In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.

Abstract-The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.

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Nowadays, web applications are popular targets for security attackers. Using specific security mechanisms, we can prevent or detect a security attack on a web application, but we cannot find out the criminal who has carried out the security attack. Being unable to trace back an attack, encourages hackers to launch new attacks on the same system. Web application forensics aims to trace back and attribute a web application security attack to its originator. This may significantly reduce the security attacks targeting a web application every day, and hence improve its security. The aim of this paper is to carry out a detailed overview about the web application forensics. First, we define the web applications forensics, and we present a taxonomic structure of the digital forensics. Then, we present the methodology of a web application forensics investigation. After that, we illustrate the forensics supportive tools for a web application forensics investigation. After that, we present a detailed presentation of a set of the main considered web application forensics tools. Finally, we provide a comparison of the main considered web application forensics tools.

Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this paper introduces the semantic annotation categories, tools, domains and models.Comment: 8 pages, 3 figure