Knowledge will propel machine understanding of content

Sheth, Amit P.; Perera, Sujan; Wijeratne, Sanjaya; Thirunarayan, Krishnaprasad

doi:10.1145/3106426.3109448

Cited by 17 publications

(14 citation statements)

References 31 publications

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“…Enhancing learning: AI techniques including ML algorithms which learn from pre-labeled examples are acknowledging that "data alone is not enough" [5]. There is a growing body of work seeking to demonstrate how and how much use of domain knowledge improves the results or effectiveness of state of the art ML and NLP techniques [6]. Learning the underlying patterns in the data goes beyond instancebased generalization to some external knowledge represented in structured graphs or networks.…”

Section: Kg Enabled Web and Enterprise Applicationsmentioning

confidence: 99%

Knowledge Graphs and Knowledge Networks: The Story in Brief

Sheth

Padhee

Gyrard

2019

IEEE Internet Comput.

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Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.

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Section: Kg Enabled Web and Enterprise Applicationsmentioning

confidence: 99%

Knowledge Graphs and Knowledge Networks: The Story in Brief

Sheth

Padhee

Gyrard

2019

IEEE Internet Comput.

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“…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 using linguistic approaches. As Islamist extremism is a complex issue that involves different contexts, traditional approaches do not adequately capture important nuances in the language related to the multiple contextual dimensions of the problem.…”

Section: Related Workmentioning

confidence: 99%

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Kursuncu¹,

Castillo²,

Alambo³

et al. 2019

Preprint

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Terror attacks have been linked in part to online extremist content. Online conversations are cloaked in religious ambiguity, with deceptive intentions, often twisted from mainstream meaning to serve a malevolent ideology. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Radicalization is a subtle long-running persuasive process that occurs over time. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. Based on prior empirical and qualitative research in social sciences, particularly political science, we model this process using a combination of three contextual dimensions -religion, ideology, and hate -each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. The significant sensitivity of the Islamist extremist ideology and its local and global security implications require reliable algorithms for modelling such communications on Twitter. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate, which reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to

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“…Subsequently, background knowledge has played a key role in various tasks ranging from search and classification to personalized recommendations. In this decade, several researchers have explored the role of background knowledge to enhance natural language processing and machine learning [17].…”

mentioning

confidence: 99%

Knowledge Graph Semantic Enhancement of Input Data for Improving AI

Bhatt

Sheth

Shalin

et al. 2020

IEEE Internet Comput.

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Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning --recommendation and community detection. The KG improves both accuracy and explainability.

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Knowledge will propel machine understanding of content

Cited by 17 publications

References 31 publications

Knowledge Graphs and Knowledge Networks: The Story in Brief

Knowledge Graphs and Knowledge Networks: The Story in Brief

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Knowledge Graph Semantic Enhancement of Input Data for Improving AI

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