338 research outputs found
Automated and Improved Search Query Effectiveness Design for Systematic Literature Reviews
This research explores and investigates strategies towards automation of the systematic literature review (SLR) process. SLR is a valuable research method that follows a comprehensive, transparent, and reproducible research methodology. SLRs are at the heart of evidence-based research in various research domains, from healthcare to software engineering.
They allow researchers to systematically collect and integrate empirical evidence in response to a focused research question, setting the foundation for future research. SLRs are also beneficial to researchers in learning about the state of the art of research and enriching their knowledge of a topic of research. Given their demonstrated value, SLRs are becoming an increasingly popular type of publication in different disciplines. Despite the valuable contributions of SLRs to science, performing timely, reliable, comprehensive, and unbiased SLRs is a challenging endeavour. With the rapid growth in primary research published every year, SLRs might fail to provide complete coverage of existing evidence and even end up being outdated by the time of publication.
These challenges have sparked motivation and discussion in research communities to explore automation techniques to support the SLR process. In investigating automatic methods for supporting the systematic review process, this thesis develops three main areas. First, by conducting a systematic literature review, we found the state of the art of automation techniques that are employed to facilitate the systematic review process. Then, in the second study, we identified the real challenges researchers face when conducting SLRs, through an empirical study. Moreover, we distinguished solutions that help researchers to overcome these challenges. We also identified the researchers' concerns regarding adopting automation techniques in SLR practice. Finally, in the third study, we leveraged the findings of our previous studies to investigate a solution to facilitate the SLR search process. We evaluated our proposed method by running some experiments
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Recent research in representation learning utilizes large databases of
proteins or molecules to acquire knowledge of drug and protein structures
through unsupervised learning techniques. These pre-trained representations
have proven to significantly enhance the accuracy of subsequent tasks, such as
predicting the affinity between drugs and target proteins. In this study, we
demonstrate that by incorporating knowledge graphs from diverse sources and
modalities into the sequences or SMILES representation, we can further enrich
the representation and achieve state-of-the-art results on established
benchmark datasets. We provide preprocessed and integrated data obtained from 7
public sources, which encompass over 30M triples. Additionally, we make
available the pre-trained models based on this data, along with the reported
outcomes of their performance on three widely-used benchmark datasets for
drug-target binding affinity prediction found in the Therapeutic Data Commons
(TDC) benchmarks. Additionally, we make the source code for training models on
benchmark datasets publicly available. Our objective in releasing these
pre-trained models, accompanied by clean data for model pretraining and
benchmark results, is to encourage research in knowledge-enhanced
representation learning
Charting differentially methylated regions in cancer with Rocker-meth
Matteo Benelli et al. present Rocker-meth, a new Hidden Markov Model (HMM)-based method, to robustly identify differentially methylated regions (DMRs). They use Rocker-meth to analyse more than 6000 methylation profiles across 14 cancer types, providing a catalog of tumor-specific and shared DMRs
requirements and use cases
In this report, we introduce our initial vision of the Corporate Semantic Web
as the next step in the broad field of Semantic Web research. We identify
requirements of the corporate environment and gaps between current approaches
to tackle problems facing ontology engineering, semantic collaboration, and
semantic search. Each of these pillars will yield innovative methods and tools
during the project runtime until 2013. Corporate ontology engineering will
improve the facilitation of agile ontology engineering to lessen the costs of
ontology development and, especially, maintenance. Corporate semantic
collaboration focuses the human-centered aspects of knowledge management in
corporate contexts. Corporate semantic search is settled on the highest
application level of the three research areas and at that point it is a
representative for applications working on and with the appropriately
represented and delivered background knowledge. We propose an initial layout
for an integrative architecture of a Corporate Semantic Web provided by these
three core pillars
Design of an E-learning system using semantic information and cloud computing technologies
Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process.
We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers.
In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy.
Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí
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