Binary semantic relation extraction from Wikipedia is particularly useful for various NLP and Web applications. Currently frequent pattern mining-based methods and syntactic analysis-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating syntactic analysis on Wikipedia text with redundancy information from the Web, we propose a multi-view learning approach for bootstrapping relationships between entities with the complementary between the Web view and linguistic view. On the one hand, from the linguistic view, linguistic features are generated from linguistic parsing on Wikipedia texts by abstracting away from different surface realizations of semantic relations. On the other hand, Web features are extracted from the Web corpus to provide frequency information for relation extraction. Experimental evaluation on a relational dataset demonstrates that linguistic analysis on Wikipedia texts and Web collective information reveal different aspects of the nature of entity-related semantic relationships. It also shows that our multi-view learning method considerably boosts the performance comparing to learning with only one view of features, with the weaknesses of one view complement the strengths of the other.