SPARQL is a standard query language to facilitate access to structured data in RDF (Resource Desc... more SPARQL is a standard query language to facilitate access to structured data in RDF (Resource Description Framework) format. Researchers have introduced several storage layouts to efficiently store RDF data and algorithms to retrieve information from the RDF data repositories. Although those storage layouts are specialized in the repository, they are mainly originated from the conventional join algorithms for SQL (Structured Query Language) such as merge join, hash join, and Nested-loop join, which are not efficient in operation and memory usage. Thus, some query optimization techniques have been developed to reduce the cost, but they do not show cost-effective results for sequential join process. To overcome this issue, we propose a set of specialized algorithms to execute cycle-free SPARQL and storage layout. Our experimental results tested on LUBM show that the proposed approach is considerably efficient for cycle-free queries.
The contribution of this article is twofold. First, we
present Indexing by latent Dirichlet alloc... more The contribution of this article is twofold. First, we present Indexing by latent Dirichlet allocation (LDI), an automatic document indexing method. Many ad hoc applications, or their variants with smoothing techniques suggested in LDA-based language modeling, can result in unsatisfactory performance as the document representations do not accurately reflect concept space. To improve document retrieval performance, we introduce a new definition of document probability vectors in the context of LDA and present a novel scheme for automatic document indexing based on LDA. Second, we propose an Ensemble Model (EnM) for document retrieval. EnM combines basic indexing models by assigning different weights and attempts to uncover the optimal weights to maximize the mean average precision. To solve the optimization problem, we propose an algorithm, which is derived based on the boosting method. The results of our computational experiments on benchmark data sets indicate that both the proposed approaches are viable options for document retrieval.
An automatic document indexing method with a probabilistic concept search is presented. The propo... more An automatic document indexing method with a probabilistic concept search is presented. The proposed method utilizes Latent Dirichlet Allocation (LDA), a generative model for document modeling and classification. Ad hoc applications of LDA to document indexing, or their variants with smoothing techniques as prompted by previous studies in LDA-based language modeling, would result in disadvantaged empirical approaches. They could result in unsatisfactory performance as the terms in documents may not properly reflect concept space.
In this study, we introduce a new definition of document
probability vectors in the context of LDA and present a scheme for automatic document indexing based on it. The results of our computational experiment on a benchmark data set indicate that the proposed approach is a viable option to use in the document indexing. A small illustrative example is also included.
SPARQL is a standard query language to facilitate access to structured data in RDF (Resource Desc... more SPARQL is a standard query language to facilitate access to structured data in RDF (Resource Description Framework) format. Researchers have introduced several storage layouts to efficiently store RDF data and algorithms to retrieve information from the RDF data repositories. Although those storage layouts are specialized in the repository, they are mainly originated from the conventional join algorithms for SQL (Structured Query Language) such as merge join, hash join, and Nested-loop join, which are not efficient in operation and memory usage. Thus, some query optimization techniques have been developed to reduce the cost, but they do not show cost-effective results for sequential join process. To overcome this issue, we propose a set of specialized algorithms to execute cycle-free SPARQL and storage layout. Our experimental results tested on LUBM show that the proposed approach is considerably efficient for cycle-free queries.
The contribution of this article is twofold. First, we
present Indexing by latent Dirichlet alloc... more The contribution of this article is twofold. First, we present Indexing by latent Dirichlet allocation (LDI), an automatic document indexing method. Many ad hoc applications, or their variants with smoothing techniques suggested in LDA-based language modeling, can result in unsatisfactory performance as the document representations do not accurately reflect concept space. To improve document retrieval performance, we introduce a new definition of document probability vectors in the context of LDA and present a novel scheme for automatic document indexing based on LDA. Second, we propose an Ensemble Model (EnM) for document retrieval. EnM combines basic indexing models by assigning different weights and attempts to uncover the optimal weights to maximize the mean average precision. To solve the optimization problem, we propose an algorithm, which is derived based on the boosting method. The results of our computational experiments on benchmark data sets indicate that both the proposed approaches are viable options for document retrieval.
An automatic document indexing method with a probabilistic concept search is presented. The propo... more An automatic document indexing method with a probabilistic concept search is presented. The proposed method utilizes Latent Dirichlet Allocation (LDA), a generative model for document modeling and classification. Ad hoc applications of LDA to document indexing, or their variants with smoothing techniques as prompted by previous studies in LDA-based language modeling, would result in disadvantaged empirical approaches. They could result in unsatisfactory performance as the terms in documents may not properly reflect concept space.
In this study, we introduce a new definition of document
probability vectors in the context of LDA and present a scheme for automatic document indexing based on it. The results of our computational experiment on a benchmark data set indicate that the proposed approach is a viable option to use in the document indexing. A small illustrative example is also included.
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Papers by Jaesung Lee
present Indexing by latent Dirichlet allocation (LDI), an
automatic document indexing method. Many ad hoc
applications, or their variants with smoothing techniques
suggested in LDA-based language modeling, can
result in unsatisfactory performance as the document
representations do not accurately reflect concept space.
To improve document retrieval performance, we introduce
a new definition of document probability vectors in
the context of LDA and present a novel scheme for
automatic document indexing based on LDA. Second,
we propose an Ensemble Model (EnM) for document
retrieval. EnM combines basic indexing models by
assigning different weights and attempts to uncover the
optimal weights to maximize the mean average precision.
To solve the optimization problem, we propose an
algorithm, which is derived based on the boosting
method. The results of our computational experiments
on benchmark data sets indicate that both the proposed
approaches are viable options for document retrieval.
In this study, we introduce a new definition of document
probability vectors in the context of LDA and present a scheme for automatic document indexing based on it. The results of our computational experiment on a benchmark data set indicate that the proposed approach is a viable option to use in the document indexing. A small illustrative example is also included.
present Indexing by latent Dirichlet allocation (LDI), an
automatic document indexing method. Many ad hoc
applications, or their variants with smoothing techniques
suggested in LDA-based language modeling, can
result in unsatisfactory performance as the document
representations do not accurately reflect concept space.
To improve document retrieval performance, we introduce
a new definition of document probability vectors in
the context of LDA and present a novel scheme for
automatic document indexing based on LDA. Second,
we propose an Ensemble Model (EnM) for document
retrieval. EnM combines basic indexing models by
assigning different weights and attempts to uncover the
optimal weights to maximize the mean average precision.
To solve the optimization problem, we propose an
algorithm, which is derived based on the boosting
method. The results of our computational experiments
on benchmark data sets indicate that both the proposed
approaches are viable options for document retrieval.
In this study, we introduce a new definition of document
probability vectors in the context of LDA and present a scheme for automatic document indexing based on it. The results of our computational experiment on a benchmark data set indicate that the proposed approach is a viable option to use in the document indexing. A small illustrative example is also included.