Context-Sensitive Service Discovery Meets Information Retrieval

2007 ◽  
Author(s):  
Julia Kuck ◽  
Melanie Gnasa
Keyword(s):  
Information Retrieval ◽  
Service Discovery ◽  
Context Sensitive

Exploiting named entity recognition for improving syntactic-based web service discovery

2018 ◽  
Vol 45 (3) ◽  
pp. 398-415 ◽  
Author(s):  
Ignacio Lizarralde ◽  
Cristian Mateos ◽  
Juan Manuel Rodriguez ◽  
Alejandro Zunino
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Software Industry ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Web Service Discovery ◽  
Named Entity ◽  
Text Corpora

Web Services have become essential to the software industry as they represent reusable, remotely accessible functionality and data. Since Web Services must be discovered before being consumed, many discovery approaches applying classic Information Retrieval techniques, which store and process textual service descriptions, have arisen. These efforts are affected by term mismatch: a description relevant to a query can be retrieved only if they share many words. We present an approach to improve Web Service discoverability that automatically augments Web Service descriptions and can be used on top of such existing syntactic-based approaches. We exploit Named Entity Recognition to identify entities in descriptions and expand them with information from public text corpora, for example, Wikidata, mitigating term mismatch since it exploits both synonyms and hypernyms. We evaluated our approach together with classical syntactic-based service discovery approaches using a real 1274-service dataset, achieving up to 15.06% better Recall scores, and up to 17% Precision-at-1, 8% Precision-at-2 and 4% Precision-at-3.

Know Your World Better

2018 ◽  
pp. 777-793
Author(s):  
Srinivasa K. G. ◽  
Satvik Jagannath ◽  
Aakash Nidhi
Keyword(s):  
Information Retrieval ◽  
Augmented Reality ◽  
Real Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Sensitive Information ◽  
Context Sensitive ◽  
Close Proximity ◽  
Augmented Reality Application ◽  
Device User

Mobile devices are changing the way people live. Users have everything on their fingertips and to support them, there are scores of application which add to the usability and comfort. “Know your world better” is an Augmented Reality application developed for Android. This application helps the user to find friends and locate places in close proximity. In this paper we talk about an application that describes a method of augmenting Point of Interests (POI's) on a mobile device. User has to move his phone pointing in a direction of his choice and POI's if any are shown in real time. The user's interest with respect to the environment is inferred from speech or by selecting from the choices; this data is used for information retrieval from the cloud. The result of context-sensitive information retrieval is augmented onto the view of the mobile and provides speech output.

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2009 ◽  
Vol 5 (4) ◽  
pp. 44-57 ◽  
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
Document Models ◽  
Clustering Quality

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this article, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

Algorithm for Web Service Discovery Based on Information Retrieval Using WordNet and Linear Discriminant Functions

2008 ◽  
Vol 12 (2) ◽  
pp. 182-189 ◽  
Author(s):  
Ricardo Sotolongo ◽  
◽  
Carlos Kobashikawa ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Distributed Applications ◽  
Service Description ◽  
Lexical Database ◽  
Linear Discriminant ◽  
Web Service Discovery ◽  
The One

An algorithm based on information retrieval that applies the lexical database WordNet together with a linear discriminant function is proposed. It calculates the degree of similarity between words and their relative importance to support the development of distributed applications based on web services. The algorithm uses the semantic information contained in the Web Service Description Language specifications and ranks web services based on their similarity to the one the developer is searching for. It is applied to a set of 48 real web services in five categories, then compared them to four other algorithms based on information retrieval, showing an averaged improvement over all data between 0.6% and 1.9% in precision and 0.7% and 3.1% in recall for the top 15 ranked web services. The objective was to reduce the burden and time spent searching web services during the development of distributed applications, and it can be used as an alternative to current web service discovery systems such as brokers in the Universal Description, Discovery, and Integration (UDDI) platform.

Web Service Clustering using a Hybrid Term-Similarity Measure with Ontology Learning

2014 ◽  
Vol 11 (2) ◽  
pp. 24-45 ◽  
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Wuhui Chen ◽  
Keun Ho Ryu
Keyword(s):  
Information Retrieval ◽  
Service Discovery ◽  
Cluster Center ◽  
Ontology Learning ◽  
Positive Effects ◽  
Document Frequency ◽  
Service Similarity ◽  
Term Similarity ◽  
Identification Approach ◽  
Service Clustering

Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. In this paper, the authors present a method that first adopts ontology learning to generate ontologies via the hidden semantic patterns existing within complex terms. If calculating similarity using the generated ontology fails, it then applies an information-retrieval-based method. Another important issue is identifying the most suitable cluster representative. This paper proposes an approach to identifying the cluster center by combining service similarity with term frequency–inverse document frequency values of service names. Experimental results show that our term-similarity approach outperforms comparable existing approaches. They also demonstrate the positive effects of our cluster-center identification approach.

Know Your World Better

2016 ◽  
Vol 7 (2) ◽  
pp. 1-15
Author(s):  
Srinivasa K. G. ◽  
Satvik Jagannath ◽  
Aakash Nidhi
Keyword(s):  
Information Retrieval ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Mobile Device ◽  
Sensitive Information ◽  
Context Sensitive ◽  
Speech Output ◽  
Close Proximity ◽  
Augmented Reality Application ◽  
Device User

Mobile devices are changing the way people live. Users have everything on their fingertips and to support them, there are scores of application which add to the usability and comfort. “Know your world better” is an Augmented Reality application developed for Android. This application helps the user to find friends and locate places in close proximity. In this paper we talk about an application that describes a method of augmenting Point of Interests (POI's) on a mobile device. User has to move his phone pointing in a direction of his choice and POI's if any are shown in real time. The user's interest with respect to the environment is inferred from speech or by selecting from the choices; this data is used for information retrieval from the cloud. The result of context-sensitive information retrieval is augmented onto the view of the mobile and provides speech output.

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

2011 ◽  
pp. 307-319
Author(s):  
Min Song ◽  
Xiaohua Hu ◽  
Illhoi Yoo ◽  
Eric Koppel
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Cell Structure ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Self Organizing Map ◽  
Context Sensitive ◽  
Growing Cell ◽  
The Neural Network ◽  
Structure Algorithm

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this paper, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

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