A Platform for Peer-to-Peer Communications and its Relation to Semantic Web Applications
Use RDF to represent resource and query. Routing table was kind of resource summarization created by leaning through the previous query history. The routing table has different granuality of descriptoin to resource. 
potential to make the search more intelligent and precise. This has been known as one of the benefits we can get from Semantic Web Technology. We use RDF as the syntax of metadata. Additionally, we applied peer-to-peer to a metadata search system to handle the increase in metadata content.
Query Language and database:
Query Routing: Efficiency in searching and data retrieval is a key issue in Peer-to-Peer networks. We have analyzed semantic routing and proposed a new approach based on semantic web technologies and on RDF to achieve better retrieval perform-ance. The main idea standing behind semantic routing is to use the content of queries to drive routing decisions.
On the peer-to-peer metadata search system, query messages will be forwarded across the peer-to-peer network. Efficient query routing is important for avoiding traffic congestion of messages. For this reason, we need to consider efficient query routing based on RDF query language, which will promote the Semantic search on the peer-to-peer network.
Semantic-routing, as the name itself suggests, is a technique where queries are routed according to their content. Each node has to build and maintain a routing table (or knowledge-base) where the most significant data of the queries are associated to specific nodes of the peer-to-peer network. This association expresses, from the node perspective, the ability of these peers to satisfy certain type of queries and it may get stronger or weaker over time. In this framework the node learns from its past “experi-ence” in which “topic” peers in the network are good; in this way it dynamically develops a knowledge related to how the content is distributed in the network. Given thus a query, the node will forward it just to the subset of peers, listed in the knowl-edge-base (KB), that have the best credentials to provide the appropriate responses. The nodes, to which the query has been passed, will in turn check if they can answer and then will apply iteratively the same procedure according to a propagation control. The expectation is that by this selective and “intelligent” routing, the overload of networks and nodes may be avoided without decreasing quality of results.
The approach we are now going to explain shows how RDF schema information can be exploited in the context of semantic routing. In this framework, we mainly focus on KB design explanation that is basically the principal point reflecting RDF.
In the peer-to-peer network, each node is supposed to manage a repository of con-tents metadata. More precisely, given defined RDF schema, every node owns RDF data models where classes and property values of the resources are specified. As first step in KB design, nodes are grouped per classes and subclasses of the RDF schema. For simplicity of explanation let us take, as an example, the RDF schema about cul-tural resources introduced in [13]. The RDF schema modified for our needs is shown in figure 14. According to the schema, KB of each node may keep node-Museum association, node-Artist association and so on. KB also contains an indicator express-ing the ability of these nodes to satisfy query on such particular contents (Museum, Artist, etc.).
This indicator is modified search-by-search depending on the behavior of the nodes whenever queried. Furthermore, the values of the properties listed in the query condi-tion are tracked. According to the number of results a specific node provides, an estimation of the minimum number of its resources having those property values is also computed.
Fig is a KB representation of a fictitious node A that has received results from the peer nodes B and C for given queries. Node B has for instance answered to que-ries whose conditions were related to Museum content and contained a possible combination of ‘location’ and ‘type’ data. These data (‘Tokyo’,’futuristic’, etc.) have been logged. The associated values (10, 3 etc.) express how many resources with these specific ‘location’ and ‘type’ characteristics node B can at least contain. These associated values are based on the number of matches Node B returned to each query. The number of results is used to compute the Minimum number of resources with certain characteristics.
As regards field Quality, it is a general measure of node goodness in the given classes. It expresses the ability of a node to reply on a certain topic on the base of its observed behavior. The formula for the Quality computation is the following: Qj,i+1=Qj,i+rj/Maxz=1,n(rz)
here, Qj,i+1 is the new Quality of the j-th node after answering to the i+1-th query, Qj,i is its current Quality in the KB, rj is the number of matches the j-th node has provided and Maxz=1,n(rz) is the maximum among the number of results provided by all the n queried nodes. As the formula highlights, nodes are compared query by query on the number of results returned and their quality is adjusted with a value that is propor-tional to the number of matches provided and relative to the nodes overall behavior on the specific query.
Node learning strategy is fundamental in semantic routing however there are also some other issues and aspects that are correlated and worth to be stressed. Knowledge base size management is an example. The knowledge base that has been designed and presented in the previous sections keeps detailed and useful information, anyway storing all the property-value pairs specified in the query conditions lead inevitably to a quick size increase.
Another delicate point, that anyway involves the semantic routing approach from a general perspective, is the initialization phase when KB of the node is empty. In this case query flooding or random neighbors selection might be used or, in order to speed up the learning process, neighbors node might exchange content information to ini-tialize their KB. Anyway, in all these scenarios we should consider that peer-to-peer networks are dynamic in term of content and node mobility: nodes can join and leave the system at any time. Node stability may also be considered as an index of goodness that could be reflected in the KB. If a node is not available whenever it is contacted, its quality value could be decreased. Finally, some user feedback, like selecting a node result for downloading, might be also positively reflected in the computation of the quality value.
