Semantic Matchmaking Research Articles

Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

Web service composition composes existing web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or non-functional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this paper, we model the problem of collectively handling multiple service composition requests as a new multi-tasking service composition problem and propose a new Permutation-based Multi-factorial Evolutionary Algorithm based on an Estimation of Distribution Algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.

A core IoT ontology for automation support in edge computing

Service providers provision more and more Internet-of-Things (IoT) services in the cloud for dynamicity and cost-effectiveness purposes. This is made possible thanks to the introduction of edge computing that brings additional computing and resources for analytics close to the data sources and thus enables meeting the low latency requirement. Edge nodes should support (i) the heterogeneity of IoT devices (e.g., sensor, actuator) and (ii) characteristics (e.g., mobility, location awareness). IoT is already integrated to the hybrid cloud/edge environment. However, the ecosystem lacks of automation due to the previously mentioned characteristics. Indeed, edge nodes are often manually selected during deployment time, and most of the regular quality-of-service (QoS) management procedures remain difficult to implement. This paper introduces a comprehensive semantic model called EdgeOnto. It encompasses all concepts related to IoT applied in the context of edge computing. The ultimate goal of EdgeOnto is to automate the several steps that make up the IoT services lifecycle in hybrid cloud/edge environment. On the one hand, semantics enable an automatic discovery of the relevant edge nodes that are suitable to host and execute IoT services considering their requirements. On the other hand, it allows supporting the specific QoS procedures that are related to such setting (e.g., low latency, mobility, jitter). The core ontology was designed with the Protégé open-source tool. A smart strawberry farming use case was implemented and evaluated for illustration purposes. The results validate the accuracy and the precision of the designed semantic matchmaker.

An Analysis of Machine Learning-Based Semantic Matchmaking

Interoperability remains to be one of the main challenges in the Internet of Things. The increasing number of IoT data sources from various vendors augments the complexity of integrating different sensors and actuators on the existing platforms, requiring human involvement and becoming error prone. To improve this situation, devices are usually coupled with a semantic description of their attributes. Such semantic descriptions, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Things Descriptions, TD</i> , are therefore an abstraction of devices, that is helpful to achieve a smoother integration of devices into IoT platforms. However, TD are usually vendor-based, so for large-scale IoT infrastructures, the integration complexity increases, as there will be different descriptions of similar sensors, provided by different vendors to be interconnected into IoT platforms. In this context, the paper assesses different ML-based semantic matchmaking approaches, against a sentence-based statistical similarity approach. For the ML approaches, the paper focuses on clustering and Natural Language Processing. The three approaches have been implemented on a realistic testbed, and experiments carried out show that the best performance achieved in terms of accuracy, time to completion of a matchmaking request, and memory usage is the NLP-based approach.

Memetic EDA-Based Approaches to QoS-Aware Fully Automated Semantic Web Service Composition

Comprehensive quality-aware automated semantic web service composition is an NP-hard problem, where service composition workflows are unknown, and comprehensive quality, i.e., Quality of services (QoS) and Quality of semantic matchmaking (QoSM) are simultaneously optimized. The objective of this problem is to find a solution with optimized or near-optimized overall QoS and QoSM within polynomial time over a service request. In this paper, we proposed novel memetic EDA-based approaches to tackle this problem. The proposed method investigates the effectiveness of several neighborhood structures of composite services by proposing domain-dependent local search operators. Apart from that, a joint strategy of the local search procedure is proposed to integrate with a modified EDA to reduce the overall computation time of our memetic approach. To better demonstrate the effectiveness and scalability of our approach, we create a more challenging, augmented version of the service composition benchmark based on WSC-08 \cite{bansal2008wsc} and WSC-09 \cite{kona2009wsc}. Experimental results on this benchmark show that one of our proposed memetic EDA-based approach (i.e., MEEDA-LOP) significantly outperforms existing state-of-the-art algorithms.

Bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery

Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and preferences. In this paper, we present a novel service discovery approach that can support user queries with various-size-grained text elements. Compared with existing approaches that only support semantics matchmaking in single texture granularity (either word level or paragraph level), our approach enables the requester to search for services with any type of query content with high performance, including word, phrase, sentence, or paragraph. Specifically, we present an unsupervised Bayesian probabilistic model, bi-Directional Sentence-Word Topic Model (bi-SWTM), to achieve semantic matchmaking between possible textual types of queries (word, phrase, sentence, paragraph) and the texts in web service descriptions, by mapping words and sentences in the same semantic space. The bi-SWTM captures textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible method to build the semantic links from user queries to service descriptions. The novel approach is validated using a collection of comprehensive experiments on ProgrammableWeb data. The results demonstrate that the bi-SWTM outperforms state-of-the-art methods on service discovery and classification. The visualization of the nearest-neighbored queries and descriptions shows the capability of our model on capturing the latent semantics of web services.

Addressing Natural Language Request Complexity: At Semantic Web Service Discovery Horizon

The wondrous influence of semantic web on Service-Oriented Architecture pushes it towards a realistic and self-driven architecture where publication, discovery, selection, composition, and monitoring of services are semi-automatically performed on the behalf of their hosts or mediators. In the direction of this realistic and self-driven architecture, this research work is adding one more realistic aspect of ‘interpretation of natural language request’ to making the service discovery more usable for novice users. Three contributions have been made: (1) description of natural language request using six-slab range (2) two algorithms for extraction, sub-request generation, inclusion of semantics and semantic matchmaking of natural language request, and (3) evaluation of proposed strategy with two semantic formalisms. The proposed algorithms handle each complex service request as an individual entity and extract the demand/s of the request by decomposing it to the simple request from conjunction, condition, and negation-oriented natural language request. The experimental evaluation of the proposed strategy signifies the given algorithms. The proposed work and result evaluation is a part of on-going research on a generic discovery mechanism for semantic web services.

Automatic categorization of Web service elements

PurposeSemantic categorization of Web services comprises a fundamental requirement for enabling more efficient and accurate search and discovery of services in the semantic Web era. However, to efficiently deal with the growing presence of Web services, more automated mechanisms are required. This paper aims to introduce an automatic Web service categorization mechanism, by exploiting various techniques that aim to increase the overall prediction accuracy.Design/methodology/approachThe paper proposes the use of Error Correcting Output Codes on top of a Logistic Model Trees-based classifier, in conjunction with a data pre-processing technique that reduces the original feature-space dimension without affecting data integrity. The proposed technique is generalized so as to adhere to all Web services with a description file. A semantic matchmaking scheme is also proposed for enabling the semantic annotation of the input and output parameters of each operation.FindingsThe proposed Web service categorization framework was tested with the OWLS-TC v4.0, as well as a synthetic data set with a systematic evaluation procedure that enables comparison with well-known approaches. After conducting exhaustive evaluation experiments, categorization efficiency in terms of accuracy, precision, recall and F-measure was measured. The presented Web service categorization framework outperformed the other benchmark techniques, which comprise different variations of it and also third-party implementations.Originality/valueThe proposed three-level categorization approach is a significant contribution to the Web service community, as it allows the automatic semantic categorization of all functional elements of Web services that are equipped with a service description file.

CoAP-based collaborative sensor networks in the Semantic Web of Things

The Semantic Web of Things (SWoT) integrates knowledge representation and reasoning techniques originally devised for the Semantic Web into Internet of Things architectures, in order to provide more advanced service/resource management and discovery. This paper proposes a novel SWoT framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The proposed mobile agent is able to discover devices and share smartphone embedded sensors in a peer-to-peer fashion. Efficient data stream mining is also integrated to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. Finally, a case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented. Experimental performance results on a real testbed assess the approach.

PaaSport semantic model: An ontology for a platform-as-a-service semantically interoperable marketplace

PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market's giants. The PaaSport Marketplace aims to avoid the provider lock-in problem by allowing Platform provider SMEs to roll out semantically interoperable PaaS offerings and Software SMEs to deploy or migrate their applications on the best-matching offering, through a thin, non-intrusive Cloud broker. In this paper, we present the PaaSport semantic model, namely an OWL ontology, extension of the DUL ontology. The ontology is used for semantically representing (a) PaaS offering capabilities and (b) requirements of applications to be deployed. The ontology has been designed to optimally support a semantic matchmaking and ranking algorithm that recommends the best-matching PaaS offering to the application developer. The DUL ontology offers seamless extensibility, since both PaaS Characteristics and parameters are defined as classes; therefore, extending the ontology with new characteristics and parameters requires the addition of new specialized subclasses of the already existing classes, which is less complicated than adding ontology properties. The PaaSport ontology is evaluated through verification tools, competency questions, human experts, application tasks and query performance tests.

Graph-Based Semantic Web Service Composition for Healthcare Data Integration.

Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

A CoAP-based framework for collaborative sensing in the Semantic Web of Things

Abstract: This paper proposes a novel Semantic Web of Things framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The framework also integrates efficient data stream mining to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. A case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented and experimental performance results on a real testbed are provided.

Facilitating resource sharing and selection in ubiquitous multi-user environments

Mobile technologies have increased the interest of industry and academia in providing users with information and services anytime and anywhere. Some services are customized according to the users’ characteristics, attempting to create ubiquitous environments that enable such users to share and discover resources. However, research that addresses the creation of these environments is scant. In this article, we go further than single-user pervasive systems, by developing a support that manages shared resources in ubiquitous multi-user environments. Our contribution is a semantic matchmaking service capable of finding the most suitable resources that satisfy the users’ requirements. This service processes the users’ requests, their context, and the community participating in the resource sharing process, by respecting restrictions, the natural interaction among participants, and the changes that this interaction produces. We model the environment, shared resources, and users as dynamic entities that generate new information, which might affect the availability states of such resources. These states have been defined through the Contextual-Graphs formalism to incorporate new knowledge that can refine, extend, or customize such states, according to the users’ habits. Our matchmaking service has been deployed across the Computer Science Department of CINVESTAV-IPN research center, where it has been validated by means of several test cases.

서비스 지향 임무 수행을 위한 온톨로지 기반 시맨틱 매칭 방법

서비스 지향 임무 수행을 위한 온톨로지 기반 시맨틱 매칭 방법

Leveraging fuzzy dominance relationship and machine learning for hybrid web service discovery

Nowadays, web service discovery plays an important role in several application domains. Existing semantic web service matchmakers usually use crisp logic-based matching, token-based similarity measures, and eventually machine learning that combines the individual scores into a global score. Unfortunately, these approaches entail an undesirable compensation between partial scores and consequently the final decision can be erroneous. Furthermore, there is no ideal matching similarity for assessing the parameters closeness, therefore, several similarity measures must be used to resolve the discovery issue. In this paper, we propose a hybrid semantic matchmaker that combines four textual similarity measures and a pure logic matching algorithm in order to search and rank the advertised services. The different scores are aggregated according to the fuzzy dominance function. We have conducted an exhaustive study based on the OWLS-TC benchmark to verify the effectiveness and efficiency of our approach.

Context Aware QOS based Web Service Discovery

This work concentrates on context of the execution environment in service selection process due to dynamically varying QoS attributes in service execution environment. In this paper, a context aware QoS Web Service(WS) description and discovery is proposed to overcome the issues related to QoS requirements in WS. In the proposed scheme, OWL-Q with OWL CONtext ONtology (CONON) ontology is modeled to provide the WS description with context aware QoS descriptions. To discover the most appropriate WS based on context and QoS descriptions, a context aware QoS semantic matchmaking algorithm is newly proposed based on Constraint Satisfaction Optimization Approach (CSOP). An efficient User Priority based Ranking (UPR) mechanism is performed based on user preferences to rank the matched WSs. Consequently, precision and recall of proposed context aware QoS WS discovery is compared with the existing QoS WS discovery and conclude that proposed approach achieves high precision and recall than existing QoS based approach.

A Utilization Framework of Ubiquitous Resources for Service Robots Using Semantic Matchmaking

The automatic and flexible utilization of ubiquitous service resources is a key prerequisite for autonomous robots in ambient intelligent environments. In this paper, a novel component-based roboti...

Personalized manufacturing service recommendation using semantics-based collaborative filtering

With the growing emergence of distributed manufacturing services over the Internet, many efforts have been made to semantically describe the manufacturing services, facilitating accurate retrieval of the required services derived from semantic matchmaking of service capabilities. However, semantic annotation of vast manufacturing services remains an open issue. Most of the efforts have been proposed to semantically annotate the manufacturing services in an inefficient manual manner. This article presents a novel collaborative filtering method for automating the semantic annotation of manufacturing service capabilities and making the personalized manufacturing service recommendation based on the usage data of previous retrievals made for similar annotations. A prototype system is built and validated with an illustrative example from manufacturing industry to demonstrate the feasibility and efficiency of the proposed approach for automatic service annotation and personalized service recommendation.

Semantic Matchmaking for Kinect-Based Posture and Gesture Recognition

Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A general data model and the corresponding ontology provide the formal underpinning for posture and gesture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking, exploiting non-standard inference services, allows to: (i) detect postures via on-the-fly comparison of the annotations with standard posture descriptions stored as instances of a proper Knowledge Base; (ii) compare subsequent postures in order to recognize gestures. The framework has been implemented in a prototypical tool and experimental tests have been carried out on a reference dataset. Preliminary results indicate the feasibility of the proposed approach.

A Mobile Matchmaker for the Ubiquitous Semantic Web

The Semantic Web and Internet of Things visions are converging toward the so-called Semantic Web of Things (SWoT). It aims to enable smart semantic-enabled applications and services in ubiquitous contexts. Due to architectural and performance issues, it is currently impractical to use existing Semantic Web reasoners. They are resource consuming and are basically optimized for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper presents Mini-ME, a novel mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios are presented and an experimental performance evaluation is provided both on a PC testbed (against other popular Semantic Web reasoners) and on a smartphone.

A Novel Approach: A Hybrid Semantic Matchmaker for Service Discovery in Service Oriented Architecture

Since service-oriented architectures make the commercial systems more reliable and reusable, they have gained more popularity in industry and scientific community in recent decades. Service-oriented architectures bring flexibility and reusability to software design. Due to the increasing number of services on the Web, finding a service which is suited to user requirements is crucial. The process of finding suitable services to user request is one of the main purposes of service-oriented architectures. Many methods have been proposed for service discovery in service-oriented architectures that try to fulfil user requirements and offer suitable services to user request; however the proposed methods do not have enough precision for discovering suitable services. In this paper, we propose a method for service discovery which offers more accurate services according to user request. The proposed method is a hybrid semantic matchmaker for service discovery in service oriented architecture. By providing accurate services suitable to user requests, we have greatly increased the reusability rate and reduced the time and cost of software development .