Knowledge Graph semantic enhancement of input data for improving AI

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

Data-driven and Knowledge-Based Strategies for Realizing Crowd Wisdom on Social Media

The wisdom of the crowd is a well-known example of collective intelligence wherein an aggregated judgment of a group of individuals is superior to that of an individual. The aggregated judgment is surprisingly accurate for predicting the outcome of a range of tasks from geopolitical forecasting to the stock price prediction. Recent research has shown that participants\u27 previous performance data contributes to the identification of a subset of participants that can collectively predict an accurate outcome. In the absence of such performance data, researchers have explored the role of human-perceived diversity, i.e., whether a human considers a crowd as a diverse crowd, to assemble an intelligent crowd. In fact, diversity among participants and independent decision making are the two most important criteria for a crowd to provide an accurate aggregated judgment. However, perceived diversity based crowd selection does not scale. This dissertation explores whether we can infer the diversity and independence from user-generated social network data to inform intelligent crowd selection. This dissertation first provides a data-driven bottom-up diversity measure and shows that participant diversity can be inferred from social media data and that it can be used to perform diverse crowd selection. It then provides a multi-objective optimization based diverse crowd selection method using this measure. The results show that the diverse crowds significantly outperform both randomly selected and expert crowds. A top-down approach then provides explainable diversity measures to select such a diverse crowd. The data-driven diversity measures do not utilize the social media profile and link information. Community detection using shared content and link information can both inform diverse crowd selection. However, the existing methods do not consider ``contextual\u27\u27 similarity that could play a crucial role in identifying and characterizing contextual communities. This dissertation provides a state-of-the-art contextual similarity measure and a knowledge graph-enhanced community detection approach to select a diverse crowd as well as explain the domain-specific diversity that could affect crowd wisdom. It is shown that such a diverse crowd can accurately predict the outcome of real-world events. These results have implications for numerous domains that utilize aggregated judgments - from consumer reviews to econometrics, to geopolitical forecasting and intelligence analysis

Knowledge Graph Semantic Enhancement of Input Data for Improving AI

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 the input data for two applications that use machine learning-recommendation and community detection. The KG improves both accuracy and explainability

With Whom to Coordinate, Why and How in Ad-Hoc Social Media Communications during Crisis Response

During crises affected people, well-wishers, and observers join social media communities to discuss the event. They often share useful information relevant to response coordination, for example, specific resource needs. However, responders face the challenge of massive data overload and lack the time to monitor social media traffic for important information. Analysis shows that only a small number of event related conversations are actionable. Moreover, responders do not know which sources are trustworthy. To address these challenges, response teams may apply manual filtering methods, resulting in limited coverage and quality. We propose a framework and interface for extracting specific resource-related information and engaging with influential users in the evolving social media community. These users can act as both sources and disseminators of important information to assist coordination, thereby emerging as virtual responders

Enhancing Crowd Wisdom Using Explainable Diversity Inferred from Social Media

A crowd sampled from a set of individuals can provide a more accurate prediction in aggregate than most individuals.This effect, referred to as wisdom of crowd, exists when crowd members bring diverse perspectives to decision making. Such diversity leads to uncorrelated prediction errors that cancel out in aggregate. As crowd members\u27 judgments are often the result of solution strategies, diversity in solution strategies can enhance crowd wisdom. One of the most challenging tasks in sampling such a crowd is to determine the individual\u27s solution strategy for a prediction problem. As participating individuals often share their perspectives through social media, we can use such data to identify an individual\u27s solution strategy. In this paper, we propose a crowd selection approach using social media posts (tweets) indicating diverse solution strategies. We use tweet classification to identify participants\u27 prediction strategies and categorize participants based on the binomial test to identify sets of participants that apply a similar strategy. We then form a diverse crowd by sampling participants from different sets. Using the domain of Fantasy Sports, we show that such a diverse crowd can outperform crowd selected at random and 90% of individual participants, and participant categorization schemes using word2vec. Further, we use a knowledge graph to investigate the factors forming such a diverse crowd and how these factors can lead to a better decision. Relative to bottom-up (data-driven) processes the approach presented here provides an explanation of diverse crowd behavior

Who Should Be the Captain This Week? Leveraging Inferred Diversity-Enhanced Crowd Wisdom for a Fantasy Premier League Captain Prediction

Participants in Fantasy Sports make a critical decision: selecting productive players for their fantasy team. The well-established Wisdom of Crowd effect can predict productive, rewarding players; popular, frequently selected players are potentially good choices. Previous performance data permits the identification of a subset of participants who collectively predict productive players. However, performance data may not always be available. Here we study the assembly of a small subset of the crowd a priori using another important crowd property: semantic diversity. We infer diversity from participants’ Twitter posts (tweets) that users voluntarily, and naturally provide as part of their reasoning. We propose the SmartCrowd framework to select a small, smart crowd using participants’ Twitter posts. SmartCrowd includes three steps: 1) characterize participants using their social media posts with summary word vectors, 2) cluster participants based on these vectors, and 3) sample participants from these clusters, maximizing multiple diversity measures to form final diverse crowds. We evaluated our approach to diversity characterization for the Fantasy Premier League (FPL) captain prediction problem, in which participants predict a successful weekly captain among a set of soccer players. Empirical evaluation shows that SmartCrowd generates diverse crowds outperforming random crowds, 93% of individual participants, and crowds consisting of the top 10%, 20% experts identified from previous performance data. We provide converging evidence that social media based diversity supports the sampling of smarter crowds that collectively predict productive players. These results have implications for other domains, such as economics and geopolitical forecasting, that benefit from aggregated judgments