Boosting Item Keyword Search with Spreading Activation

Aswath, Dipti; Ahmed, Syed Toufeeq; D'cunha, James; Davulcu, Hasan

doi:10.1109/wi.2005.44

Cited by 14 publications

(19 citation statements)

References 7 publications

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“…The results of these experiments, shown in Figure 4, have confirmed the validity of our conjecture to estimate τ as the shortest time to near-depletion of a single activation source, as per (11). As expected, Figure 4 shows that setting τ either too low or too high reduces the performance 1 We have adapted the implementation of one-class SVM and SVDD classifiers for the ranking task by sorting the documents according to the raw output of the classifier decision function trained with the default parameters. Other popular classification techniques, such as two-class SVM, were deemed not applicable due to their requirement to have both positive and negative data instances for training.…”

Section: Resultsmentioning

confidence: 70%

“…The latter calculation is performed efficiently by adapting a sparse routine based on Krylov subspace projection method. Finally, we would like to mention a technique that attempts to improve the spreading activation model by taking into account second order term interactions derived from two-level co-occurrence data [1]. The proposed approach differs from this method in the important respect that not only second order but also all the higher order term interactions can be modeled in a unified fashion via a diffusion process.…”

Section: Introductionmentioning

confidence: 99%

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Dual diffusion model of spreading activation for content-based image retrieval

Kosinov

Marchand-Maillet

Kozintsev

et al. 2006

Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval

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This paper introduces a content-based information retrieval method inspired by the ideas of spreading activation models. In response to a given query, the proposed approach computes document ranks as their final activation values obtained upon completion of a diffusion process. This diffusion process, in turn, is dual in the sense that it models the spreading of the query's initial activation simultaneously in two similarity domains: low-level feature-based and highlevel semantic.The formulation of the diffusion process relies on an approximation that makes it possible to compute the final activation as a solution to a linear system of differential equations via a matrix exponential without the need to resort to an iterative simulation. The latter calculation is performed efficiently by adapting a sparse routine based on Krylov subspace projection method.The empirical performance of the described dual diffusion model has been evaluated in terms of precision and recall on the task of content-based digital image retrieval in queryby-example scenario. The obtained experimental results demonstrate that the proposed method achieves better overall performance compared to traditional feature-based approaches. This performance improvement is attained not only when both similarity domains are used, but also when a diffusion model operates only on the feature-based similarities.

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Section: Resultsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Dual diffusion model of spreading activation for content-based image retrieval

Kosinov

Marchand-Maillet

Kozintsev

et al. 2006

Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval

View full text Add to dashboard Cite

show abstract

“…In [1] , the system used a two-level SA network to activate strongly positive and strongly negative matches based on keyword search results. The system also used synonyms of original concepts of a query to activate, and the support vector machine method to train and classify the above data.…”

Section: Systems Using Spreading Activationmentioning

confidence: 99%

“…For example, consider the queries to find documents on the following: (1) "cities that are tourist destinations of Thailand"; (2) "Jewish settlements are built in the east of Jerusalem"; and (3) "works of Ernest Hemingway". In the first query, Chiang Mai and Phuket should be added to the query because they are belongs to class City and are tourist destinations of Thailand.…”

Section: Introductionmentioning

confidence: 99%

Discovering Latent Informaion By Spreading Activation Algorithm for Document Retrieval

Ngo¹

2014

IJAIA

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Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation.

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“…Meanwhile, some works expanded queries by using SA algorithms. In [1], the system used a two-level spreading activation network to activate strongly positive and strongly negative matches based on keyword search results. In [26], given an ontology, weights were assigned to links based on certain properties of the ontology, to measure the strength of the links.…”

Section: Related Workmentioning

confidence: 99%

Combining Named Entities with WordNet and Using Query-Oriented Spreading Activation for Semantic Text Search

Ngo

Cao

2010

2010 IEEE RIVF International Conference on Computing &Amp; Communication Technologies, Research, Innovation, and Vision for The

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Purely keyword-based text search is not satisfactory because named entities and WordNet words are also important elements to define the content of a document or a query in which they occur. Named entities have ontological features, namely, their aliases, classes, and identifiers. Words in WordNet also have ontological features, namely, their synonyms, hypernyms, hyponyms, and senses. Those features of concepts may be hidden from their textual appearance. Besides, there are related concepts that do not appear in a query, but can bring out the meaning of the query if they are added. We propose an ontology-based generalized Vector Space Model to semantic text search. It exploits ontological features of named entities and WordNet words, and develops a query-oriented spreading activation algorithm to expand queries. In addition, it combines and utilizes advantages of different ontologies for semantic annotation and searching. Experiments on a benchmark dataset show that, in terms of the MAP measure, our model is 42.5% better than the purely keyword-based model, and 32.3% and 15.9% respectively better than the ones using only WordNet or named entities.

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Boosting Item Keyword Search with Spreading Activation

Cited by 14 publications

References 7 publications

Dual diffusion model of spreading activation for content-based image retrieval

Dual diffusion model of spreading activation for content-based image retrieval

Discovering Latent Informaion By Spreading Activation Algorithm for Document Retrieval

Combining Named Entities with WordNet and Using Query-Oriented Spreading Activation for Semantic Text Search

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