advertising keyword suggestion using relevance-based language models from wikipedia rich articles

when emerging technologies such as search engine marketing (sem) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. keyword suggestion for search engine advertising is an important problem for sponsored search and sem that requires a goldmine repository of knowledge. a recent strategy in this area is bidding on non-obvious yet relevant keywords, which are economically more viable. in this paper, we exploited a modified relevance-based language model for keyword suggestion problem using wikipedia as our knowledge base. huge amounts of clean information in wikipedia allowed us to uncover important relations between concepts and suggest excessive low volume, inexpensive keywords. also, we will show the viability of our approach by comparing its results to recent proposed systems. compared to previous researches, our proposed approach have many advantages, namely, being language independent, being well-grounded, containing expert keywords and being more computationally efficient.