Using semantic techniques to access web data

Trillo, Raquel; Po, Laura; Ilarri, Sergio; Bergamaschi, Sonia; Mena, Eduardo

doi:10.1016/j.is.2010.06.008

Cited by 23 publications

(22 citation statements)

References 35 publications

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“…[3] utilizes movie features that have been extracted from IMDB and [7] shows how the similarity of the features can be combined to define the distance of two movies, but none of these works involves the analysis of movie plots. Many efforts in other research areas, like schema matching and ontology matching, developed keyword similarity techniques exploiting lexical resources [27].…”

Section: Related Workmentioning

confidence: 99%

A Non-intrusive Movie Recommendation System

Farinella¹,

Bergamaschi

2012

On the Move to Meaningful Internet Systems: OTM 2012

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Abstract. Several recommendation systems have been developed to support the user in choosing an interesting movie from multimedia repositories. The widely utilized collaborative-filtering systems focus on the analysis of user profiles or user ratings of the items. However, these systems decrease their performance at the start-up phase and due to privacy issues, when a user hides most of his personal data. On the other hand, content-based recommendation systems compare movie features to suggest similar multimedia contents; these systems are based on less invasive observations, however they find some difficulties to supply tailored suggestions. In this paper, we propose a plot-based recommendation system, which is based upon an evaluation of similarity among the plot of a video that was watched by the user and a large amount of plots that is stored in a movie database. Since it is independent from the number of user ratings, it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. We experimented different methodologies to compare natural language descriptions of movies (plots) and evaluated the Latent Semantic Analysis (LSA) to be the superior one in supporting the selection of similar plots. In order to increase the efficiency of LSA, different models have been experimented and in the end, a recommendation system that is able to compare about two hundred thousands movie plots in less than a minute has been developed.

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Section: Related Workmentioning

confidence: 99%

A Non-intrusive Movie Recommendation System

Farinella¹,

Bergamaschi

2012

On the Move to Meaningful Internet Systems: OTM 2012

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“…Extension to the current approach, Trillo et al [11] proposes categorization or clustering method which turns up with a semantic technique to group the output of searching keywords into different categories. They use online ontologies to define the possible categories.…”

Section: Clustering/categorizationmentioning

confidence: 99%

“…Semantic Web is chosen based on certain advantages stated in the previous section. Ensuring results obtained are more reliable, method in [11] is used with modification in user profiling concept.…”

Section: Semantic Web Search Constructionmentioning

confidence: 99%

“…Based on [11], the approach of extracting online ontology from the web is applied. The results are then categorized to facilitate users.…”

Section: Proposed Modelmentioning

confidence: 99%

“…While ontologies are capable in giving good outcome, researchers are trying to enhance searching method using clustering technique [11] and user profiling/personalization [12,13,14]. Although previous researches are capable to give good results, we are motivated to improve the output.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Web Search Engine Using Ontology, Clustering and Personalization Techniques

Abdullah

Ibrahim

2012

Computational Science and Its Applications – ICCSA 2012

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Abstract. Data accuracy and reliability have been a serious issue in the vast emergence of information on the web. Advanced web searching has assisted in knowledge retrieving. However, most knowledge on the Web is presented in natural-language text that understandable by human but difficult for computers to interpret. Therefore, Semantic Web approach is widely used to give more reliable application. This paper presents a framework in enhancing knowledge retrieval processes using Semantic Web technologies. Instead of using ontology and categorization alone, we are injecting personalization concept from Relational Database (RDB) to ensure more reliable data are obtained. The proposed framework is discussed in details. A case study is presented to see the viability of the proposed framework in retrieving the meaningful information.

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Indexing byLatentDirichletAllocation and anEnsembleModel

Wang

Lee

Choi

2015

Asso for Info Science & Tech

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The contribution of this paper is two-fold. First, we present Indexing by Latent Dirichlet Allocation (LDI), an automatic document indexing method. The probability distributions in LDI utilize those in Latent Dirichlet Allocation (LDA), a generative topic model that has been previously used in applications for document retrieval tasks. However, the ad hoc applications, or their variants with smoothing techniques as prompted by previous studies in LDA-based language modeling, result in unsatisfactory performance as the document representations do not accurately reflect concept space. To improve 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. The EnM combines basis indexing models by assigning different weights and attempts to uncover the optimal weights to maximize the Mean Average Precision (MAP). To solve the optimization problem, we propose an algorithm, EnM.B, 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.

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Using semantic techniques to access web data

Cited by 23 publications

References 35 publications

A Non-intrusive Movie Recommendation System

A Non-intrusive Movie Recommendation System

Semantic Web Search Engine Using Ontology, Clustering and Personalization Techniques

Indexing byLatentDirichletAllocation and anEnsembleModel

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