Mohammed Nazim Uddin

A Collaborative Ontology-Based User Profiles System

The main goal of this research is to investigate the techniques that implicitly build ontology-based user profiles . In particular, automatically building profiles based on users information (blogs, publications, home pages,...) generated from the internet. We proposed a framework to search the user details to build profile automatically. An initial profile is constructed with user interest in hierarchical manner and this profile is learned by assigning user details collected by our search method. Main focused on how quickly we can collect users information and achieve a profile stability, and how effectively improve profiles. Along with the framework we also consider a new method to extract feature from document. A Wordnet and Lexico-syntactic pattern for hyponyms approach is applied to export the important feature of document to represent the profile ontology. The user profile further improved by learning interesting knowledge from similar profiles.

The wordNet based semantic relationship between tags in folksonomies

Collaborative tagging is a popular method in social resource sharing system. The reason why collaborative tagging system becomes popular is users can freely tags on the resources, like Flickr1 and Delicious2 . Any relationship between tags and resources are not made explicitly when tagging in folksonomy. Due to lack of semantic meaning, it is difficult for users to find related resources. Also, short of sufficient number of relevant tags increase the data sparsity which decreases the rate of information extraction against the user queries. Defining semantic relationship between tags, resources and users is an important research issue to retrieval of related information from folksonomies. In this paper, we propose a WordNet based method to find the semantic relationship between tags in folksonomy. Finally, the experiment results using Flickr data show our proposed method works well finding semantic relationship between tags in folksonomy.

Personalized Semantic Search Using ODP: A Study Case in Academic Domain

Personalized search utilizes the user context in a form of profile to increase the information retrieval accuracy with user’s interests. Recently, semantic search has greatly attracted researchers’ attention over the traditional keyword-based search because of having capabilities to figure out the meaning of search query, understanding users’ information needs accurately using semantic web technology. In this paper, a ODP (open directory project)-based approach for personalized semantic information search is proposed. A reference ontology is generated by utilizing ODP to model user’s interests and semantic search space. User profile is initially derived by matching between user’s details and the reference ontology to model his/her interests and preference. Semantic search space is constructed by fuzzily classifying documents into the reference ontology. We also present an evaluation of our proposed method which indicates a considerable improvement of search accuracy with the field of application human judgment.

User-agent based access control for DLNA devices

DLNA based media sharing is very popular nowadays. In current DLNA specification, a DLNA device advertises its presence to everyone in the network. Any control point application receiving the advertisement can access/control the device. However, with increasing popularity and availability of public Wi-Fi hotspots, it is necessary for devices to have some sort of access control. DLNA specification has no mandatory authentication procedure. So a device receiving a request from any unwanted control point cannot block/verify its access. The UPnP recommended authentication procedure is computationally expensive and complex for most personal devices. So, in this paper we propose a simple User-Agent based access control system that is effective to protect devices from unwanted control point applications.

Construction of semantic user profile for personalized web search

User profile is an essential component for accessing the personalized information from the Web. Efficiency of personalized accessed information highly depends on how to model the user details to construct user profile. Previously, user profile was constructed by collecting list of keywords to inferring user interests. These kinds of approaches are not sufficient for many applications. In this paper, we have proposed a new method for constructing semantic user profile for personalized information access. Users query is extended using ontological profile for generation of personalized search context. Experimental results show that our method of constructing semantic profile is effective for searching information with individual needs.

Character-Net: Character Network Analysis from Video

Managing the video content for searching and summarizing has become a challenging task. Extracting semantics from video scenes enables information to be presented in a more understandable manner. Finding the semantics between video contexts is a difficult task; much recent research has focused on this issue. Most videos, such as TV serials and commercial movies, are character- centric. Therefore, the context and relationship between characters needs to be organized systematically to analyze the video. So, it is necessary to identify the contextual relationships between characters in the scene and the video. We propose Character-Net, a network structure. It finds characters in a group of shots, extracts the speaker and listeners in the scene, represents it with character-based graphs and draws the relationship between all characters by accumulating the character-based graphs at video. In this paper, we describe how to build Character-Net. Experimental results show Character-Net is an effective methodology to extract the major characters in videos.

EXPERTS SEARCH AND RANK WITH SOCIAL NETWORK: AN ONTOLOGY-BASED APPROACH

Experts finding, one of the most important tasks in social networks, is aimed at identifying individuals with relevant expertise or experience in a given topic. Several approaches have been proposed for finding experts in social networks from documents or web repositories. However, the semantic approach for modeling the information to find experts has not yet been explored. In this paper, we propose a novel method to index the academic information in an ontology-based model for finding and ranking the experts in a particular domain. Additionally, we propose an effective method to construct the academic social network by exploring the relations among the experts and measuring the score of each expert. The score of an expert is measured considering the contributions of relevant publications and relationships among other expert candidates. It is very efficient to find and ranking experts to take advantage of the millions of candidate experts being with relationships. An experiment conducted to evaluate our model shows that experts finding and ranking with an ontological approach integrated with the social network is more effective than other approaches.

An ontology based model for experts search and ranking

Experts finding is an important issue for finding potential contributors or expertise in a specific field. In scientific research, researchers often try to find an experts list related to their interest areas to acquire the knowledge about state arts of current research and novices can get benefit to find new ideas for research. In this paper, we proposed an ontological model to find and rank the experts in a particular domain. First, an Academic Knowledge Base(AKB) is built for a particular domain and then an academic social network (ASN) is constructed based on the information provided by the knowledge base for a given topic. In our approach, we proposed a cohesive modeling approach to investigate academic information considering heterogeneous relationship. Our proposed model provides a novel approach to organize and manage the real world academic information in a structural way which can share and reuse by others. Based on this structured academic information an academic social network is built to find the experts for a particular topic. Moreover, the academic social network ranks the experts with a ranking scores depending upon relationships among expert candidates. Finally, we verify the experimental evaluations of our model which improve precision of finding experts compare to baseline methods.

Collaborative Web for Personal Ontology Generation and Visualization for a Social Network

There is a huge amount of indexed web resources are used by millions of users in every country the Earth. It is this huge amount of the content and the integration of information across space and time that leads to the Web containing itself a social network. In this paper, we proposed a frame- work for construction and visualization of a social network by collaborative web approach, in which a consensus method is proposed to identify personal information from different web search spaces (include personal/organizational sites, blogs, publications) of user. A personal ontology is automatically generated from the personal information. Construction of social network is carried out by analyzing relationship among people, contents and organizations found from the different search spaces of each target user.

Learning Using a Self-building Associative Frequent Network

In this paper, we propose a novel framework, called a frequent network, to discover frequent itemsets and potentially frequent patterns by logical inference. We also introduce some new terms and concepts to define the frequent network, and we show the procedure of constructing the frequent network. We then describe a new method LAFN (Learning based on Associative Frequent Network) for mining frequent itemsets and potentially patterns, which are considered as a useful pattern logically over the frequent network. Finally, we present a useful application, classification with these discovered patterns from the proposed framework, and report the results of the experiment to evaluate our classifier on some data sets.