Automatic Classification of Springer Nature Proceedings with Smart Topic Miner

Osborne, Francesco; Salatino, Angelo A.; Birukou, Aliaksandr; Motta, Enrico

doi:10.1007/978-3-319-46547-0_33

Cited by 29 publications

(65 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a reference topic ontology, we used the Computer Science Ontology (CSO), created to represent topics in the Rexplore system [3], which is currently being trialled by Springer Nature to classify proceedings in the field of Computer Science [17], such as the well-known LNCS series. CSO was created by applying the Klink-2 algorithm [18] to the 16 million publications of our Scopus-derived dataset [3].…”

Section: Input Knowledge Basesmentioning

confidence: 99%

“…However, as extensively discussed in previous works [3,17,18], these solutions ignore the rich network of semantic relationships between research topics and are often unable to distinguish research areas from other terms that may be used to annotate publications. Therefore, we exploit the topic ontology by associating to each paper i) all the concepts in CSO whose label is found either in the title, the abstract or the keyword set, as well as ii) all skos:broaderGeneric and iii) all relatedEquivalent areas of the initial set of topics extracted from the scholarly dataset.…”

Section: Generation Of Technology-topic Matricesmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting the Spreading of Technologies in Research Communities

Osborne

Mannocci

Motta

2017

Proceedings of the Knowledge Capture Conference

Self Cite

View full text Add to dashboard Cite

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution.

show abstract

Section: Input Knowledge Basesmentioning

confidence: 99%

Section: Generation Of Technology-topic Matricesmentioning

confidence: 99%

Forecasting the Spreading of Technologies in Research Communities

Osborne

Mannocci

Motta

2017

Proceedings of the Knowledge Capture Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…In scenarios where it already exists a taxonomy of research areas [21], it is also possible to use entity linking techniques [7] for mapping documents to related concepts. For example, the Smart Topic Miner [22], an application used by Springer Nature for annotating proceedings books, maps keywords extracted from papers to the automatically generated Klink-2 Computer Science Ontology [20] with the aim of selecting a comprehensive set of structured keywords. The approaches for topic evolution can be distinguished in discriminative and generative [13].…”

Section: Related Workmentioning

confidence: 99%

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors

Cano-Basave

Osborne

Salatino

2016

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

show abstract

“…This is particularly challenging in the field of Computer Science, where new areas evolve constantly and taxonomies tend to become obsolete very quickly [22]. In the context of the collaboration between The Open University and Springer Nature [2,13], we focused on the issue of supporting the evolution of the Computer Science portion of PMC, concentrating in particular on some branches that had become obsolete.…”

Section: Motivating Scenario: Evolving Springer Nature Market Codesmentioning

confidence: 99%

“…Indeed, ontologies have proved to be very useful in the context of a variety of tasks [1], including the integration of data from different sources, domain reasoning, classification [2], generation of recommendations [3], cluster analysis [4], community detection [5], sentiment analysis, forecasting [6], and others. Naturally, ontologies need to be regularly maintained and need to evolve according to changes in the domain or new requirements from users or applications [7].…”

Section: Introductionmentioning

confidence: 99%

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Osborne¹,

Motta²

2018

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks -e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the realworld scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches.

show abstract

Automatic Classification of Springer Nature Proceedings with Smart Topic Miner

Cited by 29 publications

References 17 publications

Forecasting the Spreading of Technologies in Research Communities

Forecasting the Spreading of Technologies in Research Communities

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors

Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Contact Info

Product

Resources

About