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Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been ...

Automated detection of false information is crucial for combating “fake news” on online social media networks (OSMN), reducing the need for manual discernment. Existing solutions often use content or context features of OSMN documents in isolation,...

Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social ...

• A study of the discovery problem of Graph Entity Dependencies (GEDs).
• A new and efficient approach for the discovery of GEDs in property graphs.
• A minimum description length inspired definition of interestingness of GEDs to rank ...

Graph differential dependencies (GDDs) are a novel class of integrity constraints in property graphs for capturing and expressing the semantics of difference in graph data. They are more expressive, and subsume other graph dependencies; and thus, ...

This paper studies the discovery of approximate rules in property graphs. First, we propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) that almost hold, to tolerate errors and inconsistencies that exist ...

Planned organisational changes are frequent occurrences in large enterprises due to the dynamicity of employees’ roles, evolution of teams, units and divisions as a result of mergers, demergers, and general restructuring. To safeguard system ...

Story timeline summarization is widely used by analysts, law enforcement agencies, and policymakers for content presentation, story-telling, and other data-driven decision-making applications. Recent advancements in web technologies have rendered social ...

Entity resolution (ER) is the problem of accurately identifying multiple, differing, and possibly contradicting representations of unique real-world entities in data. It is a challenging and fundamental task in data cleansing and data integration. In ...