Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
By Amit P. Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan. 2016
23 citations recorded by the source corpus.
Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ugur Kursuncu
- Manas Gaur
- Amit P. Sheth
- Krishnaprasad Thirunarayan
- Ismailcem Budak Arpinar
- Carlos Castillo
- Valerie L. Shalin
- Amanuel Alambo
In Molecular Biology
Citations in the corpus
- Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “However, (Sheth et al. 2017; Kho et al. 2019) have pointed out the importance of using domain-specific knowledge especially in cases where the problem is complex in nature (Perera et al. 2016).”
- Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions (2019, result): “n precision, recall and F1-score of 0.04, 0.04 and 0.03, respectively. In contrast to this literature, our work is grounded in the social science literature and incorporates domain-specific resources [30, 31, 52] in the model to better understand and detect extremist content usinglinguisticapproache”
- Shades of Knowledge-Infused Learning for Enhancing Deep Learning (2019, methodology): “However, Sheth et al.(11) have pointed out the importance of using domainspecific knowledge especially in cases where the problem is complex.”