.

3.2 Selectively Apply Consistency

Rules

Individuals are iteratively added to the weighted

individual queue Q

ind

, as follows. Let CX denote the

set of individuals which were last added to Q

ind

Originally, CX = {X}. Loop individuals in set CX.

Let Y denote the current individual from the CX. Let

AV denote a universal quantifier expression and let

AVS denote the set of all universal quantifiers for

individual Y such that AVS = {AV

, AV

...AV

Let R

denote the relation to which AV

relates to

and let RS denote a set of all distinct R relations to

which any AV

in the set AVS, relates. Let OS

denote the a set containing those individuals which

are objects O of any role R

in RS, for the individual

Y, such that OS = {O

, O

..O

}. Add all of the

elements in OS to the weighted individual queue

ind

and increment the weight value WV of these

individuals by 1. Set CX = OS.

3.3 Rank Individuals and Disjunctions

The following algorithm is used by both the rank

individual and rank disjunction strategies to

establish a path of individuals and disjunctions to a

potential clash. Let CS denote this path. The path

may be via conjunction/disjunction elements and

role fillers of universal quantifiers. When ranking

individuals let C denote the last applied non-clashing

disjunction element, let WO

ind

denote the individual

to which the disjunction relates, let CS = {} and

execute ClashDetect once. When ranking

disjunctions, execute ClashDetect once, for each

disjunction WO

disj

in the disjunction queue Q

disj

, of

each individual WO

ind

in the individual queue Q

ind

and let WO

ind

= WO

ind

, C = WO

disj

and CS = {}. If

the algorithm returns a non-empty clash path set CS

≠ {}, increment the weight value WV for all the

elements (individuals and disjunctions) within CS by

1. The pseudo code for ClashDetect is given below:

ClashDetect:

Inputs(WO

ind

, C, CS), Outputs(Set).

If C is primitive, negation, normal or

value, then:

If WO

ind

has type negation of C,

then:

CS = WO

ind

+ CS. return CS.

Else:

UCS{} = unfold(C).

for each UC in UCS:

CS = ClashDetect(WO

ind

UC, CS).

If CS is not null,

then: return CS.

If C is a disjunction, then:

For each disjunction element E

in C:

CSNEW{} = ClashDetect(WO

ind

E, CS).

If CSNEW is null,

then: return null.

CS = CS + CSNEW.

If C is a conjunction, then:

Create CSS (a set).

For each conjunction element E in C:

CSNEW{} = ClashDetect(WO

ind

E, CS).

CSS = CSS + CSNEW.

CS = SelectBestCS(CSS).

Return CS.

If C is a universal quantifier,

then:

AVR = Role of C.

AVC = Role filler type of C.

OS{} = all the objects of

ind

for role AVR. CSS = {}.

For each individual O in OS:

CSNEW{} = ClashDetect(O,

AVC, CS).

CSS = CSS + CSNEW.

CS = SelectBestCS(CSS).

Return CS + WO

ind

Note SelectBaseCS selects the best clash

pathway which has fewest disjunctions and depends

on types which were added by the earliest branches.

3.4 Disjunction Skipping

The following strategy determines whether

disjunctions are applied to create a new branch or

skipped. Let C denote the service request type

definition, which is a conjunction. Let DC = {T

.. T

} denote a set of valid class types. Let DI

denote any disjunction being added to the

disjunction queue Q

disj

, by the reasoner or other

optimisation strategy. DI is of the form DI = {D

..D

}, where D

is a disjunction element. Let

NND

denote D

in non-negated form (as a positive

term). If DC contains any NND

[1..m]

from the

disjunction DI, then DI is added to Q

disj

with a

weighting of 1, otherwise it is skipped.

The set DC is filled using the following

population algorithm. Where C (service request) is a

conjunction or disjunction list of the form C = {E

..E

}, let E

denote an element of the list. Let

NNE

denote E

in non-negated form. Add all

elements NNE

[1..o]

to DC, set DC = DC + NNE

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Table 1: Optimisation strategies enabled in each test.

Test # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Selective Consistency × × × × × × × ×

Rank Disjunctions × × × × × × ×

Rank Individuals × × × × × × × ×

Skip Disjunctions × × × × × × ×

Recursively reapply this DC population

algorithm on each E

[1..o]

by setting C = E

. If E

is a

primitive or nominal type, unfold each E

and NNE

into a new set UCS, such that UCS = {UC

..UC

}, and then also recursively reapply this

population algorithm on each unfolded type UC

setting C = UC

4 PERFORMANCE EVALUATION

We implemented the optimisation strategies defined

in section 3, in the Pellet v1.5 reasoner. We selected

Pellet because it is open source while the other

reasoners were not, allowing us to provide a proof of

concept and compare performance with and without

the strategies enabled.

The evaluation was performed on a HP iPAQ

hx2700 PDA, with Intel PXA270 624Mhz

processor, 64MB RAM, running Windows Mobile

5.0 with Mysaifu Java Virtual Machine (JVM), J2SE

allocated 15mb of memory.

We implemented the scenario outlined in section

2.3 to create ontologies containing 141 classes, 126

roles and 337 individuals. Due to the resource

intensive nature of XML parsing, we pre-parsed

OWL XML files into text files of triples and

postpone XML parsing to future work.

We executed a single consistency check to

compare matching individual LaserPrinter1, against

the service request, 15 times using various randomly

selected combinations of our strategies, as shown in

table 1, where test 11 represents normal Tableaux

execution (no optimisations). Tests 1-11 provided

the expected positive matching result and 12-15 did

not complete due to lack of memory.

Figure 1 illustrates the performance of each

successful test in terms of time (seconds).

Consistency time involves application of consistency

rules and branching, to perform the Tableaux

consistency check for LaserPrinter1

∈ Request.

This also encompasses the overhead cost for

performing the optimisations, which is also shown

separately. The total includes consistency time as

well as the time required for preparing the reasoner

(eg loading triple text files into the reasoner).

As observed in Figure 1, the our optimisation

strategies considerably reduce the consistency time

required to find a clash for all models of the query

compared to test 11 which represents normal

execution of Tableaux. We found that consistency

time was influenced by the number of branches and

rules applied.

Figure 2 presents a breakdown of how much time

each strategy contributed to the overhead cost. Tests

8, 9 and 10 suffered particularly costly optimisation

overhead, due to ranking disjunctions. This was

because either selective consistency or skip

disjunctions, or both, were disabled, resulting in

more disjunctions to rank.

Test 5, 7 and 2 show that selective consistency

and skip disjunctions are the most effective

optimisations, especially when used together, and

have low overheads. Rank disjunction and individual

strategies were found to reduce the number of

branches applied, but did not provide any

performance improvement due to the high overhead.

Our performance tests show that some of our

optimisations and ranking algorithms are very

effective in improving Tableaux reasoning

performance on resource limited devices, compared

with no optimisations. This makes mobile reasoning

feasible on resource constrained devices.

5 CONCLUSION AND FUTURE

WORK

Our optimisation strategies were shown to

significantly improve the performance of reasoning

tasks on small resource constrained devices, making

deployment on these devices feasible. Some

strategies performed well, while others require

future work and we implementing a greater number

of scenarios to test our strategies more thoroughly.

We will also leverage our weighted approach, to

adaptively skip branches which have a lower weight,

when resources are low. This will provide a result

with a level of uncertainty rather than “Out Of

Memory” errors, to better manage the trade-off

between resource availability and result precision. In

addition, although we demonstrated the use of

A WEIGHTED APPROACH FOR OPTIMISED REASONING FOR PERVASIVE SERVICE DISCOVERY USING

SEMANTICS AND CONTEXT

117

100

150

200

250

300

350

1234567891011

Test Number

Time (seconds)

Total

Consistency

Optimisation Overhead

Figure 1: Processing time taken to perform each test.

1234567891011

Test Numbe

Time (seconds)

Selective Consistency

Rank D isjuncti ons

Rank Individuals

Skip Disjunctions

Figure 2: Optimisation strategy overhead breakdown.

context-aware attributes in our scenario, we will use

context to pre-emptively rank the pool of

discoverable services based on user preferences and

current user context, before a request takes place.

These services will be matched first as they are more

likely to meet the user’s needs.

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