A Probabilistic Latent Factor approach to service ranking

Cassar, Gilbert; Barnaghi, Payam; Moessner, Klaus

doi:10.1109/iccp.2011.6047850

Cited by 9 publications

(15 citation statements)

References 12 publications

(9 reference statements)

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“…It uses a non-logic-based probabilistic service matchmaking component [35,36] to find a short list of more relevant services and a logic-based component that uses individual Links between a source parameter and a destination parameter [34] to verify that the services in the short list are compatible with the IO signature of the request.…”

Section: Matchmakingmentioning

confidence: 99%

“…The non-logicbased component works by computing the degree of match between a service request and a service description in the latent factor space. We map the request templates into the latent factor space using the folding-in techniques as described in [36]. The degree of matching between the probability distribution of latent factors for the request and a service description can be calculated using a vector similarity measure (e.g., the Cosine similarity).…”

Section: Service Rankingmentioning

confidence: 99%

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Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Wang

Cassar

et al. 2013

Automatika

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Original scientific paperSemantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the "Things". We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.Key words: Internet of things, Knowledge representation, Ontology engineering, Distributed service storage, Service discovery, Service ranking Prikaz znanja u internetu stvari: semantičko modeliranje i njegove primjene. Semantičko modeliranje pruža potencijalnu osnovu za meudjelovanje različitih sustava i aplikacija unutar interneta stvari (IoT). Meutim, postojeći radovi uglavnom su fokusirani na upravljanje IoT resursima, ali ne i pristupu i korištenju informacija koje generira "stvar". Predstavljamo projektiranje sveobuhvatnog i laganog semantičkog opisnog modela za prikaz znanja u IoT domeni. Projektiranje slijedi široko-priznate najbolje običaje u inženjerstvu znanja i ontološkom modeliranju. Korisnicima se dopušta proširenje modela povezivanjem na eksterne ontologije, baze znanja ili postojeće povezane podatke. Skalabilni pristup IoT uslugama i resursima postiže se kroz distribuirano, semantičko projektiranje pohrane. Upotrebljivost modela takoer je ilustrirana kroz metodu pronalaska IoT usluga.Ključne riječi: Internet stvari, prikaz znanja, ontološko inženjerstvo, distribuirana pohrana usluga, pronalazak usluga, poredak usluga

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Section: Matchmakingmentioning

confidence: 99%

Section: Service Rankingmentioning

confidence: 99%

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Wang

Cassar

et al. 2013

Automatika

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“…Thus, we obtain automatically an efficient ranking of the services retrieved. 7 http://www.cs.princeton.edu/ blei/ctm-c/index.html We propose also to use an other approach based on the proximity measure called Multidimentional Angle (also known as Cosine Similarity); a measure which uses the cosine of the angle between two vectors [20], [7]. In the first time, we represent the user's query as a distribution over topics.…”

Section: B a Probabilistic Topic Model Approachmentioning

confidence: 99%

“…Cassar et al [6], [7] investigated the use of probabilistic machine-learning techniques (PLSA and LDA) to extract latent factors from semantically enriched service descriptions. These latent factors provide a model which represents any type of service's descriptions in a vector form.…”

mentioning

confidence: 99%

Correlated Topic Model for Web Services Ranking

Aznag¹,

Quafafou²,

Jarir³

2013

IJACSA

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Abstract-With the increasing number of published Web services providing similar functionalities, it's very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn).

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“…They hybrid matchmaking method combines the probabilistic matchmaking method described in [5] to a link-based matchmaking [2] described above thus increasing the accuracy of the results. The list of search results returned by the matchmaking method ranks the most relevant services at the top of the list.…”

Section: Service Search and Matchmakingmentioning

confidence: 99%

Composition of services in pervasive environments: A Divide and Conquer approach

Cassar

Barnaghi

Wang

et al. 2013

2013 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.

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A Probabilistic Latent Factor approach to service ranking

Cited by 9 publications

References 12 publications

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Knowledge Representation in the Internet of Things: Semantic Modelling and its Applications

Correlated Topic Model for Web Services Ranking

Composition of services in pervasive environments: A Divide and Conquer approach

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