There is a growing interest in supporting semantic search on knowledge bases such asDBpedia, YaGo,FreeBase, and other similar systems, which play a key role in many semantic web applications. Although the standard RDF format is often used by these systems,the sharing of their knowledge is hampered by the fact that various synonyms are frequently used to denote the same entity or attribute---actually, even an individual system may use alternative synonyms in different contexts, and polynyms also represent a frequent problem. Recognizing such synonyms and polynyms is critical for improving the precision and recall of semantic search. Most of previous efforts in this area have focused on entity synonym recognition, whereas attribute synonyms were neglected, and so was the use of context to select the appropriate synonym. For instance, the attribute `birthdate' can be a synonym for `born' when it is used with a value of type `date'; but if `born' comes with values which indicate places, then `birthplace' should be considered as its synonym. Thus, the context is critical to find more specific and accurate synonyms. In this paper, we propose new techniques to generate context-aware synonyms for the entities and attributes that we are using to reconcile knowledge extracted from various sources. To this end, we propose the Context-aware Synonym Suggestion System (CS3) which learns synonyms from text by using our NLP-based text mining framework, called SemScape, and also from existing evidence in the current knowledge bases. Using $CS^3$ and our previously proposed knowledge extraction systemIBminer, we integrate some of the publicly available knowledge bases into one of the superior quality and coverage, called IKBstore.
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