Web services discovery and rank: An information retrieval approach

Hao, Yong; Zhang, Yanchun; Cao, Tru H.

doi:10.1016/j.future.2010.04.012

Cited by 61 publications

(23 citation statements)

References 27 publications

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“…Originally, only low-level information on the operations of the web resources (such as the basic interface details like method names, types and parameters) was available, or non-functional criteria such as response time or usage data (Birukou et al, 2007); Al-Masri and Mahmoud, 2007). The development of registries based on standards such as UDDI allowed the addition of metadata-based techniques (Dong et al, 2004;Chukmol, 2008;Crasso et al, 2008), initially based on traditional keyword search but eventually considering more advanced IR techniques (Plebani and Pernici, 2009) and, in line with the priorities of our work, a more user-oriented view (Rong and Liu, 2010), explicitly based on requirements (Hao and Zhang, 2007;Hao et al, 2010;Nazir et al, 2008). (Skoutas et al, 2010) also bases the discovery of web services on the requirements and, ranks and clusters the relevant services with objective measures based on dominance relationships among them.…”

Section: Related Workmentioning

confidence: 85%

A semantic approach for the requirement-driven discovery of web resources in the Life Sciences

et al. 2012

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Abstract. Research in the Life Sciences depends on the integration of large, distributed and heterogeneous web resources (e.g. data sources and web services). The discovery of which of these resources are the most appropriate to solve a given task is a complex research question, since there are many candidate resources and there is little, mostly unstructured, metadata to be able to decide among them.In this paper we contribute with a semi-automatic approach, based on semantic techniques, to assist researchers in the discovery of the most appropriate web resources to fulfill a set of requirements. The main feature of our approach is that it exploits broad knowledge resources in order to annotate the unstructured texts that are available in the emerging web-based repositories of web resource metadata.The results show that the web resource discovery process benefits from a semanticbased approach in several important aspects. One of the advantages is that the user can express her requirements in natural language avoiding the use of specific vocabularies or query languages. Moreover, the discovery exploits not only the categories or tags of web resources, but also their description and documentation.

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Section: Related Workmentioning

confidence: 85%

A semantic approach for the requirement-driven discovery of web resources in the Life Sciences

et al. 2012

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“…Relying on such parameter names most likely leads to incorrect matching. Hao et al [16] use the TF-IDF score to compute the similarity between Web service clusters. They propose two indexing structures of TF-IDF vectors to provide relevancy score and ranking of the output results.…”

Section: Related Workmentioning

confidence: 99%

Web Service Discovery for Large Scale Iot Deployments

Elshater¹,

Elgazzar²,

Martin³

2016

STSC

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IoT scenarios cannot tolerate long latency in finding relevant Web services to consume on the fly or dynamically integrate in IoT applications providing real time services. In this paper, we present a comprehensive Web service discovery approach for large scale IoT deployments. We leverage the information available in the Web service description document to cluster large service repositories into functionally similar groups to reduce the discovery latency and improve precision. Then, we use statistical indexing techniques to generate data structures for each cluster for fast and efficient matching. In this research, we propose and study the performance of four matching algorithms: semantic keyword matching using Normalized Google Distance (NGD), brute force search over Term Frequency-Inverse Document Frequency (TF-IDF) matrix, K-Dimensional (K-D) tree, and Locality Sensitive Hashing (LSH). Our thesis is that indexing-based discovery algorithms (i.e., K-D tree and LSH) provide a much faster response with comparable precision, while NGD and brute force search provide a slightly better accuracy, but at the cost of high latency. Our experimental results show that we can reduce the query latency by up to 5x fold, while achieving comparable precision with the state-of-the-art service discovery mechanisms.

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“…Xu et al [51] and Wang et al [52] used semantic matching technology to analyse service description files, and then ranked services in the candidate set according to their semantic distance from the user's request. A schema tree matching algorithm was proposed by Hao et al [53] to rank web services automatically. In [54], a service network is first constructed by analysing descriptions of the Web services from Web pages or WSDL files, then the PageRank algorithm is used to rank the related services with respect to a specified query topic.…”

Section: Related Workmentioning

confidence: 99%