Whenever some user posts on social media networks, knowingly or unknowingly their activity is registered in countless online repositories. This exposes parts of the data to be publicly available; as a consequence of this a forensic analyst can reveal past activities, reconstruct a biased timeline and recover deleted data of the suspect. Movie ontology maps the input from movies and videos of user via facebook graph API to produce adaptive results of user activity related to their entertainment activities. Movie ontology will demonstrate how to reconstruct the social behavior of the user from the file and how to interpret the gained information as a potential source of evidence in digital forensic investigations.

Forensic future of social media analysis using web ontology

This paper is aimed to provide a brief description about the working and the processes involved with the system. And it is also focusing on the challenges during the deployment of ontology for university examination system. The development of semantic web also enhanced the creation and publishing of ontology and relationships in almost all domains. The ontology developed in this paper using protege can be extended to model and create data and metadata elements which are helpful for developing university examination applications and for knowledge representation in semantic web.

Knowledge representation of university examination system ontology for semantic web

Building information model (BIM) is a new emergence in the field of Architecture, Engineering and Construction (AEC) industry, which facilitates detailed digital representation of a building. The BIM archetype describes the details of a building in the Industry Foundation Class (IFC), which is a platform-agnostic, open data model developed by buildingSMART. The BIM data model embeds interoperability and querying facility among its principal traits in support of the AEC industry. The building information is also required by domains outside the AEC industry, to develop a wide variety of applications such as smart building and indoor navigation systems. In such cases, the IFC data model falls short in supporting interaction with applications of external domains other than AEC. The proposed work is aimed to perform a study on various efforts undertaken in the integration of BIM model with external domain querying and reasoning systems, to retrieve details from underlying building models.

AI based Semantic Extensibility and Querying Techniques for Building Information Model

Sentiment analysis is used to quantify the sentiment of unstructured text, which could be used to draw insights on public opinions about products, services, or any topic that is of relevance. Most of the available sentiment analysis implementations provide an overall sentiment which is not sufficient because it does not provide an aspect-based sentiment. In this work, we present an extendible ontology-based approach for aspect-based sentiment analysis. In this work, we propose a novel and extendible Ontological approach for aspect-based Sentiment Analysis. It also focuses on learning the aspects using topic modelling to automate the aspect detection. The results indicate that the proposed approach can be effective in classifying sentiments under different aspects. The proposed approach may have applications in domains such as e-commerce web sites, product reviewing platforms and social media platforms.

Ontological Approach for Aspect-Based Sentiment Analysis

The semantic web is evolving and helping users to find, share, understand and combine the information and be processed by the automated tools. With the traditional web, it is a time-consuming and tedious task to find the necessary information. By using the semantic web, we obtain information in a way that is more efficient and fast and avoid all the unnecessary data. Semantic web helps to make intelligent decisions by understanding the meaning of data. Educational institutions have the requirement of keeping track of resources like students, faculty, visitors etc. In our work, we have recorded the information of various resources of the institution in Web Ontology Language (OWL) using Protege tool. Then the SPARQL Query is used to acquire useful information. This helps to keep track of the various required resources. The developed ontology can be extended and reused by other institutions based on the need.

Resource tracking in institutions using semantic web

A Study On Verbalization Of OWL Axioms Using Controlled Natural Language

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A Study On Verbalization Of OWL Axioms Using Controlled Natural Language