Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.

In real information systems, there are few static documents. On the other hand, there are too many documents that their content change during the time that could be considered as signals to improve the quality of information retrieval. Unfortunately, considering all these changes could be time-consuming. In this paper, a method has been proposed that the time of analyzing these changes could be reduced significantly. The main idea of this method is choosing a special part of changes that do not make effective changes in the quality of information retrieval; but it could be possible to reduce the analyzing time. To evaluate the proposed method, three different datasets selected from Wikipedia. Different factors have been assessed in term weighting and the effect of the proposed method investigated on these factors. The results of empirical experiments showed that the proposed method could keep the quality of retrieved information in an acceptable rate and reduce the documents' analysis time as a result.

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