- From: Francesco.Osborne <fo444@open.ac.uk>
- Date: Tue, 20 Sep 2022 09:12:27 +0000
- To: Sarven Capadisli <info@csarven.ca>, "mm@zeroexp.com" <mm@zeroexp.com>
- CC: "semantic-web@w3.org" <semantic-web@w3.org>
Hi Sarven and Margaret, That is actually something we are also working on and one of the most exciting scientific challenges in my opinion, as it can have a profound impact on the research process. It is not easy since it requires a deep understanding of natural language (to do it automatically) or a new set of strong incentives (to convince researchers to do it). As first step, we recently generated the Computer Science Knowledge Graph (CS-KG, http://w3id.org/cskg), which includes over 350M RDF triples describing 41M statements from 6.7M articles about 10M entities (e.g., tasks, methods, materials, metrics) linked by 179 semantic relations. Each statement (e.g. <sentiment_analysis, usesMethod, deep_learning>) is associated with the set of articles it was extracted from. CS-KG was designed to support a large variety of intelligent services for analyzing and making sense of research dynamics, supporting researchers in their daily job, and informing decision of funding bodies and research policy makers. We will present it in the ISWC Resource track and hope that the community will be able to reuse it and improve it in the following years. Cheers, Francesco > On 14 Sep 2022, at 10:48, Sarven Capadisli <info@csarven.ca> wrote: > > On 2022-09-14 10:11, Angelo Salatino wrote: > Is it possible to discover problem statements, motivation, hypothesis, arguments, workflow steps, methodology, design, results, evaluation, conclusions, future challenges, as well as all inline semantic citations (to name a few) where they are uniquely identified and related to other data? > > If not, why not? > > -Sarven > https://csarven.ca/#i > <OpenPGP_0xA74187CE3D508E3A.asc>
Received on Friday, 23 September 2022 08:16:19 UTC