Automatic Classification of Springer Nature Proceedings with Smart Topic Miner

The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas. STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It analyses in real time a set of publications provided by an editor and produces a structured set of topics and a number of Springer Nature Classification tags, which best characterise the given input. In this paper we present the architecture of the system and report on an evaluation study conducted with a team of Springer Nature editors. The results of the evaluation, which showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result we are currently discussing the required next steps to ensure large-scale deployment within the company

Mining semantic relations between research areas

For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard

Klink-2: integrating multiple web sources to generate semantic topic networks

The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i) they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics – e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii) they do not distinguish between different kinds of hierarchical relationships; and iii) they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities (including papers, authors, venues, and technologies) to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords (e.g., “ontology”) and separates them into the appropriate distinct topics – e.g., “ontology/philosophy” vs. “ontology/semantic web”. Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall

Repositorio de conocimiento científico integrado conforme a linked data

Este proyecto presenta una solución para la recuperación, limpieza y estructuración de datos sobre publicaciones de investigadores de la Facultad de Ingeniería de la Univer-sidad Javeriana, concretamente de los departamentos de ingeniería de Sistemas, Indus-trial, Civil y Electrónica, conforme a los principios de Linked Data. Así, este trabajo per-mite conseguir una organización del conocimiento existente sobre dichas publicacio-nes, logrando la integración de datos distribuidos en múltiples y heterogéneos reposi-torios, superando los obstáculos de acceso y gestión actuales de estos recursos y ali-viando los esfuerzos por parte de los usuarios de estos datos, tales como investigadores, analistas y directivos de instituciones relacionados con la investigación científica.This project presents a solution for the recovery, cleaning and structuring of data on publications of researchers from the Faculty of Engineering of the University Javeriana, specifically of the departments of Systems Engineering, Electronic, Civil, and indus-trial, according to the principles of Linked Data. Thus, this project allows to achieve an organization of the existing knowledge about said publications, Linked Data achieving the integration of distributed data in multiple and heterogeneous repositories, overcom-ing the obstacles of access and current management of these resources and alleviating the efforts on the part of the users of these data, such as researchers, analysts and di-rectors of institutions related to scientific research.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí