FaBiO and CiTO: Ontologies for describing bibliographic resources and citations

Iorio

et al. 2017

PeerJ Computer Science

Self Cite

Section: Grammar and Peculiaritiesmentioning

confidence: 99%

“…Thus we will not consider such vocabularies in the rest of the evaluation. The other common vocabularies are Dublin Core, which appears in 82% of the papers, FOAF (27%) and the SPAR ontologies (Peroni, 2014a), such as FABIO (36%) and CITO (27%) (Peroni & Shotton, 2012). The right panel of Fig.…”

Section: Analysis Of Rdf Annotations In Rash Documentsmentioning

confidence: 99%

Research Articles in Simplified HTML: a Web-first format for HTML-based scholarly articles

Peroni

Iorio

et al. 2017

PeerJ Computer Science

Self Cite

“…Our intention was not to create 'yet another ontology' of the scholarly domain, but to craft a simple scheme for representing our output. For this reason we reused concepts and relationships from a number of well-known scholarly ontologies (including FABIO [22], FOAF 16 , CITO, SKOS, SRO 17 , FRBR 18 ) and introduced new entities and properties only when necessary. The main classes of the TechMiner OWL ontology are Technology, foaf:Person, to represent the researchers associated to the technology, Topic (equivalent to frbr:concept and skos:concept) and Category, representing the category of the technology (e.g., application, format, language).…”

Section: Triple Generationmentioning

confidence: 99%

TechMiner: Extracting Technologies from Academic Publications

Lecture Notes in Computer Science

Ribaupierre

Motta

2016

Abstract. In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture 'standard' scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision.

“…Increasingly, Semantic Web standards are being used to represent this complex data and, as a result, we have seen the emergence of a number of bibliographic repositories in the Linked Data Cloud [1,2,3] and a variety of ontologies to describe scholarly data, including SWRC 1 , BIBO 2 , BiDO 3 , AKT 4 and FABIO 5 . The semantic enhancement of scholarly articles, known as semantic publishing [4], is also becoming an important topic, attracting the interest of major publishers and leading to the formation of new communities (e.g., FORCE11 6 ), workshops (e.g., Linked Science at ISWC, Sepublica at ESWC, SAVE-SD at WWW), and challenges (e.g., the ESWC Semantic Publishing Challenge 7 ).…”

Section: Introductionmentioning

confidence: 99%

“…However, there is growing consensus that semantic technologies can help to overcome this problem by improving our ability to discover, query, explore, annotate and visualize research information on the web [4,5,6,7,8,9,10]. Nonetheless, we still face some important technical challenges before this vision can be realized.…”

Section: Introductionmentioning

confidence: 99%

Klink-2: Integrating Multiple Web Sources to Generate Semantic Topic Networks

Lecture Notes in Computer Science

Motta

2015

Abstract. 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-todate ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarsegrained. 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.