This dissertation focuses only on the task of development of the novel algorithms implementing the match operator. Each line of the input consists of two user IDs, followed by the semantic distance, normalized to a scale of The output of S-Match is a set of semantic correspondences called mappings attached with one of the following semantic relations: Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized.
The approach has been evaluated on various real world test cases with encouraging results, thus, proving empirically its benefits.
Semantic matching Semantic matching is a technique used in computer science to identify information which is semantically related. The context data is the intersection node in the shortest path calculation between the two nodes.
The arcs connecting nodes represent relationship between concepts or the entities. That feature is useful if the entities have text fields and you want explicit keyword based match.
University of Trento Abstract: The second idea is that the relations are determined by analyzing the meaning which is codified in the elements and the structures of ontologies. The entities usually appear at the leaf level of the graph.
Especially with the advent of the Web and the consequential information explosion, the problem seems to be emphasized. I will use an example with personal preferences and show how to find the match between two persons, given their preferences in various facets of life. This information can be taken from a linguistic resource like WordNet.
The attributes following the user ID in the input corresponds to rdf: Since there may be a plethora of algorithms for semantic matching, I decided to implement the solution as a plugin framework, so that custom algorithms may be used, provided through a plugin. People face the concrete problem to retrieve, disambiguate and integrate information coming from a wide variety of sources.
In the recent years many of them have been offered. One example is document similarity, where each document is associated with a set of words.
To find the match between two tags, we navigate the RDF model graph starting from the two object literals which are the leaf nodes and we find the shortest possible path between the two leaf nodes. This output can be fed to the MR ToMatches to find the top k matches for a given user.
This a general purpose map reduce used to find similarities between entities with variable number of attributes, but all of the same type.
Solution As input we have an entity pace dating relationship set of associated tags. For example, if one user1 likes a chinese restaurant A and user2 likes chinese restaurant B, a semantic matching based solution will be able to find a match, because they both like chinese cuisine or asian cuisine.
Its use is also being investigated in other areas such as event processing.