Probabilistic Approach for Diversifying Web Services Discovery and Composition

Naim, Hafida; Aznag, Mustapha; Quafafou, Mohamed; Durand, Nicolas

doi:10.1109/icws.2016.19

Cited by 13 publications

(5 citation statements)

References 17 publications

(34 reference statements)

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“…In future work, we will extend our system by predicting user behavior in a given area during the next time window. We will also use our recent works on approximation of frequent itemset borders [10,9] and on probabilistic topic models [27,26] for enrichment and trajectory recommendation purposes.…”

Section: Resultsmentioning

confidence: 99%

Detecting behavior types of moving object trajectories

Almuhisen

Durand

Quafafou

2018

Int J Data Sci Anal

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Trajectory mining is a challenging and crucial problem especially in the context of smart cities where many applications depend on human behaviors. In this paper, we characterize such behaviors by patterns, where each pattern type represents a particular behavior, e.g. emerging, latent, lost, etc. From GPS raw data, we introduce algorithms that allow computing a formal concept lattice which encodes optimal correspondences between hidden patterns and trajectories. In order to detect behaviors, we propose an algorithm that analyses the evolution of the discovered formal concepts over time. The method generates tagged city maps to easily visualize the resulting behaviors at different spatio-temporal granularity values. Refined or coarse analysis can thus be performed for a given situation. Experimental results using real-world GPS trajectory data show the relevance of the proposed method and the usefulness of the resulting tagged city maps.

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Section: Resultsmentioning

confidence: 99%

Detecting behavior types of moving object trajectories

Almuhisen

Durand

Quafafou

2018

Int J Data Sci Anal

Self Cite

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“…Cao et al [6] propose a mashup service clustering method that exploits a two-level topic model to mine the latent useful and novel topics. Hafida et al [25] propose a new content-based topic model to capture the maximal common semantic of sets of services. Samanta et al [29] use the Hierarchical Dirichlet Process to intelligently discover the functionally relevant services.…”

Section: Related Workmentioning

confidence: 99%

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

Luo

Zhao

et al. 2022

World Wide Web

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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.

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“…Currently, a number of attempts have been made to focus on solving the issue of properly discovering the required services within the semantic-based approaches (e.g., [5], [37]). Among these efforts, the similarity-based approaches are very representative [5].…”

Section: A Web Service Discoverymentioning

confidence: 99%

Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

Huang

Zhao

2020

IEEE Access

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With the rapid growth of Web services, the demand for discovering the optimal services to satisfy the users' requirements is no longer an easy task. The critical issue in the process of service discovery is to conduct a similarity calculation. To solve such an issue, this study proposes an effective approach that combines the Embeddings from Language Models (ELMo) representation and Convolutional Neural Network (CNN) to obtain a more accurate similarity score for retrieving target Web services. More specifically, first, the study adopts the ELMo model to generate effective word representations for capturing the sufficient information from services and queries. Then, the word representations are used to compose a similarity matrix, which will be taken as the input for the CNN to learn the matching relationships. Finally, the combination of the ELMo representation and CNN is used to address the representation and interaction processes within the matching task to improve the service discovery performance. The results demonstrate the effectiveness of our proposed approach for retrieving better targeted Web services.

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Probabilistic Approach for Diversifying Web Services Discovery and Composition

Cited by 13 publications

References 17 publications

Detecting behavior types of moving object trajectories

Detecting behavior types of moving object trajectories

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

Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

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