8,366 research outputs found
On Recognizing Argumentation Schemes in Formal Text Genres
Argumentation mining research should address the challenge of recognition of argumentation schemes in formal text genres such as scientific articles. This paper argues that identification of argumentation schemes differs from identification of other aspects of discourse such as argumentative zones and coherence relations. Argumentation schemes can be defined at a level of abstraction applicable across the natural sciences. There are useful applications of automatic argumentation scheme recognition. However, it is likely that inference-based techniques will be required.(Note: Due to a publishing error, only the abstract of the paper appeared in the published Seminar Report at http://dx.doi.org/10.4230/DagRep.6.4.80
TechMiner: Extracting Technologies from Academic Publications
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
Requirements Analysis for an Open Research Knowledge Graph
Current science communication has a number of drawbacks and bottlenecks which
have been subject of discussion lately: Among others, the rising number of
published articles makes it nearly impossible to get an overview of the state
of the art in a certain field, or reproducibility is hampered by fixed-length,
document-based publications which normally cannot cover all details of a
research work. Recently, several initiatives have proposed knowledge graphs
(KGs) for organising scientific information as a solution to many of the
current issues. The focus of these proposals is, however, usually restricted to
very specific use cases. In this paper, we aim to transcend this limited
perspective by presenting a comprehensive analysis of requirements for an Open
Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a
scientist, (b) establishing their consequential requirements for a KG-based
system, (c) identifying overlaps and specificities, and their coverage in
current solutions. As a result, we map necessary and desirable requirements for
successful KG-based science communication, derive implications and outline
possible solutions.Comment: Accepted for publishing in 24th International Conference on Theory
and Practice of Digital Libraries, TPDL 202
Requirements Analysis for an Open Research Knowledge Graph
Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions
Domain-independent Extraction of Scientific Concepts from Research Articles
We examine the novel task of domain-independent scientific concept extraction
from abstracts of scholarly articles and present two contributions. First, we
suggest a set of generic scientific concepts that have been identified in a
systematic annotation process. This set of concepts is utilised to annotate a
corpus of scientific abstracts from 10 domains of Science, Technology and
Medicine at the phrasal level in a joint effort with domain experts. The
resulting dataset is used in a set of benchmark experiments to (a) provide
baseline performance for this task, (b) examine the transferability of concepts
between domains. Second, we present two deep learning systems as baselines. In
particular, we propose active learning to deal with different domains in our
task. The experimental results show that (1) a substantial agreement is
achievable by non-experts after consultation with domain experts, (2) the
baseline system achieves a fairly high F1 score, (3) active learning enables us
to nearly halve the amount of required training data.Comment: Accepted for publishing in 42nd European Conference on IR Research,
ECIR 202
Data science for engineering design: State of the art and future directions
Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities
Plague Dot Text:Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)
The design of models that govern diseases in population is commonly built on
information and data gathered from past outbreaks. However, epidemic outbreaks
are never captured in statistical data alone but are communicated by
narratives, supported by empirical observations. Outbreak reports discuss
correlations between populations, locations and the disease to infer insights
into causes, vectors and potential interventions. The problem with these
narratives is usually the lack of consistent structure or strong conventions,
which prohibit their formal analysis in larger corpora. Our interdisciplinary
research investigates more than 100 reports from the third plague pandemic
(1894-1952) evaluating ways of building a corpus to extract and structure this
narrative information through text mining and manual annotation. In this paper
we discuss the progress of our ongoing exploratory project, how we enhance
optical character recognition (OCR) methods to improve text capture, our
approach to structure the narratives and identify relevant entities in the
reports. The structured corpus is made available via Solr enabling search and
analysis across the whole collection for future research dedicated, for
example, to the identification of concepts. We show preliminary visualisations
of the characteristics of causation and differences with respect to gender as a
result of syntactic-category-dependent corpus statistics. Our goal is to
develop structured accounts of some of the most significant concepts that were
used to understand the epidemiology of the third plague pandemic around the
globe. The corpus enables researchers to analyse the reports collectively
allowing for deep insights into the global epidemiological consideration of
plague in the early twentieth century.Comment: Journal of Data Mining & Digital Humanities 202
AGROVOC: The linked data concept hub for food and agriculture
Newly acquired, aggregated and shared data are essential for innovation in food and agriculture to improve the discoverability of research. Since the early 1980′s, the Food and Agriculture Organization of the United Nations (FAO) has coordinated AGROVOC, a valuable tool for data to be classified homogeneously, facilitating interoperability and reuse. AGROVOC is a multilingual and controlled vocabulary designed to cover concepts and terminology under FAO's areas of interest. It is the largest Linked Open Data set about agriculture available for public use and its highest impact is through facilitating the access and visibility of data across domains and languages. This chapter has the aim of describing the current status of one of the most popular thesaurus in all FAO’s areas of interest, and how it has become the Linked Data Concept Hub for food and agriculture, through new procedures put in plac
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