We tackle the problem of semantic heterogeneity in the context of agent communication and argue that solutions based solely on ontologies and ontology matching do not capture adequately the richness of semantics as it arises in dynamic and open multiagent systems. Current solutions to the semantic heterogeneity problem in distributed systems usually do not address the contextual nuances of the ...

Internet Image search is a day to day activity performed by user. User enters a keyword in a search engine like Google, Yahoo, Bing etc for retrieval of keyword related images, where millions of images are retrieved through search engines. The problem with a keyword search is that keywords entered by user are very short and ambiguous, hence images which are retrieved are of different categories...

We tackle the problem of semantic heterogeneity in the context of agent communication and argue that solutions based solely on ontologies and ontology matching do not capture adequately the richness of semantics as it arises in dynamic and open multiagent systems. Current solutions to the semantic heterogeneity problem in distributed systems usually do not address the contextual nuances of the ...

We tackle the problem of semantic heterogeneity in the context of agent communication and argue that solutions based solely on ontologies and ontology matching do not capture adequately the richness of semantics as it arises in dynamic and open multiagent systems. Current solutions to the semantic heterogeneity problem in distributed systems usually do not address the contextual nuances of the ...

The class of DHT-based P2P systems like Chord, Pastry, Tapestry, Kademlia greatly improve over unstructured P2P systems like Gnutella and Kazaa by providing (1) Scalable and efficient O(log(n)) lookup and routing for any document (2) Good load balancing properties for very large number of keys or documents. However, to lookup a document, its complete initial identifier must be known to compute ...

This paper describes text mining technique for automatically extracting association rules from collections of textual documents. The technique called, Extracting Association Rules from Text (EART). It depends on keyword features for discover association rules amongst keywords labeling the documents. In this work, the EART system ignores the order in which the words occur, but instead focusing o...

With the advent of time, the number of images being captured and shared online has grown exponentially. The images which are captured are later accessed for the purpose of searching, classification and retrieval operation. Hence these images must be labelled with appropriate words, phrases or keywords so that the requisite operation can be performed efficiently. Automatic Image Tagging is such ...

Keyword search is by far the most popular technique for searching linked data on the web. The simplicity of keyword search on data graphs comes with at least two drawbacks: difficulty in identifying results relevant to the user intent among an overwhelming number of candidates and performance scalability problems. In this paper, we claim that result ranking and top-k processing which adapt sche...

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. One of the key factors influencing team performance is the personality of team members. In this paper, we introduce a new algorithm to partition a group of individuals into ge...

With the ever increasing number of web services, discovering the appropriate web service requested by users has become a vital yet challenging task. The aim of this project is to provide an efficient search engine that can retrieve the most relevant web services in a short time. The proposed search engine WSSE is based on the probabilistic topic modeling and clustering techniques that are integ...