Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides enabling technologies to annotate and match services' descriptions with a users' features, interests and preferences, thus allowing for more efficient access to services and then information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? To this end, we exploit Description Logics reasoning through semantic matching to i) identify useful parts of a user profile that satisfy services requirements (i.e., input parameters) and ii) compute the description required by a service to be executed but not provided by the profile. Finally, the scalability of our approach has been evaluated through its integration in the service consumption of the EC-funded project SOA4All.
Personalization in web-based applications [1], as a global tendency nowadays, aims at alleviating the burden of information overload by tailoring the information presented based on an individual and immediate user’s needs. Between the numerous examples that can be found all across the web, we can highlight the proliferation of personalized home sites, such as iGoogle (http://www.google.com/ig) or netvibes (http://www.netvibes.com), but also the fact that many other web applications of different kinds treat user configuration as one of their most prominent characteristics. From collaborative [2] and content [3] to hybrid-based [4], various personalization techniques have been introduced, depending on the data they manipulate and personalization levels they achieve. In most of these approaches, the user profile, as a collection of data modelling the user extended with its interests, its preferences and context, is a prominent element to ensure accurate and efficient personalized access to information.
In recent years, web service [5], as an emergent technology to consume information on the web, has benefited from research progress in web personalization. Indeed, many approaches addressing user-centric and preference-based consumption of services and more specially their publication [6], discovery [7], selection [8] and execution [9] have emerged. The possibility to customize their results [10] even goes further by giving the users the chance to experience those services in a personalized fashion, which is prominent in order to permit the users to fulfil their desires more suitably.
However most of these approaches ensure personalization by collecting and analizing syntactic content of user profile and services description e.g., [11]. This under specification limits the accuracy of personalization and their automation. Towards this issue, the semantic web [12], where the semantic content of the information is tagged using machine-processable languages, provides many advantages over the current ”formatting only” version of the web, its services and users. OWL [13], as one of its Web Ontology Language which is based on Description Logics (DLs) [14], aims at modelling knowledge on the web through ontologies i.e., formal conceptualization of a particular domain. Therefore, services with their functionalities (i.e., input and output parameters, preconditions, effects and invariants) and user profile with their interests, preferences can be both annotated and then enhanced using semantic descriptions. Such annotations are one important features to enable reasoning on services and user profile descriptions, hence automation of personalized tasks such as the consumption of services.
In this work, we address automated instantiation of services, part of personalization in service consumption, which is crucial for advanced usability. Service instantiation, which is between in selection and execution, aims at preparing and presenting preselected services ready to be executed while limiting useless interactions with users. To this end, execution-time constraints attached to services descriptions are required to be satisfy before their execution. Most of existing approaches [10] undervalue this issue by rarely considering suitable and efficient methods for such a personalization level. Contrary to the latter that consider several levels of interaction to manually collect information (from the users) required by input parameters of services to be executed, we consider automation of this process through semantic instantiation of services.
By addressing the latter, we thus aim at i) improving and easing the user interaction with services, beneficial for both parties and ii) better supporting the user by anticipating her needs. To reach the goal of automated and personalized consumption of services and more specially to suggest accurate and personalized presentations of services to users, we benefit from the semantic augmentation of service and user profile descriptions. In this direction, we define a framework, where potential matching between both descriptions is defined as a reasoning task to be solved for service instantiation (in the rest of the paper we refer to it as service personalization and adaptation). The semantic matching, core of our approach, exploits standard DL reasoning [15, 16] and abduction [17] to i) adapt services to the user by identifying useful parts of its profile that satisfy the service requirements (i.e., input parameters) and ii) compute the descriptions required by a service to be executed but not provided by the user profile.
The remainder of this paper is organised as follows. Section 2 briefly reviews i) DL reasoning and abduction, both required for automated personalization, ii) semantic web services and iii) semantic user profiles. Section 3 presents our approach to personalize semantic web services. Section 4 presents details about the prototype implementation and reports some experiment results. Section 5 briefly comments on related work. Finally Section 6 draws some conclusions and talks about possible future directions.
In this section we review i) DL as a semantic language and abduction reasoning to perform personalization. Then we remain the definitions of ii) semantic web service and iii) semantic user profile, as core elements in our approach. 2.1
Description Logic and Abduction Reasoning The model we considered to represent semantics of services and user profiles is provided by an ontology. In more detail, we focused on DL as a formal knowledge representation language to define ontologies since the latter offers good reasoning support for most of its expressive families and compatibility to current W3C standards e.g., OWL. Terminological Box T (or TBox i.e., intentional knowledge) and Assertional Box A (or ABox i.e., extensional knowledge) are core elements to represent knowledge in DL systems. In the following, we will focus on the TBox T (see Fig. 1 as an example) that supports different level of inference by means of DL reasoning. As a trade-off between expressivity and complexity, we use the expressiveness of the DL ALC [14] to perform service personalization, which is the standard DL AL (Attributive Language) extended with full existential qualification E and concept union U .
Besides standard DL reasoning approaches such as satisfiability or subsumption to guarantee consistency of DL knowledge bases or build concepts hierarchy, authors of [17] suggest computing the Abduction (Definition 1) between concepts C and D, representing what is underspecified in D in order to completely satisfy C taking into account the information modelled in a ALN (so compliant with ALC) TBox T .
Let L be a DL, C, D be two concepts in L, T be a set of axioms in L and A be a set of assertions. A Concept Abduction Problem (CAP), denoted as hL; D; C; T i (or shortly CnD) consists in finding a concept B 2 L such that T j= D u B v C.
Let C and D be two ALC descriptions respectively defined by BusinessAccount u 9hasSkype:SkypeAccount and P ersonalAccount (Fig.1). According to Definition 1, the description B required by D to satisfy (or more precisely to be subsumed by) C is denoted by CnD i.e.,
CnD =: 9hasID:OpenID u 9hasSkype:SkypeAccount
(
CS11 =: 9hasF N:F irstN ame u 9hasLN:LastN ame CS1 =: BusinessAccount u 9hasSkype:SkypeAccount
2 CS1 =: M aillingAddress u 9hasM ail:GM ail
3
CS41 =: Location u 9hasLat:Latitude u 9hasLong:Longitude 2.2
Semantic Web Services
Semantics of web services can be expressed by means of different descriptions, from
their process levels [18] (i.e., internal and complex behaviours) and causal levels [19]
(i.e., preconditions and effects on the world) to their functional levels (i.e., simple
interface). In this work we will focus on the latter and more generally their functional input
and output parameters, which are prominent to personalize and execute any service. In
the semantic web, these functional parameters are enhanced with DL concepts that
determine the semantics of the operations they achieve. Therefore, semantic web services
can be expressed as DL concepts in (
Service =: 9requires:Input u 9returns:Output
This definition confines a semantic web service to being anything that requires input parameters Input to be processed and returns some output parameters Output. Both latter parameters are defined in T such that T j= Input v > and T j= Output v >. According to this model, the OWL-S profile [20], WSMO capability [21] or SAWSDL [22] can be used to describe the functional level of semantic web services.
Suppose a semantic web service S1 locating friends and professional colleagues of a
specific person, as its main functionality. This service, starting from a FirstName,
LastName, BusinessAccount, SkypeAccount and a GMail address of this
person, returns the list of her nearby ContactPersons according to her Location.
According to (
:
S1 = 9requires:CS11 u 9requires:CS21 u 9requires:CS31 u 9requires:CS41 u
9returns:ContactP erson
where conjuncts CSi;11 i 4, described by means of TBox T in Fig.1, are defined by:
(
Semantic User Profile
Semantics of user profile can be expressed on different dimensions, mainly on
information related to her i) identity and possessions, but could be also extended with her
ii) long-term interests in topics [23] and preferences, iii) skills [24], iv) behaviour [25]
and v) knowledge or beliefs in certain domains. Since the description of semantic user
profiles are tailored, in this work, to access services in a personalized way trough their
instantiation, we will focus solely on the former descriptions. Indeed, descriptions from
(ii) to (v) such as the user’s interests and preferences are mainly relevant information for
prioritizing services in a discovery process rather than making services and their (input)
parameters adapted to the user. Therefore, a DL expression of semantic user profiles is
defined by the conjunction of different parts as expressed in (
P rof ile =: l 9hasInf o:Inf oi l 8hasInf o:(:N onInf oj)
i j
(
This definition is based on a single role or property called hasInf o, which describes
the user. While descriptions Inf oi cover a collection of data identifying users,
descriptions expressed by N onInf oj refer to information users are not inclined to provide.
The latter could be sensitive data such as medical record data. All initial information in
(
Suppose a British lady identified by her Name, ElectronicID and connected to
the LinkedIn social network (http://www.linkedin.com/), but without any authorized
access to her Bank Account from third parties. Its semantic profile P1 is defined by:
P1 =: F emale u 9hasInf o:CP11 u 9hasInf o:CP21 u 8hasInf o:CP31
(
The formalization of services (
Personalized Adaptation of Services with Semantic User Profiles
Our approach, based on semantic matching, aims at discovering relevant information in
the semantic user profile that could fit (i.e., be used by) the service (i.e., its functional
input parameters) to be executed. By considering such a personalization, we suggest an
approach which adapts any service to the user. To support this adaptation, the matching
is performed over a service S and a user profile P with respect to a TBox T . Therefore, a
role hierarchy (
T j= hasInf o v requires
(
According to (
In detail, our approach achieves such an personalized adaptation by following steps in Algorithm 1. From a logical point of view, this algorithm evaluates potential matching between conjuncts CS and CP of respectively S and P . By emphasizing only robust matching [28] i.e., Exact in line 7 and PlugIn in line 10, this algorithm focuses on the description required by S and provided by P . Therefore, other matching such as T j= CP w CS (Subsume) or T 6j= CP u CS v ? (Intersection) are not valued since they do not fit our service adaptation purpose.
Algorithm 1: Matching-based Personalized Adaptation: adapt(S; P; T ). 1 Input: A user Service S, a Profile P , a Terminological Box T . 2 Result: match: a set of matching triples (CS; CP ; type) if CS could be adapted by CP with a matching type, Incompatibility otherwise. 3 begin 4 match ;; 5 foreach 9requires:CS 2 Input(S) do 6 // Exact: The descriptions of user profile and service match perfectly. 7 if there exists 9hasInf o:CP 2 Inf o(P ) such that T j= CP CS then 8 match match [set (CS; CP ;00 0000);
In Algorithm 1 Exact matching are tested before the PlugIn matching, mainly because of the logical implication relation between these matching. Indeed, if T j= CP CS (Exact), then T j= CP v CS (PlugIn). The algorithm is then based on structural algorithms for satisfiability and subsumption [29]. Since it is reasonable to assume that users and service providers do not enter contradicting information, we assume that the services S and users profiles P descriptions are consistent in T .
The result of this personalized adaptation step is a set of matching triples (CS ; CP ; type) (referring to match) wherein the description CP in P could be used to adapt the service parameter CS in S. The latter descriptions are completed with their matching type, emphasizing the accuracy of the personalized adaptation step (from CP to CS ). In case S violates some descriptions in P , S and P are returned as incompatible.
Suppose the service S1 and user profile P1 in Examples 2 and 3. According to Algorithm 1, the triple (CS11 ; CP11 ; P lugIn) is returned. Indeed, according to the TBox T in Fig. 1, it seems possible to personalized S1 by adapting the FirstName and LastName input parameters of S1 with information of CP11 in the user profile, and more specially with its Name.
Even if our approach is able to personalize services with user profile descriptions using subsumption-based DL reasoning, the latter profile maybe not always as accurate as it should be hence limiting its benefits. Indeed, in some cases parts of services could only partially match or even mismatch the profile.
Even if CS21 and CP21 in Examples 2 and 3 are both related to Account description in
T , CP21 cannot be used to personalize S1 and more specially CS21 because of missing
description (
Towards Incomplete Semantic User Profile Since the personalization process may fail because of under specification or missing description in the user profile (e.g., Example 5), we suggest to extend Algorithm 1 with Algorithm 2 by exploiting results from non robust matching cases between conjuncts CS and CP i.e., Intersection and Subsume. Therefore, we aim at i) inferring further matching triples from the latter matching cases, and ii) discovering descriptions which are required by services but not provided by the user profile by applying abduction. Further Matching Triples for Personalized Adaptation of Services: In both Intersection and Subsume matching cases, simple matching triple (CS ; CP ; type) cannot be returned as in Algorithm 1 mainly because only a part of CP is required to adapt (again) a part of CS . Towards this issue, we identify descriptions B and A that need to be removed respectively from CS and CP to obtain a PlugIn matching between CP and CS i.e., CP CS (Definition 2, adapted from [30]).
Let L be a DL, C, D be two concepts in L, T be a set of axioms in L and A be a set of assertions. The symmetric difference between C, D, denoted as C D consists in finding two concepts A; B 2 L such that
T j= CnA v DnB
(
Let CP21 and CS21 be two ALC descriptions respectively defined in Examples 2, 3 with
respect to T in Figure 1. According to Definition 2, C D is defined by A and B i.e.,
A =: 9hasID:ElectronicID
B =: 9hasID:OpenID u 9hasSkype:SkypeAccount
(
According to Definitions 1 and 2, it is straightforward to identify the parts X 2 P(CS ) and Y 2 P(CP ) respectively from CS and CP (where P(S) refers to power set of S) which are required to ensure that X can be adapted by Y (in the sense of Algorithm 1 i.e., T j= CP v CS ). Both descriptions X and Y , parts of the new matching triples, are defined as CS n(CP CS ) and CP n(CP CS ). Algorithm 2 elaborates these further matching triples (line 12) from respectively lines 9, 10 and lines 8, 11. The first element of the matching triples represents the description in CS which subsumes CP , whereas the second element represents the part in CP which is subsumed by CS . In other words, these triples present the descriptions in CP which will be used to further adapt the descriptions in CS . Finally, as Algorithm 1, results are aggregated in match.
According to Example 2, 3 and T in Figure 1, T 6j= CP21 u CS21 v ?. Therefore, only some parts Y of CP21 can be used to adapt some parts X of CS21 . Applying Algorithm 2 (lines from 8 to 12) and using results from Example 6, Y and X are defined as: Y X
(17) This simply means that the SocialNetworkAccount of CP21 (i.e., LinkedIn) can be used to instantiate the SocialNetworkAccount requirement (i.e., input parameter) of CS21 .
Computing Missing Description in a User Profile: In addition, Algorithm 2 (lines 13 and 15) computes descriptions which are required by CS and not (or partially) provided by CP . To this end, abduction is applied between the latter conjuncts (Definition 1) and then the result is aggregated in a set of missing description miss (Definition 3).
The set of missing description miss of a service personalization problem (S; P; T ) is defined by: miss(S; P; T ) =: inffCSnCP jT 6j= CP u CS v ?gnset f>g v (19) where CS and CP are respectively conjuncts of 9hasInf o:CP and 9requires:CS .
According to Definition 3, miss gathers the most specific descriptions of the set fCSi nCPj g. Therefore a same description (i.e., the most specific in the service requirements) can be used to satisfy different abduction problems CSi nCPj and then could be exposed as a description not provided by P but required by some conjuncts (related by subsumption) of S. miss does not only explain why services have not been adapted and personalized (regarding a user profile) but also suggest a solution to extend the personalization of semantic web services.
The set of missing description miss of a web service personalization problem (S; P; T ) with either i) T j= CP CS ; or ii) T j= CP v CS is the empty set.
Proof. By Definition 1, CS nCP is defined by T j= CP u (CS nCP ) v CS . Therefore, we obtain in both cases that CS nCP > is a solution i.e., miss is defined by the empty set according to Definition 3.
The property 1 justifies our choice of not computing missing descriptions in Algorithm 1. Indeed, such a computation would reach to the empty set.
Since the description in P1 is not enough to totally adapt and personalize S1 (Example 5), Algorithm 2 and Definition 3 are required to discover the missing description miss in P1. According to the latter definition, miss is constituted by the union of results of the abduction problems CS21 nCP21 (Example 1), CS41 n> and CS31 nCP21 : 2 2 CS1 nCP1
4 CS1 n> 3 2 CS1 nCP1
(20) (21) Since GM ail v OpenID, hasM ail v hasID and miss only considers most specific description (whether subsumption-based comparable), miss is fA; CS41 g where: A
(23) According to Property 1, conjuncts CSi;11 i 4 and CPj;11 j 2 such that T j= CP1 v CSi1 j are not considered by Algorithm 2.
Extending Semantic User Profile with Further (Missing) Descriptions Once the set of missing descriptions is retrieved through miss, two approaches are considered. First of all, an intuitive method consists in discovering [31] which new and appropriate services Si;1 i n would be able to return the missing description. Following this approach, this description miss could be identified and satisfied by the conjunction of some output parameters Out Si;1 i n of these services Si;1 i n . Therefore, depending on the available description in the user profile P and the description of output parameters of Si;1 i n , we could proceed to the service S personalization. Indeed, the conjunction C of the output parameters Out Si;1 i n and the user profile P i.e., dn
i=1 Out Si u P can be used to adapt and personalize the service S by applying Algorithm 1 as following: adapt(S; C; T ). However, each input parameter of these discovered services Si has to be known at run time. To this end, we can imagine use the description available in P to adapt this new discovered services. In this direction, Algorithm 1 is applied on the n relevant services as following: adapti;1 i n (Si; P; T ). One constraint of this method is related to the number of services (and their input parameters to be satisfied by P ) which are required to satisfy miss.
In the second approach, the set of missing description is simply suggested to the user. The user is then responsible of providing the description that the system needed to adapt and personalize the service. The requested information is also used to populate the semantic user profile, hence available for further personalization purposes. 4
In this section, we discuss the prototype tool that we developed to provide personalized adaptation of semantic web services. Moreover we give a preliminary evaluation of the suggested approach by analyzing some results obtained with the prototype. 4.1
Architecture and Implementation 1 http://www.soa4all.eu/ description. The MAMAS-tng2 reasoner has been used to compute standard reasoning and evaluate abduction. This reasoner has been extended to compute symmetric difference (Definition 2). The SPARQL3 Query Engine module RDF2GO4, is required to manipulate matching triples i.e., RDF-based CP , CS and miss data returned by Algorithms 1 and 2. For instance, this module transforms RDF-based CP in CS using a CONSTRUCT query form of SPARQL in order to adapt CS with CP .
are described by Users Profiles
Upgraded Profiles
User Profile Upgrade with Semantic Annotations of Services (LinkedData Principle)
Services
Annotations y b d e b i r c s e d e r a
Update of
Profiles
OpenID Access Ontologies
SPARQL Query
Engine (RDF2GO)
Service to
Personalize
Personalization Approach reason on
Description Logic
Reasoning (Mamas−tng)
Semantic−based Services Description
(RDF Repository [32]) ___| ___| ___| ___| ___| Personalized
Service Service Discovery ([31])
Caption (Impl) := Implementation used
State−Of−The−Art Tools Starting Point End Point Processing
In addition, a pool of (SA-WSDL) semantic-based services (formalized in (
Therefore we analyze the performances of our approach by i) comparing abductionwith difference-based [36, 37] personalization using different expressivities of DL, and ii) studying the impact of the User Profile Upgrade component (Fig.2) on the personalization process. The experiments have been conducted on Intel(R) Core(TM)2 CPU, 2.4GHz and 2GB RAM.
Comparing Abduction- and Difference-based Service Personalization: Since other approaches based on DL difference operator such as [36] or [37] can be used to compute from a given description all the information different in another description, we suggest to compare them with abduction to achieve service personalization (Fig.3). The comparison is driven on three set of ontologies with different DLs used to annotate services and profiles i.e., ALC, ALN and ALE , from the most to the least expressive. In particular, the two former ontologies are based on the ALE TBox (formally defined by 1100 concepts and 390 properties) wherein only DL operators changed in descriptions. Personalization of up to 100 services have been considered in this experiment, especially for obtaining convincing results towards their applicability in real (industrial) scenarios.
The [37]’s difference consider a semantic maximum (ordering according to the subsumption operator) between (only) subsumption-based comparable descriptions. Even if they provides sufficient condition (i.e., structural subsumption relation) to characterize the uniqueness of difference, some TBoxes cannot be considered such as ALN . In addition, this is the most time consuming approach regarding the three set of experimentation, mainly because they perform an equivalence between two concept descriptions (T j= D u B C ) whereas abduction computes (only) a subsumption of concept descriptions (T j= D u B v C). The difference operator of [36] is a refinement of [37]’s difference that considers the syntactic minimum ( d) between incomparable descriptions. Such a consideration, limiting the relevance of its results using expressive DLs, explains its very good performance.
Even if deciding subsumption, computing abduction and difference in ALE is NPcomplete, Figure 3 reports the feasibility and the scalability of the personalization process. However these results depend on size and structure of the used ontologies, size and complexity of user profile and service descriptions. The choice of abduction to personalize services is justified by its performance in the three different DLs studied. Indeed, our process of personalizing services with an expressive DL ALC over performs the time consuming [37]’s difference-based personalization using ALN or ALE DLs. Impact of the User Profile Upgrade Component: Since our personalization approach aims at matching services to user profiles, it is required that their descriptions can be semantically compared, either using a same ontology, or by establishing subsumptionbased relationships between some of them. This experiment studies the qualitative impact of the latter (i.e., User Profile Upgrade component in Fig.2) on personalization.
To this end, 55 different initial ALN TBoxes (i.e., average of 103 concepts and 61 properties) have been used to annotate 100 services Si;1 i 100 and one profile P . Then, progressively, some inter-connections have been established between them, favouring the matching hence the personalization. Roughly speaking, the progression of connections grows exponentially along 10 rounds Ri;1 i 10 i.e., from adding 21 to 210 connections. After each round, we run our personalization approach on these 100 services and evaluate the rate of i) input parameters qS that can be adapted given P and ii) missing description qmiss, both regarding the number of description in Si.
60 )% 50 ( te 40 aR 30 s ino 20 t ircp 10 eD 0 qSqmiss qSqmiss qSqmiss qSqmiss qSqmiss qSqmiss qSqmiss qSqmiss qSqmiss qSqmiss s
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
Fig. 4. Impact of the User Profile Upgrade Component on Service Personalization.
As shown in Fig.4, the more interconnections between ontologies, the better personalization (i.e., qS ). In the same way, the rate of description to be updated in the user profile (i.e., qmiss) is also improving. Both improvements follow a linear evolution even if an exponential generation of interconnections is applied. In more detail, 37% and 98% of service descriptions can be semantically compared (not matched) to profile description respectively in the first and last round of interconnections generation. The transition between round 5 and 6 (with 64 new connections) is the most significant with respectively 53% and 72% of comparable descriptions in services and profile. While our approach is able to adapt and personalize 44% of services in the last two rounds, the personalization only reaches 15% of services in the first three rounds. This confirms the high impact of the user profile upgrade component in our personalization framework.
qmiss is in general higher than qr since some input parameters of services cannot be captured by personalization e.g., variables such as flight destination, or sensitive data. 5
Based on [38], [10] present Personal Reader Framework, an approach for RDF-based data extraction, combination, visualization and personalization. In particular, they generate personalized view of data by applying standard subsumption-based matching between data description, user profile and contextual information. To this end they adopt TRIPLE [39], a rule language which is designed for querying and transforming RDF models. Even if their approach is augmented with contextual information, their matchingbased personalization approach do not consider automated update of user profile.
Contrary to our approach, [8, 23, 27] perform personalization to obtain more relevant services during the discovery process. Therefore they do not address services instantiation (through their personalized adaptation) but rather service selection. The selection process is based on the compatibility of users’ interests, disinterests and service descriptions. Services that do not match a certain profile are discarded on the fly. Since the matching process between the latter descriptions is handled on mobile device, both approaches [23] sacrifice expressivity of DL and use standard DL inferences [15, 16]. Even more (semantically) limited, [27] consider only one-to-one syntactic matching of service and profile descriptions for personalization. [8] consider non-functional parameters, preferences and knowledge that is implicitly given by previous service to personalize the selection of services.
[24] present an approach for matching user profiles for applications such as job recruitment or dating system. The matching, which is performed on a demand profile Pd and a supply profile PS , aims at evaluating their semantic similarity. In the same way as our work, abduction is used, but only for weighting, ranking purposes and not for extracting and reusing relevant parts of Pd and PS . In addition they apply the nonstandard inference contraction [40] to evaluate the effort (i.e., description) required to make Pd u Ps satisfiable in H. On the contrary, we assume the latter conjunct to be satisfiable in H since one goal of our personalization approach is to suggest more specific descriptions (and so satisfiable) to the user profile regarding the service descriptions. 6
In this work we studied service personalization or the way to tailor services to a particular user. In particular, we addressed automated instantiation of services (through personalized adaptation) which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? Towards this issue, we considered a semantic augmentation of services and extensible user profiles to infer potential matching between both descriptions. The semantic matching, core of our approach, exploits standard DL reasoning and abduction to i) identify useful parts of a user profile that satisfy the service requirements (i.e., input parameters) and ii) compute the descriptions required by a service to be consumed but not provided by the user profile. Our approach, integrated in the service consumption of the EC-funded project SOA4All, has been augmented with a process of user profile upgrade in case heterogeneous ontologies are used to describe services. Such an augmentation, aiming at linking data description of services and user profile, has been validated by experimental results. In the same way, the latter results confirm our choice of preferring abduction rather than other difference operators for scalability and expressivity reasons.
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