Building a Conference Recommender System Based on SciGraph and WikiCFP

Iana, Andreea; Naeser, Philipp; Birukou, Aliaksandr; Hertling, Sven; Paulheim, Heiko

doi:10.1007/978-3-030-33220-4_9

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…In particular, we use tf-idf vectors [15] as well as the aforementioned NMF topics as features. This being said, we emphasize in our investigation the logistic regression approach, since this has already been successful in previous work [6,4]. Additionally, logistic regression is a robust procedure that does not require a large number of parameter values to be identified.…”

Section: Classification Methodsmentioning

confidence: 90%

“…Recommendations are only available for items in a pre-computed database. Iana et al [4] develop a conference recommender based on SciGraph, a taxonomy by Springer Nature. The authors experiment with author information, paper abstracts and paper keywords.…”

Section: Related Workmentioning

confidence: 99%

“…This count is divided by the total size of the test set. Iana et al [4] used the term recall@k, which, due to the single ground truth label, calculates the same. MRR is calculated from a test set T ⊂ C as follows: Let rank(x, C) ∈ N >0 be the rank of the true label of publication x ∈ T in a ranking generated by algorithm C. Then MRR is the average of the inverse ranks, i.e.,…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Towards Explainable Scientific Venue Recommendations

Schäfermeier,

Stumme,

Hanika

2021

Preprint

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Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.

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Section: Classification Methodsmentioning

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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Towards Explainable Scientific Venue Recommendations

Schäfermeier,

Stumme,

Hanika

2021

Preprint

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“…None of the provided services explain why a specific venue was chosen. Only very recently, recommending conferences based on authors, abstracts, and keywords became a new research area (Iana et al, 2019). However, the authors approach a more general setting that includes conferences from a wide variety of fields.…”

Section: Related Workmentioning

confidence: 99%

“…We also compare our method to one of the approaches outlined in (Iana et al, 2019). For better comparison, we use their best performing approach (according to Recall@10) that does not incorporate any third-party information (called "Ensemble TF-IDF & word2vec plus CNN (10)").…”

Section: Iana Et Almentioning

confidence: 99%

Where to Submit? Helping Researchers to Choose the Right Venue

Kobs¹,

Koopmann²,

Zehe³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Whenever researchers write a paper, the same question occurs: "Where to submit?" In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83 % for AI papers and 95 % in the field of medicine. It is open source 1 and available for testing on https://wheretosubmit.ml.

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Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature

Angioni

Salatino

Osborne

et al. 2022

The Semantic Web – ISWC 2022

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Research publishing companies need to constantly monitor and compare scientific journals and conferences in order to inform critical business and editorial decisions. Semantic Web and Knowledge Graph technologies are natural solutions since they allow these companies to integrate, represent, and analyse a large quantity of information from heterogeneous sources. In this paper, we present the AIDA Dashboard 2.0, an innovative system developed in collaboration with Springer Nature to analyse and compare scientific venues, now also available to the public. This tool builds on a knowledge graph which includes over 1.5B RDF triples and was produced by integrating information about 25M research articles from Microsoft Academic Graph, Dimensions, DBpedia, GRID, CSO, and INDUSO. It can produce sophisticated analytics and rankings that are not available in alternative systems. We discuss the advantages of this solution for the Springer Nature editorial process and present a user study involving 5 editors and 5 researchers, which yielded excellent results in terms of quality of the analytics and usability.

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Building a Conference Recommender System Based on SciGraph and WikiCFP

Cited by 13 publications

References 15 publications

Towards Explainable Scientific Venue Recommendations

Towards Explainable Scientific Venue Recommendations

Where to Submit? Helping Researchers to Choose the Right Venue

Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature

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