Ontology-Based Recommendation of Editorial Products
- URL: http://arxiv.org/abs/2103.13526v1
- Date: Wed, 24 Mar 2021 23:23:53 GMT
- Title: Ontology-Based Recommendation of Editorial Products
- Authors: Thiviyan Thanapalasingam, Francesco Osborne, Aliaksandr Birukou and
Enrico Motta
- Abstract summary: Smart Book Recommender (SBR) supports Springer Nature's Computer Science editorial team in selecting the products to market at specific venues.
SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products.
SBR also allows users to investigate why a certain publication was suggested by the system.
- Score: 7.1717344176500335
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Major academic publishers need to be able to analyse their vast catalogue of
products and select the best items to be marketed in scientific venues. This is
a complex exercise that requires characterising with a high precision the
topics of thousands of books and matching them with the interests of the
relevant communities. In Springer Nature, this task has been traditionally
handled manually by publishing editors. However, the rapid growth in the number
of scientific publications and the dynamic nature of the Computer Science
landscape has made this solution increasingly inefficient. We have addressed
this issue by creating Smart Book Recommender (SBR), an ontology-based
recommender system developed by The Open University (OU) in collaboration with
Springer Nature, which supports their Computer Science editorial team in
selecting the products to market at specific venues. SBR recommends books,
journals, and conference proceedings relevant to a conference by taking
advantage of a semantically enhanced representation of about 27K editorial
products. This is based on the Computer Science Ontology, a very large-scale,
automatically generated taxonomy of research areas. SBR also allows users to
investigate why a certain publication was suggested by the system. It does so
by means of an interactive graph view that displays the topic taxonomy of the
recommended editorial product and compares it with the topic-centric
characterization of the input conference. An evaluation carried out with seven
Springer Nature editors and seven OU researchers has confirmed the
effectiveness of the solution.
Related papers
- An Overview of zbMATH Open Digital Library [3.1017265002574175]
zbMATH Open is a comprehensive repository of mathematical literature.
It serves as a unified quality-assured infrastructure for finding, evaluating, and connecting mathematical information.
arXiv Detail & Related papers (2024-10-09T14:45:43Z) - MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects.
MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years.
We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
arXiv Detail & Related papers (2024-06-10T15:19:09Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is a large language model-powered research idea writing agent.
It generates problems, methods, and experiment designs while iteratively refining them based on scientific literature.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - Harnessing PubMed User Query Logs for Post Hoc Explanations of
Recommended Similar Articles [5.306261813981977]
We build PubCLogs by repurposing 5.6 million pairs of coclicked articles from PubMed's user query logs.
Using our PubCLogs dataset, we train the Highlight Similar Article Title (HSAT), a model designed to select the most relevant parts of the title of a similar article.
HSAT demonstrates strong performance in our empirical evaluations, achieving an F1 score of 91.72 percent on the PubCLogs test set.
arXiv Detail & Related papers (2024-02-05T19:56:27Z) - Towards Controlled Table-to-Text Generation with Scientific Reasoning [46.87189607486007]
We present a new task for generating fluent and logical descriptions that match user preferences over scientific data, aiming to automate scientific document analysis.
We construct a new challenging dataset,SciTab, consisting of table-description pairs extracted from the scientific literature, with highlighted cells and corresponding domain-specific knowledge base.
The results showed that large models struggle to produce accurate content that aligns with user preferences. As the first of its kind, our work should motivate further research in scientific domains.
arXiv Detail & Related papers (2023-12-08T22:57:35Z) - Tag-Aware Document Representation for Research Paper Recommendation [68.8204255655161]
We propose a hybrid approach that leverages deep semantic representation of research papers based on social tags assigned by users.
The proposed model is effective in recommending research papers even when the rating data is very sparse.
arXiv Detail & Related papers (2022-09-08T09:13:07Z) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z) - SciNoBo : A Hierarchical Multi-Label Classifier of Scientific
Publications [0.7305019142196583]
Classifying scientific publications according to Field-of-Science (FoS) is of crucial importance.
We present SciNoBo, a novel classification system of publications to predefined FoS.
In contrast to other works, our system supports assignments of publications to multiple fields by considering their multi-arity potential.
arXiv Detail & Related papers (2022-04-02T15:09:33Z) - Improving Editorial Workflow and Metadata Quality at Springer Nature [7.1717344176500335]
Smart Topic Miner (STM) is an application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science.
STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year.
In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads.
arXiv Detail & Related papers (2021-03-24T23:23:59Z) - What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work.
We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels.
We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
arXiv Detail & Related papers (2020-11-06T02:23:01Z) - A Correspondence Analysis Framework for Author-Conference
Recommendations [2.1055643409860743]
We use Correspondence Analysis (CA) to derive appropriate relationships between the entities in question, such as conferences and papers.
Our models show promising results when compared with existing methods such as content-based filtering, collaborative filtering and hybrid filtering.
arXiv Detail & Related papers (2020-01-08T18:52:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.