Search

Achieving sustainable development goals (SDGs) necessitates the adoption of sustainable policies, which entails a crucial task of policy formulation. Policymakers must consider multiple factors, such as the present development status, which can be assessed using diverse data points, as well as the policy’s impact and an action plan outlining its implementation. These data points typically originate from various sources, primarily governmental bodies encompassing statistics, budgets, legislation, public services, and geospatial data (maps, satellite imagery), along with domain users comprising individuals, organizations, and governments. The SDGs are intricately crafted, taking into account a multitude of factors and pinpointing key indicators that influence these factors. These factors typically span across diverse data points sourced from various sectors. For instance, in assessing the student dropout rate, it is imperative to consider factors such as the availability of transportation facilities, basic hygiene amenities, access to drinking water, and the effectiveness of government-organized schemes in utilization and impact. Employing an open data framework coupled with advanced artificial intelligence (AI) models has the potential to facilitate in-depth exploration and analysis, providing valuable insights into the complex interplay of these factors and contributing to a more comprehensive understanding of SDG-related challenges and opportunities. [ABSTRACT FROM AUTHOR]

The process of data analytics on large-scale government administrative data — that belong to various domains like education, transport, energy, and health — can be enhanced by retrieving pertinent documents from diverse data sources. Without a supporting framework of metadata, big data analytics can be daunting. Even though statistical algorithms can perform extensive analyses on a variety of data with little help from metadata, applying these techniques to heterogeneous data may not always result in reliable findings. Recently, semantics-aware (or semantic search) search techniques received much attention as they utilize implicit knowledge to enhance the search. Similarly, traditional search engines rely on the inherent linkages within the underlying data model to improve their search quality. In the case of general-purpose information retrieval systems, to gather information from the internet (open access data) or to access open government administrative data, a domain agnostic ontology shall be employed to supply background knowledge. This paper draws on research undertaken by the authors at IIIT Bangalore Center for Open Data Research (CODR) in developing a semantics-aware data lake framework to host and analyze government administrative data. In this study, we present an ontology-based document retrieval solution where an ontology serves as an intermediary to close the gap between what the user seeks and what the search retrieves. Although our study settings are based on the Government of Karnataka (GoK, India), we believe the findings have wider resonance. Our experimental results based on agricultural data from the GoK look promising. [ABSTRACT FROM AUTHOR]

Semantics-based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty-level of these system-generated questions is helpful to effectively utilize them in various educational and professional applications. The existing approach for predicting the difficulty-level of factual questions utilizes only few naive features and, its accuracy (F-measure) is found to be close to only 50% while considering our benchmark set of 185 questions. In this paper, we propose a new methodology for this problem by identifying new features and by incorporating an educational theory, related to difficulty-level of a question, called Item Response Theory (IRT). In the IRT, knowledge proficiency of end users (learners) are considered for assigning difficulty-levels, because of the assumptions that a given question is perceived differently by learners of various proficiency levels. We have done a detailed study on the features/factors of a question statement which could possibly determine its difficulty-level for three learner categories (experts, intermediates, and beginners). We formulate ontology-based metrics for the same. We then train three logistic regression models to predict the difficulty-level corresponding to the three learner categories. The output of these models is interpreted using the IRT to find a question's overall difficulty-level. The accuracy of the three models based on cross-validation is found to be in satisfactory range (67–84%). The proposed model (containing three classifiers) outperforms the existing model by more than 20% in precision, recall and F1-score measures. [ABSTRACT FROM AUTHOR]