An Improved Approach for Semantics Based Filtering and Selection of Services Using Bipartite Matching

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An Improved Approach for Semantics Based Filtering and Selection of Services Using Bipartite Matching

P. Mahesh

Computer Science and Engineering, PSN College of Engineering and Technology, maheshmario@gmail.com


In todays world, a vast number of web services

create WSDL [9] descriptions and publish them to UDDI [8] registries. Clients search the registry to locate providers of the desired service. Today, in most cases,

are available, providing a variety of services. As a result service discovery becomes an important task. Large number of services made discovery of web services is a critical one and also discovery process has the scalability issues when the number of services increases. In this work, we address the issue of scalability in web service discovery process by adding a preprocessing stage. Our approach to preprocessing stage involves filtering the services by checking the details of descriptions; semantic- based web service discovery involves Bipartite Matching and semantic enhancement of the service request. Our approach proposes a preprocessing stage based on the details of descriptions that filter service repositories with enhanced service request. We propose a solution for achieving functional level Bipartite Matching based on an Ontology framework. The semantic enhancement of the service request achieves a better matching with relevant services. The service request enhancement involves expansion of additional terms (retrieved from WordNet) that are deemed relevant for the requested functionality. An efficient matching of the enhanced service request with the filtered services is achieved utilizing Bipartite Matching. Our Experimental results validate the effectiveness and feasibility of the proposed work.

Keywords: Automated Web Service Discovery, Bipartite Matching, Semantics-based Filtering


    A large number of Web Services structure a Service Oriented Architecture (SOA) and facilitate the creation of distributed applications over web. Loose coupling is an important principle underlying Service Oriented Architecture. One aspect of loose coupling is the ability to invoke a service with little (or no) knowledge about it. The publish-find-bind architecture is intended to facilitate this process. Service providers

    the WSDL is compiled into client-stubs and the service is invoked. This approach, however, has several limitations.

    The WSDL is a specification of the messaging syntax between the client and the provider. It is necessary for a human to interpret the WSDL and then invoke the client-stub with the correct parameters. The search capabilities of UDDI are limited to a syntax- based search. A client can search the registry for a string in the service description or it can search the service classification hierarchy (like NAICS [3]) in the TModel. Neither of these techniques is sufficient, for a client, to be able to autonomously choose a service provider and invoke it without human intervention.

    In order to overcome these limitations, techniques for semantic description and matchmaking of services have been proposed in recent literature. These techniques use semantic concepts from Ontologies to describe the Inputs, Outputs, Pre- conditions and Effects (IOPE) of a service. Current Web service matchmaking approaches check the capabilities of the requested Web service against the capabilities of all advertised Web service advertisements in the registry. As the number of advertised Web services in the registry can be huge, the process of checking all advertised Web services against a single client query can be time consuming.

    This work proposes a semantic-based Web service registry filtering mechanism that takes the responsibility of narrowing down the number of Web service descriptions to be checked in detail to the number of only the relevant advertisements to the client request. The proposed filtering mechanism picks the advertisements that are relevant to the client request and ignore, from an early stage and before checking the details of the descriptions, the advertisements that are

    not able to satisfy the client request. This work makes use of current web technologies like OWL and OWL-S to describe services and define ontologies.

    Initially the Semantic Matchmaking Algorithm is proposed by M. Paolucci et al [21]. This work has considerable interest in this algorithm because it has been cited extensively in recent literatures and several subsequent proposals ([13], [22], [16], [14]) are based on it. M. Paoluccis algorithm [21] adopts a greedy approach towards matching the concept-lists. For example, in the case of output matching, for each concept in the Queryout,, It determines a corresponding concept in Advtout to which it has a maximum degree of match. Once all such max-matchings are computed, the minimum match amongst them is the overall degree of match between the advertisement and the client request.

    M. Paoluccis algorithm has been tested that the Greedy algorithm indeed generates false positive and false negative outcomes. On the other hand, the outcomes of the Bipartite matching are identical to that of the Brute Force reference model. So, the proposed work utilizes an efficient matchmaking algorithm based on bipartite graphs.

    The rest of the paper organized as follows: Section 2 provides background material and Section 3 presents an overview of the proposed approach. Section 4 provides a detailed discussion on semantic enhancement of service request. The detailed description for parameters based service repository filtering is presented in Section 5. Section 6 includes a discussion on Bipartite Matching. The implementation details of the proposed approach and our evaluations presented in Section 7. We present the related work in Section 8. Finally, conclusion and future work are presented in Section 9.


    This section provides a brief background of the methodologies utilized for parameters based service repository filtering and selection of services using Bipartite Matching. We briefly discuss the parameters for ranking the services using Jaccard Similarity in the context of semantic based service repository filtering. We also briefly discuss the Bipartite Matching algorithm used for service selection.

      1. Jaccard Similarity

        Jaccard Similarity is a simple and very intuitive measure of document to document similarity. It is defined as follows:

        similarity(A,B) = n(A B) / n(A B) (1)

        Using the Inclusion-Exclusion Principle, this reduces to:

        similarity(A,B) = n(A B) / n(A) + n(B) – n(A B) (2)

        Where: n(A B) = min(Ai, Bi) n(A) = Ai

        n(B) = Bi

        for i = [0..n-1], where n = number of terms in our term-document matrix.

      2. Bipartite Graphs and Matching

        Bipartite Graph:

        A Bipartite Graph is a graph G = (V, E) in which the vertex set can be partitioned into two disjoint sets, V=V0 V1, such that every edge e E has one vertex in V0 and another in V1.


        A matching of a bipartite graph G = (V, E) is subgraph G = (V, E), E E, such that no two edges e1, e2 E share the same vertex. We say that a vertex v is matched if it is incident to an edge in the matching. Given a bipartite graph G = (V0 + V1, E) and its matching G, the matching is complete if and only if, all vertices in V0 are matched


Fig. 1 Automated Service Discovery

Fig. 1 illustrates the key steps of the proposed approach for automated service discovery using bipartte matching. The first step of proposed approach involves enhancement of the service request. This step involves two tasks: 1) enhancement of the web service request with WordNet terms, 2) filtering of the set of web services based on the input, output and description

parameters of the service. The purpose of this filtering is to select a set of services from the available set of advertisements. The next step deals with the selection of web services for the enhanced service request with the filtered set of advertisements.

Fig. 2. Automated Service Discovery Components.

Fig. 2 illustrates the main components of the overall system that performs two key steps related to automated service discovery

The WordNet guided enhancement of Service Request, is illustrated in steps 1 to 4, a key part of this process involves enhancing the service request. Step 1 collects the input file from the client. Step 2 demonstrates the initial processing of the service request and its transformation to service request vector. Our approach for enhancement of service request utilizes WordNet to enhance the service request. In step

  1. we expand the request vector with relevant terms from WordNet dictionary. Step 4 indicates the formation of enhanced service request vector.

The enhancement of service request is followed by service filtering from the relevant group of services. This is achieved by parameters based service filtering as illustrated in steps 5 and 6 of Fig. 2. Parameters based service filtering includes narrowing the set of appropriate services matching the service request based on parameters, i.e., input, output and description. The filtered set of services is then matched against an enhanced service request as part of Selection of Web Services, as illustrated in steps 7 and 8 of Fig.

2. Parameters based filtering of web services begins with the representation of the web service parameters as a vector in which each entry records the terms of the operations input and output. The set of related web services is represented by a collection of such vectors. Next we rank this web service collection by applying Jaccard Similarity, as illustrated in step 6.

The filtered set of web services is then matched against an enhanced service request as part of Selection of Web Services, as illustrated in steps 7 and

  1. Our approach for bipartite matching utilizes ontology linking to the filtered services. We provide ranking of services according to the terms of input and

    output parameters. However, consi input and output parameters we matching as the outputs of a service ar for client of a matchmaker. The follo used to for this weight calculation:

    S final = W output * S output + W input * S in

    In the above equation, S represent the similarity score consideri parameters and output parameters res and W output represents the weights for

    dering both the prioritize output e more important wing equation is

    put (3)

    input and S output

    ng only the input pectively. W input the input and

    output similarity score, where they are fixed to 0.4 and

    0.6 after several runs of matchmaker considering the expected outcome. Finally, S final represents the final score of similarity considering both the input and output parameters.

    1. ENHANCEMENT OF SERVICE REQUEST In our approach we begin with the

      enhancement of web service request wherein we combine the WordNet terms, following the service request vector building process. For each term in the service request vector, a collection of similar words added from the WordNet dictionary. Additional terms are added based on semantic relationship between the terms in the request vector.

      1. Web Service Request Preprocessing

        The service request OWL-S file is parsed and preprocessed. Preprocessing includes: the removal of markups, translation of upper case characters in to lower case, punctuation removal and white space used as a term limiters and stopword removals. The outcome of this preprocessing is in a term vector.

      2. Service Request Expansion

        To achieve semantic enhancement we utilize an approach that augments the WordNet noun database [24],[15]. The add step extends each service vector by additional WordNet elements. The generateEnhancedRequest Algorith (Fig. 3) gives the details. Step 2 of Fig. 2 executes the modification of the web service request vector.

        Algorithm: generateEnhancedRequest Input: Web Service Request (OWL-S) Output: Enhanced Service Request SRe 1: begin

        2: for each Web Service Request SR do 3: /* Preprocess Service Request SR */ 4: // Removal of Stopwords

        5: end for

        6: for each Web Service Vector wsi WS do 7: for each term tj ti && tj T do

        8: Extract WordNet Element ej 9: append terms in ej to SRe 10: end for

        11: end for

        12: return SRe

        13: end



      The next step is service filtering from the relevant category of services using Parameters Based Service Filtering. Web service parameters, i.e., input, output and descriptions, aid service filtering through narrowing the set of appropriate services matching the enhanced service request. Sections 5.1 and 5.2 provide a detailed discussion of each of the steps for parameters based service filtering.

      1. Service Parameters Retrieval

        As discussed earlier, the web service description is provided in the OWL-S document. For retrieving the relevant service parameters, the corresponding OWL-S document is processed to extract the associated operation parameters by retrieving all the terms under <profile:hasInput> and

        <profile:hasOutput> tags. The OWL-S preprocessing also includes stoplist removal. Final outcome of this step is Service Description Vector for the published services using OWL-S.

      2. Service Collection Pruning

        A large number of services available in the Registry which are relevant and irrelevant to the Service request. So irrelevant

        services need to be discarded from the search registry. The pruning of the Service Collection is based on eliminate the services that have zero ranking between the enhanced request. This is illustrated in the PruneAdvertisements algorithm.

        Algorithm: PruneAdvertisements

        Input: Advertisement Collection present in the Domain registry

        Output: Relevant Service Description Set 1: begin

        2: for Advertisement in Domain registry do

        3: /* form service description vector after stoplist removal*/

        4: Rank advt= SIM(Advt, SRe) 5: if Rank advt >0

    3. append Advt to pruneSet 7: end for

8: end


    The parameters-based filtered set of web services is then matched against an enhanced service request as part of Semantic Bipartite Matching. Search() procedure accepts a Query as input and tries to match it with each advertisement in the repository. A match is computed for both, output and input concepts. If the match is not a Fail, it appends the advertisement to the result set. Finally the sorted result set is returned to the client.

    The match() procedure accepts two concept- lists as inputs and constructs a bipartite graph using them. It then invokes a hungarian algorithm [17], [20] to compute a complete matching on the graph. The match() procedure is invoked twice in search(). The order of Query and Advertisement in each call is however swapped.

    The computeWeights() function computes the values of w1,w2,w3, depending on the number of concepts in V0. It uses the formulae presented in the section Computation of Edge Weights to compute the values. The degreeOfMatch() function is a call to the reasoned in order to determine the relationship between the two concepts a and b.

    Algorithm: Search(Query) 1: Result = Empty List

    2: for each Advt in Repository do

    3: outMatch = match(Queryout, Advtout) 4: if (outMatc = Fail) then

    5: Skip Advt. Take next Advt. 6: end if

    7: inMatch = match(Advtin, Queryin) 8: if (inMatch = Fail) then

    9: Skip Advt. Take next Advt. 10: end if

    11: Result.append(Advt, outMatch, inMatch) 12: end for

    13: return sort(Result)

    Algorithm: Match(List1, List2)

    1: Graph G = Empty Graph (V0 + V1,E) 2: V0 List1

    3: V1 List2

    4: (w1, w2, w3) computeWeights(|V0|) 5: for each concept a in V0 do

    6: for each concept b in V1 do 7: degree = degreeOfMatch(a, b) 8: if degree 6= Fail then

    9: Add edge (a, b) to G

    10: if (degree = Exact) then w(a, b) = w1 11: if (degree = Plugin) then w(a, b) = w2 12: if (degree = Subsume) then w(a, b) = w3 13: end if

    14: end for

    15: end for

    16: GraphM = hungarianMatch(G) 17: if (M = null) then

    18: No complete matching exists 19: return Fail

    20: end if

    21: Let (a, b) denote Max-Weight Edge in G 22: degree degreeOfMatch(a, b)

    23: return degree


    For evaluating the performance of the algorithms which are proposed in previous sections, we semi-automatically built a test collection of 1007 OWL- S service descriptions which use a reference ontology with 4694 concepts. Most of the structure of the taxonomy of the referenced ontology is also based on the hierarchy of the words in WordNet. We then used Recall and Precision metrics for evaluating the results. Recall is the extent to which the method retrieves all of the similar services while Precision is the extent to which the method retrieves only the real similarities.

    The effectiveness of the Compound Similarity is shown by conducting three set of experiments: (1) Greedy Match Making algorithm by Paolucci; (2) The search using the Text Based Matching algorithm with the available service descriptions only; (3) The search using Bipartite matching. The results are shown in the below table.




    Paolucci Matching



    Text Based Matching



    Bipartite Matching




    Our approach has similarities to existing approaches [4], [1], [2], [19] of natural language processing techniques that address the text part of the challenge in content-based image retrieval (CBIR). These approaches, however, were used in isolation to one another.

    In [12], Sassen et al. describe the SeCSE approach for architecture time service discovery that is based on ontologies that are validated, easy to use, complete, and widely accepted in domains. In contrast to this, our approach begins with the description of an ontology framework that includes upper ontologies, e.g., SUMO and more descriptive domain and application related ontologies. We propose a linked

    ontology structure for a wide-ranging description of domain semantics.

    Phatak [22] adds ontologymappings and QoS constraints to the algorithm from [21]. Choi [13] expands the search scope of [21] by the use of analogous terms from an ontology server. It also makes use of a rule-based search in order to apply user restrictions and to rank search results. It computes fine- grained rankings by the use of concept similarity (horizontal and vertical closeness between concepts). Jaeger [16] extends the work from [21] by using matching over the properties and over the Service Profile hierarchy. It offers a better (fine-grained) ranking scheme as compared to [21].

    The usage of synonyms does not capture the overall semantics of the domain and application functionality. However, our approach utilizes concepts extracted from domain ontology. These extracted concepts account for relationships between the domain objects and provide a comprehensive coverage for the underlying semantics for both the domain and the application functionality. Our approach appends the syntactic service description with relevant semantic terms. This enables uniform combination of syntactic and semantic matching rendering our approach more generalizable for overall service matching and requiring minimal human interaction.


The proposed approach presents an integrated approach for automated service discovery. Specifically, the approach addresses two major aspects related to semantics based discovery: Semantics based filtering and selection based on bipartite matching. For semantics based filtering, this work proposes a WordNet guided filtering of advertisements leads to reduce the scalability issues in MatchMaking process. For Selection based on Bipartite matching, we employ ontology linking to discover services in functional level. Our experiments show that this lead increased precision levels, recall levels and the relevance scores of the retrieved services.

In future, proposed work can be extended to web service composition. Typically, multiple services have to be discovered so that they together match a service request. It should be possible to utilize ontologies, and explicitly return the sequence of individual service invocations to be performed in order to achieve the desired composite service. When no full match is possible, a flexible matching approach could be created to return partial matches and/or suggest additional inputs that would produce a full match by capturing the dependencies among the matched

services. This has several interesting research issues. Another avenue for future work is to create an interactive, intelligent service composer that is semantically guided to locate the target service components step by step. We also intend to extend our ontology framework and investigate additional mapping tools to better express a service request to search for relevant concepts.


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