Detecting Knowledge Innovation through Automatic Topic Labeling on Scholar Data

Nolasco, Diogo; Oliveira, Jonice

doi:10.1109/hicss.2016.51

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…Ortak atıfa dayalı pennant erişim algoritmasının derlemdeki en ilgili makalelere eriştiği yönündeki bulgular, atıfların önemli ve etkili çalışmaları bulmada çok önemli rol oynadığının saptandığı daha önceki araştırmaların bulgularını desteklemektedir (Huang ve diğerleri, 2016;Huang ve diğerleri, 2018;Li ve diğerleri, 2017;Zhou ve diğerleri, 2017). Öte yandan, pennant erişim algoritması ile elde edilen bağlamların otomatik olarak etiketlenmesi, görselleştirmelerin kapsamlılığını büyük ölçüde artırabilir (Chang ve diğerleri, 2009;Nolasco ve Oliveira, 2016;Rüdiger ve diğerleri, 2021). Görselleştirme için pennant erişim diyagramlarından yararlanılması kullanıcıların literatürü izlemelerini kolaylaştırılabilir (Akbulut ve diğerleri, 2020).…”

Section: şEkil 13unclassified

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Akbulut

Tonta

2022

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Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with the “pennant retrieval” method using citation data. Data and Method: We applied this method to the iSearch corpus consisting of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used the pennant retrieval to fuse the citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. The Maximal Marginal Relevance (MMR) algorithm was used to evaluate the retrieval performance of the proposed method by finding out its effect on relevance ranking algorithms that we used. Findings: Findings suggest that the terms used in different contexts in the papers might sometimes be overlooked by the topic modeling algorithm. Yet, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby further enriching the results with diverse (interdisciplinary) papers of higher relevance. Moreover, results can easily be re-ranked and personalized. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can in time be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using pennant retrieval techniques based on citation data.

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Section: şEkil 13unclassified

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Akbulut

Tonta

2022

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“…Additional visualization techniques may enhance usability (Chaney & Blei, 2012;Chuang et al, 2012;Sievert & Shirley, 2014). Automatic labelling of the derived thematic contexts may greatly enhance the comprehensiveness of the visualizations (Chang et al, 2009;Chuang et al, 2014;Hindle et al, 2013;Mei et al, 2007;Nolasco & Oliveira, 2016). Another useful extension would be to track changes of thematic impact over time, similar to what is already possible in the science mapping tools.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

The explanatory power of citations: a new approach to unpacking impact in science

2021

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Citation analysis has been applied to map the landscape of scientific disciplines and to assess the impact of publications. However, it is limited in that it assumes all citations to be of equal weight. Doing away with this assumption could make such studies even more insightful. Current developments in this regard focus on the evaluation of the syntactic and semantic qualities of the text that surrounds citations. Still lacking, however, are computational techniques to unpack the thematic context in which citations appear. It is against this backdrop that we propose a text clustering approach to derive contextual aspects of individual citations and the relationship between cited and citing work in an automated and scalable fashion. The method reveals a focal publication’s absorption and use within the scientific community. It can also facilitate impact assessments at all levels. In addition to analyzing individual publications, the method can also be extended to creating impact profiles for authors, institutions, disciplines, and regions. We illustrate our results based on a large corpus of full-text articles from the field of Information systems (IS) with the help of exemplary visualizations. In addition, we provide a case study, the scientific impact of the Technology acceptance model. This way, we not only show the usefulness of our method in comparison to existing techniques but also enhance the understanding of the field by providing an in-depth analysis of the absorption of a key IS theoretical base.

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“…For topic labeling, Nalasco et al proposed a technique of automatic labeling using a new candidate selection algorithm and three scoring methods [19]. Bhatia et al proposed a neural embedding approach that involves automatic topic labeling using Wikipedia article titles [20].…”

Section: Related Workmentioning

confidence: 99%

Text-based Causality Modeling with a Conceptual Label in a Hierarchical Topic Structure Using Bayesian Rose Trees

Ogawa

Saga

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper describes a method for constructing a causality model from review text data. Review text data include the evaluation factors of rating, and causality model extraction from text data is important for understanding the evaluation factors and their relationships. Several methods are available for extracting causality models by using a topic model. In particular, the method based on hierarchical latent Dirichlet allocation is useful for hierarchically comprehending causality structure. However, the depth of each topic in a hierarchical structure is forcefully pruned even if granularities differ for each topic. Thus, interpreting a hierarchical topic structure is difficult. To solve these problems, we construct a hierarchical topic structure with different depths by using Bayesian rose trees. Furthermore, we use conceptual labeling to add explicit semantics for each topic for interpretation. An experiment confirms that this model is accurate and interpretable using actual data.

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Detecting Knowledge Innovation through Automatic Topic Labeling on Scholar Data

Cited by 13 publications

References 10 publications

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

The explanatory power of citations: a new approach to unpacking impact in science

Text-based Causality Modeling with a Conceptual Label in a Hierarchical Topic Structure Using Bayesian Rose Trees

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