Knowledge discovery through directed probabilistic topic models: a survey

Daud, Ali; Li, Juanzi; Zhou, Lizhu; Muhammad, Faqir

doi:10.1007/s11704-009-0062-y

Cited by 136 publications

(62 citation statements)

References 42 publications

(86 reference statements)

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“…To accomplish this unique analysis, our recent works in knowledge management [11] was very helpful and also our ability in context driven algorithms [12] and use of Suffix Array based RDF Indexing using RDQL Queries [13]. Semantic has been used for various other reasons like semantic based dynamic modeling has been carried out to find out the research interests of the authors [14] using topic models [15]. A recent survey has been done covering Significance of work and many approaches for semantic search have been discussed.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Qureshi¹

2013

IJFCC

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Abstract-Semantic Web promises to add metadata to web content to make it understandable to computers. Search is the most widely used activity on web. Semantic search engines have already changed the way we search the data on web. Uren. V, Yuanguilei Uren et al., proposed requirement space pyramid arguing that iterative and exploratory search modes are important to the usability of search engines. It identified the types of semantic queries the users need to make, the issues concerning the search development and the problems intrinsic to semantic search in particular. We have extensively examined the semantic search engines and have done broad survey to analyse the semantic search engines. Comparative analysis of the semantic search engines have been done on the basis of factors cited in the pyramid. The research provides deep understanding of five main semantic search engines based on comparative analysis that may help for future work for semantic web in general and for semantic search engines in particular.Index Terms-Semantic web, semantic search engine, requirement space pyramid. I. INTRODUCTIONSemantic Web [1], the future of web, promises to provide content that will be understandable to computer. Search is the most important and popular applications of the web. Semantic search [2] is one of the most significant applications of semantic web. Recently a number of semantic search engines have been introduced. Semantic Search engine is a tool that produces precise results to user queries by retrieving data semantically [3]. The purpose of this paper is to compare semantic search engines along with their salient characteristic approaches. This paper focuses mainly on five semantic search engines including Hakia, Sensebot, Powerset, Lexxe and Cognition. There are many benchmarks and standards used in every field and computer science is not an exception. Google and other search engines have revolutionized the world with their services but with the idea of semantic search, the rise of semantic search engines has changed the requirements and issues related with search engines. A pyramid scheme has been used as model for discussion to present the requirements, issues and challenges for semantic search engines. The remaining paper has been formatted as follows that related work have discussed in section II. Comparative evaluation of the semantic search engines on the basis of the pyramid has been described in Manuscript received November 8, 2012; revised January 28, 2013 section III. The semantic search engines along with their GUI's have been discussed in section IV while finally the concluding remarks include the issues and challenges of semantic search engines. II. RELATED WORKSSemantic search engine is new field and realtively less work has been done in this domain. Hakia has been compared with google, yahoo and msn in [4] and that of Hakia with dogpile. A relation-based page rank algorithm has been proposed to be used as semantic web search engine. In this work, relevance is measured as probability of finding con...

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Qureshi¹

2013

IJFCC

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“…In the related work on some past surveys on topic modeling have been done that include [1,2,3,4]. In survey paper [1], presents a classification of directed probabilistic topic models and explains a broader view on graphical models.…”

Section: Introductionmentioning

confidence: 99%

“…In survey paper [1], presents a classification of directed probabilistic topic models and explains a broader view on graphical models. It may be considered as an enormous initial point for venture in the field of topic modeling.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

Sharma¹,

Kumar²,

Chand³

2017

IJMECS

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Abstract-Topic modeling techniques have been primarily being used to mine the topics from text corpora. These techniques reveal the hidden thematic structure in a collection of documents and facilitate to build up new ways to browse, search and summarize large archive of texts. A topic is a group of words that frequently occur together. A topic modeling can connect words with similar meanings and make a distinction between uses of words with several meanings. Here we present a survey on journey of topic modeling techniques comprising Latent Dirichlet Allocation (LDA) and non-LDA based techniques and the reason for classify the techniques into LDA and non-LDA is that LDA has ruled the topic modeling techniques since its inception. We have used the three hierarchical classification criteria's for classifying topic models that include LDA and non-LDA based, bag-of-words or sequence-of-words approach and unsupervised or supervised learning for our survey. Purpose of this survey is to explore the topic modeling techniques since Singular Value Decomposition (SVD) topic model to the latest topic models in deep learning. Also, provide the brief summary of current probabilistic topic models as well as a motivation for future research.

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“…Hundreds of LDA extensions have been developed recently to model natural language phenomena and to incorporate additional information about authors, time, labels, categories, citations, links, etc., (Daud et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Classes may refer to text categories (Rubin et al 2012;Zhou et al 2009), authors (Rosen-Zvi et al 2004), time periods (Cui et al 2011;Varadarajan et al 2010), cited documents (Dietz et al 2007), cited authors (Kataria et al 2011), users of documents (Wang and Blei 2011). More information about special models can be found in the survey (Daud et al 2010). All these models fall into several groups and all of them can be easily expressed in terms of ARTM.…”

Section: Correlated Topic Model (Ctm) Was First Introduced Bymentioning

confidence: 99%

Additive regularization of topic models

Vorontsov

Potapenko

2014

Mach Learn

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Probabilistic topic modeling of text collections has been recently developed mainly within the framework of graphical models and Bayesian inference. In this paper we introduce an alternative semi-probabilistic approach, which we call additive regularization of topic models (ARTM). Instead of building a purely probabilistic generative model of text we regularize an ill-posed problem of stochastic matrix factorization by maximizing a weighted sum of the log-likelihood and additional criteria. This approach enables us to combine probabilistic assumptions with linguistic and problem-specific requirements in a single multi-objective topic model. In the theoretical part of the work we derive the regularized EM-algorithm and provide a pool of regularizers, which can be applied together in any combination. We show that many models previously developed within Bayesian framework can be inferred easier within ARTM and in some cases generalized. In the experimental part we show that a combination of sparsing, smoothing, and decorrelation improves several quality measures at once with almost no loss of the likelihood.

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Knowledge discovery through directed probabilistic topic models: a survey

Cited by 136 publications

References 42 publications

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

Additive regularization of topic models

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