2 research outputs found
A Generalized Vector Space Model for Ontology-Based Information Retrieval
Named entities (NE) are objects that are referred to by names such as people,
organizations and locations. Named entities and keywords are important to the
meaning of a document. We propose a generalized vector space model that
combines named entities and keywords. In the model, we take into account
different ontological features of named entities, namely, aliases, classes and
identifiers. Moreover, we use entity classes to represent the latent
information of interrogative words in Wh-queries, which are ignored in
traditional keyword-based searching. We have implemented and tested the
proposed model on a TREC dataset, as presented and discussed in the paper.Comment: 5 pages, in Vietnamese. information retrieval, vector space model,
ontology, named entity, keyword. Accepted by Vietnamese Journal on
Information Technologies and Communication
Semantic Search using Spreading Activation based on Ontology
Currently, the text document retrieval systems have many challenges in
exploring the semantics of queries and documents. Each query implies
information which does not appear in the query but the documents related with
the information are also expected by user. The disadvantage of the previous
spreading activation algorithms could be many irrelevant concepts added to the
query. In this paper, a proposed novel algorithm is only activate and add to
the query named entities which are related with original entities in the query
and explicit relations in the query.Comment: 21 pages, in Vietnames