Zur Grundlegung der Erkenntnistheorie

Ingarden, Román; Fieguth, Rolf; Küng, Guido; Galewicz, Wlodzimierz

doi:10.1515/9783110916393

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To derive such laws as general principles from data, a cognitive process seems to be required that abstracts from observations to scientific laws. This step relates to our human cognitive ability of making idealizations, and has early been described as necessary for scientific research by philosophers such as Husserl [29] or Ingarden [30].…”

Section: Limits Of Data Sciencementioning

confidence: 99%

Data Science and symbolic AI: Synergies, challenges and opportunities

Hoehndorf

Queralt-Rosiñach

2017

View full text Add to dashboard Cite

Editor: Tobias Kuhn (https://orcid.org/0000-0002-1267-0234) Solicited reviews: anonymous reviewer; Rinke Hoekstra (https://orcid.org/0000-0001-7076-9083); Agnieszka Ławrynowicz (https://orcid.org/0000-0002-2442-345X); Honghan Wu (https://orcid.org/0000-0002-0213-5668) Received 21 February 2017 Accepted 27 April 2017Abstract. Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Symbolic representations and symbolic inference are close to human cognitive representations and therefore comprehensible and interpretable; they are widely used to represent data and metadata, and their specific semantic content must be taken into account for analysis of such information; and human communication largely relies on symbols, making symbolic representations a crucial part in the analysis of natural language. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists' toolbox.

show abstract

Section: Limits Of Data Sciencementioning

confidence: 99%