Hélène de Ribaupierre

User-centric design and evaluation of a semantic annotation model for scientific documents

When performing document search, scientists have specific goals in mind. We conducted interviews with scientists to understand exactly how they were looking for information and working with documents. We found that scientists are generally searching specific discourse elements, not the entire document. Therefore, we created an annotation model that can represent the different types of discourse elements contained in documents. We have implemented this model in the form of an OWL ontology and a semantic indexing and retrieval tool. The experiments we have conducted (in the gender studies field) show that the model is sufficient to represent a large part of the document contents and that it is possible to automatically annote documents according to this model. We also showed that this model can be used to answer specific and complex queries on a corpus of scientific documents.

Extracting discourse elements and annotating scientific documents using the SciAnnotDoc model: a use case in gender documents

When scientists are searching for information, they generally have a precise objective in mind. Instead of looking for documents “about a topic T”, they try to answer specific questions such as finding the definition of a concept, finding results for a particular problem, checking whether an idea has already been tested, or comparing the scientific conclusions of two articles. Answering these precise or complex queries on a corpus of scientific documents requires precise modelling of the full content of the documents. In particular, each document element must be characterised by its discourse type (hypothesis, definition, result, method, etc.). In this paper, we present a scientific document model (SciAnnotDoc ontology), developed from an empirical study conducted with scientists, that models the discourse types. We developed an automated process that analyses documents effectively identifying the discourse types of each element. Using syntactic rules (patterns), we evaluated the process output in terms of precision and recall using a previously annotated corpus in Gender Studies. We chose to annotate documents in Humanities, as these documents are well known to be less formalised than those in “hard science”. The process output has been used to create a SciAnnotDoc representation of the corpus on top of which we built a faceted search interface. Experiments with users show that searches using with this interface clearly outperform standard keyword searches for precise or complex queries.

Combining NLP And Semantics For Mining Software Technologies From Research Publications

The natural language processing (NLP) community has developed a variety of methods for extracting and disambiguating information from research publications. However, they usually focus only on standard research entities such as authors, affiliations, venues, references and keywords. We propose a novel approach, which combines NLP and semantic technologies for generating from the text of research publications an OWL ontology describing software technologies used or introduced by researchers, such as applications, systems, frameworks, programming languages, and formats. The method was tested on a sample of 300 publications in the Semantic Web field, yielding promising results.

Towards gender equality in software engineering: the NSA approach

Multiple studies show that women are under-represented in almost all of fields of Science, Technology, Engineering and Maths (STEM). This gender gap is also present at higher education institutions in both student numbers and academic staff. A range of measures could be implemented to tackle this issue. In this position paper, we outline the measures that the School of Computer Sciences and Informatics of Cardiff University, UK, implemented over the past years, to foster a culture in which women could excel. Then, we discuss the measures that we plan to implement in future in order to increase the number of women both among students and academic staff.