Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph
of Empirical Research in Requirements Engineering
- URL: http://arxiv.org/abs/2306.16791v1
- Date: Thu, 29 Jun 2023 08:55:35 GMT
- Title: Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph
of Empirical Research in Requirements Engineering
- Authors: Oliver Karras, Felix Wernlein, Jil Kl\"under and S\"oren Auer
- Abstract summary: Empirical research in requirements engineering (RE) is constantly evolving.
The underlying problem is the unavailability of data from earlier works.
We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE.
- Score: 0.3277163122167433
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: [Background.] Empirical research in requirements engineering (RE) is a
constantly evolving topic, with a growing number of publications. Several
papers address this topic using literature reviews to provide a snapshot of its
"current" state and evolution. However, these papers have never built on or
updated earlier ones, resulting in overlap and redundancy. The underlying
problem is the unavailability of data from earlier works. Researchers need
technical infrastructures to conduct sustainable literature reviews. [Aims.] We
examine the use of the Open Research Knowledge Graph (ORKG) as such an
infrastructure to build and publish an initial Knowledge Graph of Empirical
research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal
is to continuously maintain KG-EmpiRE with the research community to synthesize
a comprehensive, up-to-date, and long-term available overview of the state and
evolution of empirical research in RE. [Method.] We conduct a literature review
using the ORKG to build and publish KG-EmpiRE which we evaluate against
competency questions derived from a published vision of empirical research in
software (requirements) engineering for 2020 - 2025. [Results.] From 570 papers
of the IEEE International Requirements Engineering Conference (2000 - 2022), we
extract and analyze data on the reported empirical research and answer 16 out
of 77 competency questions. These answers show a positive development towards
the vision, but also the need for future improvements. [Conclusions.] The ORKG
is a ready-to-use and advanced infrastructure to organize data from literature
reviews as knowledge graphs. The resulting knowledge graphs make the data
openly available and maintainable by research communities, enabling sustainable
literature reviews.
Related papers
- KG-EmpiRE: A Community-Maintainable Knowledge Graph for a Sustainable Literature Review on the State and Evolution of Empirical Research in Requirements Engineering [0.43512163406552]
KG-EmpiRE is a Knowledge Graph (KG) of empirical research in requirements engineering (RE) based on scientific data extracted from 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022)
KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles.
Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers.
arXiv Detail & Related papers (2024-05-14T06:42:47Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - An approach based on Open Research Knowledge Graph for Knowledge
Acquisition from scientific papers [4.8951183832371]
Open Research Knowledge Graph (ORKG) is a computer-assisted tool to organize key-insights extracted from research papers.
It is currently used to document "food information engineering", "Tabular data to Knowledge Graph Matching" and "Question Answering" research problems and "Neuro-symbolic AI" domain.
arXiv Detail & Related papers (2023-08-23T20:05:42Z) - The Semantic Scholar Open Data Platform [79.4493235243312]
Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature.
We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction.
The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.
arXiv Detail & Related papers (2023-01-24T17:13:08Z) - Artificial Intelligence in Concrete Materials: A Scientometric View [77.34726150561087]
This chapter aims to uncover the main research interests and knowledge structure of the existing literature on AI for concrete materials.
To begin with, a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science.
Scientometric tools such as keyword co-occurrence analysis and documentation co-citation analysis were adopted to quantify features and characteristics of the research field.
arXiv Detail & Related papers (2022-09-17T18:24:56Z) - Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature.
We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
arXiv Detail & Related papers (2022-07-18T11:38:32Z) - A Systematic Literature Review of Empiricism and Norms of Reporting in
Computing Education Research Literature [4.339510167603376]
The goal of this study is to characterize the reporting of empiricism in Computing Education Research (CER) literature.
We conducted an SLR of 427 papers published during 2014 and 2015 in five CER venues.
Over 80% of papers had some form of empirical evaluation.
arXiv Detail & Related papers (2021-07-02T16:37:29Z) - Studying the characteristics of scientific communities using
individual-level bibliometrics: the case of Big Data research [2.208242292882514]
We study the academic age, production, and research focus of the community of authors active in Big Data research.
Results show that the academic realm of "Big Data" is a growing topic with an expanding community of authors.
arXiv Detail & Related papers (2021-06-10T08:17:09Z) - CitationIE: Leveraging the Citation Graph for Scientific Information
Extraction [89.33938657493765]
We use the citation graph of referential links between citing and cited papers.
We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
arXiv Detail & Related papers (2021-06-03T03:00:12Z) - Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
We redefine the task of scientific papers summarization by utilizing their citation graph.
We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains.
Our model can achieve competitive performance when compared with the pretrained models.
arXiv Detail & Related papers (2021-04-07T11:13:35Z) - Generating Knowledge Graphs by Employing Natural Language Processing and
Machine Learning Techniques within the Scholarly Domain [1.9004296236396943]
We present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications.
Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools.
We generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain.
arXiv Detail & Related papers (2020-10-28T08:31:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.