Keyword search is an new paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multilevel scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets, and sub graphs that connect these elements. In the keyword searching, we mainly follow two approaches. They are schema-based approaches and schema-agnostic approaches. Schema-based approaches are implemented on top of off-the-shelf databases. A keyword is processed by mapping keywords to the elements of the databases, called keyword elements. Then, using the schema, valid join sequences are derived and are employed to join the computed keyword elements to form the candidate-networks that represent the possible results to the keyword query. Schema agnostic approaches operate directly on the data. By exploring the underlying graphs the structured results are computed in these approaches. Keywords and elements which are connected are represented using Steiner trees/graphs. The goal of this approach is to find structures in the Steiner trees. For the query “Stanley Robert Award” for instance, a Steiner graph is the path between uni1andprize Various kinds of algorithms have been proposed for the efficient exploration of keyword search results over data graphs, which might be very large. Examples are bidirectional search and dynamic programming.