In this paper, we propose a new merge-based index maintenance strategy for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types of sub-indexes. Inverted lists of the terms contained in the queries that are frequently posed to the Information Retrieval systems are kept in one partition, called frequent-term index and the other inverted lists form another partition, called infrequentterm index. We use a lazy-merge strategy for maintaining infrequent-term sub-indexes, and an active merge strategy for maintaining frequent-term sub-indexes. The sub-indexes are also similarly split into frequent and in-frequent parts. Experimental results show that the proposed method improves both index maintenance performance and query performance compared to the existing merge-based strategies.

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Though many ontologies have a huge number of classes, one cannot find a good number of object properties connecting the classes in most of the cases. Adding object properties makes an ontology richer and more applicable for tasks such as Question Answering. In this context, determining the pair of classes that are most likely to have object properties between them becomes very important. We address the above question in this paper. We propose a simple yet powerful framework based on the popular multi-criteria decision making algorithm TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for identifying relation-gaps. In order to identify such relation-gaps (i.e class-pairs) the proposed system utilizes a mix of criteria derived from the ontology itself and from external sources such as Wikipedia text corpora. Our experimental results show that by means of these carefully chosen criteria and their corresponding weights, the proposed system yields promising results with respect to the precision of the relation-gaps identified.