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)
Named Entity Disambiguation (NED) is the task of disambiguating 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 unstructured text into structured knowledge. Previous work on this task has proven a strong impact(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)
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)
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)
Web Table Understanding in the context of Knowledge Base Population and the Semantic Web is the task of i) linking the content of tables retrieved from the Web to an RDF knowledge base, ii) of building hypotheses about the tables’ structures and contents, iii) of extracting novel information from these tables, and iv) of adding this new information to a(More)
The task of answering natural language questions over RDF data has received wIde interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i)(More)
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