Sherzod Hakimov

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Named Entity Recognition (NER) is a subtask of information extraction and aims to identify atomic entities in text that fall into predefined categories such as person, location, organization, etc. Recent efforts in NER try to extract entities and link them to linked data entities. Linked data is a term used for data resources that are created using semantic(More)
Question answering is the task of answering questions in natural language. Linked Data project and Semantic Web community made it possible for us to query structured knowledge bases like DBpedia and YAGO. Only expert users, however, with the knowledge of RDF and ontology definitions can build correct SPARQL queries for querying knowledge bases formally. In(More)
Question answering over linked data has emerged in the past years as an important topic of research in order to provide natural language access to a growing body of linked open data on the Web. In this paper we focus on analyzing the lexical gap that arises as a challenge for any such question answering system. The lexical gap refers to the mismatch between(More)
Named Entity Disambiguation (NED) is the task of disam-biguating named entities in a natural language text by linking them to their corresponding entities in a knowledge base such as DBpedia, which are already recognized. It is an important step in transforming unstruc-tured text into structured knowledge. Previous work on this task has proven a strong(More)
Statistical Schema Induction can be applied on an RDF dataset to induce domain and range restrictions. We extend an existing approach that derives independent domain and range restrictions to derive coupled domain/range restrictions, which may be beneficial in the context of Natural Language Processing tasks such as Semantic Parsing and Entity(More)
Model-driven approach for web application development is an important topic in software engineering. There are many existing tools to support model-driven engineering for web application development. However, most tools and techniques are complex and not very practical when it comes to real-life usage. Here we present a simple data model-driven approach for(More)
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