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This document discusses using machine learning techniques to automatically generate semantic metadata for web services. It explores supervised learning approaches for classifying domains, datatypes and categories of web services. It also examines unsupervised clustering algorithms for grouping web services into coherent categories. The techniques are evaluated on a collection of web services and forms, with results showing the machine learning approaches outperform simple baselines.

Learning to attach semantic metadata to Web Services Andreas Heß Nicholas Kushmerick Computer Science Department, University College Dublin, Ireland Presented by Lin Chi Hsiang

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Introduction ,[object Object],[object Object]

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Supervised domain and datatype classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Problem formulation

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Supervised category classification ,[object Object],[object Object],[object Object],Problem formulation

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Evaluation ,[object Object],[object Object],[object Object]

Unsupervised category clustering ,[object Object],[object Object],[object Object],[object Object],Clustering algorithms

Quality Metrics for Clustering ,[object Object],[object Object],[object Object]

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Conclusion ,[object Object],[object Object]

Classification is a step by step practice for allocating a given piece of input into any of the given category. Classification is an essential Machine Learning technique. There are many classification problem occurs in different application areas and need to be solved. Different types are classification algorithms like memory-based, tree-based, rule-based, etc are widely used. This work studies the performance of different memory based classifiers for classification of Multivariate data set from UCI machine learning repository using the open source machine learning tool. A comparison of different memory based classifiers used and a practical guideline for selecting the most suited algorithm for a classification is presented. Apart fromthat some empirical criteria for describing and evaluating the best classifiers are discussed

IRE Semantic Annotation of Documents

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Semantic annotation is done through first representing words and documents in the vector space model using Word2Vec and Doc2Vec implementations, the vectors are taken as features into a classifier, trained and a model is made which can classify a document with ACM classification tree categories, with the help of Wikipedia corpus. Project Presentation: https://youtu.be/706HJteh1xc Project Webpage: http://rohitsakala.github.io/semanticAnnotationAcmCategories/ Source Code: https://github.com/rohitsakala/semanticAnnotationAcmCategories References: Quoc V. Le, and Tomas Mikolov, ''Distributed Representations of Sentences and Documents ICML", 2014

Database Design and the ER Model, Indexing and Hashing

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AI to advance science research

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Object‐oriented software development is an evolutionary process, and hence the opportunities for integration are abundant. Conceptually, classes are encapsulation of data attributes and their associated functions. Software components are amalgamation of logically and/or physically related classes. A complete software system is also an aggregation of software components. All of these various integration levels warrant contemporary integration techniques. Traditional integration techniques towards the end of software development process do not suffice any more. Integration strategies are needed at class level, component level, sub‐system level, and system levels. Classes require integration of methods. Various types of class interaction mechanisms demand different testing strategies. Integration of classes into components presses its own integration requirements. Finally, the system integration demands different types of integration testing strategies.

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Data partitioning methods are used to partition the data values with similarity. Similarity measures are used to estimate transaction relationships. Hierarchical clustering model produces tree structured results. Partitioned clustering produces results in grid format. Text documents are unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable cluster count. Textual data elements are divided into two types’ discriminative words and nondiscriminative words. Only discriminative words are useful for grouping documents. The involvement of nondiscriminative words confuses the clustering process and leads to poor clustering solution in return. A variation inference algorithm is used to infer the document collection structure and partition of document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition documents. DPM clustering model uses both the data likelihood and the clustering property of the Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without requiring the number of clusters as input. Document labels are used to estimate the discriminative word identification process. Concept relationships are analyzed with Ontology support. Semantic weight model is used for the document similarity analysis. The system improves the scalability with the support of labels and concept relations for dimensionality reduction process.

Literature Survey: Clustering Technique

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Clustering is a partition of data into the groups of similar or dissimilar objects. Clustering is unsupervised learning technique helps to find out hidden patterns of Data Objects. These hidden patterns represent a data concept. Clustering is used in many data mining applications for data analysis by finding data patterns. There is a number of clustering techniques and algorithms are available to cluster the data object. According to the type of data object and structure appropriate clustering technique is selected. This survey focuses on the clustering techniques for their input attribute data type, their input parameters and output. The main objective is not to understand the actual working of clustering technique. Instead, the input data requirement and input parameters of clustering technique are focused.

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In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method.

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journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals, yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal

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