Semantic Scholar

@article{Fricke2018SemanticS,
  title={Semantic Scholar},
  author={Suzanne Fricke},
  journal={Journal of the Medical Library Association : JMLA},
  year={2018},
  volume={106},
  pages={145 - 147}
}
  • Suzanne Fricke
  • Published 1 January 2018
  • Computer Science
  • Journal of the Medical Library Association : JMLA
Eagerly awaited by researchers for years, concrete examples of artificial intelligence–enabled search engines are beginning to emerge. Founded by the nonprofit Allen Institute for Artificial Intelligence (AI2), Semantic Scholar began as a search engine for computer science, geoscience, and neuroscience in 2015. In response to researchers’ inability to keep pace with reading all of the publications in their disciplines, the purpose of the project is automated learning from text in order to… 
Machine learning for rediscovering revolutionary ideas of the past
TLDR
It is suggested how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.
Geospatial and Semantic Mapping Platform for Massive COVID-19 Scientific Publication Search
TLDR
A geospatial and semantic mapping platform to search and organize large and unmapped digital collections of COVID-19 studies, and a semantic map visualizes research topics based on customized natural language processing algorithms, which helps users to identify their content of specific interest beyond keyword searches in web search engines.
The Semantic Web: 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31–June 4, 2020, Proceedings
TLDR
A new technique is introduced that infers the impossibility of certain derivations in the future and blocks the reasoner from performing computation that is doomed to fail anyway and leads to a significant reduction of the reasoning runtime.
Representing Semantified Biological Assays in the Open Research Knowledge Graph
TLDR
A semantification system work-in-progress is described to generate, automatically and quickly, the critical semantified bioassay data mass needed to foster a consistent user audience to adopt the ORKG for recording their bioassays and facilitate the organisation of research, according to FAIR principles.
GIS-KG: building a large-scale hierarchical knowledge graph for geographic information science
TLDR
A GIS knowledge graph (GIS-KG) is built by merging existing GIS bodies of knowledge to create a hierarchical ontology and then applying deep-learning methods to map GIS publications to the ontology.
Digital Libraries at Times of Massive Societal Transition: 22nd International Conference on Asia-Pacific Digital Libraries, ICADL 2020, Kyoto, Japan, November 30 – December 1, 2020, Proceedings
TLDR
This study aims to offer recommendations to the stakeholders of digital libraries for selecting the appropriate technique to build knowledge-graph-based systems for enhanced scholarly information organization by presenting a thorough empirical evaluation of eight Bert-based classification models.
Open Science Graphs Must Interoperate!
TLDR
This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) thedefinition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
TLDR
This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation.
AI Paradigms for Teaching Biotechnology.
Graphing Contributions in Natural Language Processing Research: Intra-Annotator Agreement on a Trial Dataset
TLDR
Data from NLPContributionGraph is integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 12 REFERENCES
Identifying Meaningful Citations
TLDR
This work introduces the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort, and proposes a supervised classification approach that addresses this task with a battery of features.
Chan-Zuckerberg Initiative acquires AI-powered search engine Meta
  • Search Engine Watch [Internet]
  • 2017
Allen’s AI2 expands smart search engine Semantic Scholar to neuroscience research. GeekWire [Internet
  • Nov 2016 [cited
  • 2018
Semantic Scholar
  • Suzanne Fricke
  • Computer Science
    Journal of the Medical Library Association : JMLA
  • 2018
TLDR
Semantic Scholar began as a search engine for computer science, geoscience, and neuroscience in 2015 and the purpose of the project is automated learning from text in order to overcome information overload.
Identifying meaningful citations. In: Scholarly big data AI perspectives, challenges, and ideas: papers from the 2015 AAAI workshop [Internet
  • Association for the Advancement of Artificial Intelligence [cited
  • 2017
Semantic Scholar. Technophiles Newscast 132 [Internet
  • Nov 2015 [cited
  • 2017
AI for the common good
  • MIT Technol Rev [Internet]
  • 2016
Scientists are drowning, artificial intelligence will save them. Discover [Internet
  • Nov 2016 [cited
  • 2016
Allen's AI2 expands smart search engine Semantic Scholar to neuroscience research. GeekWire
Chan-Zuckerberg Initiative acquires AI-powered search engine Meta. Search Engine Watch
...
1
2
...