Attribute Lattices: A Schema-free Unified Framework for Data Semantics

One key characteristic of big data is variety. With massive and growing amounts of data existing in independent and heterogeneous (structured and unstructured) sources, assigning consistent data semantics, which is essential for making sense of data sources, is an increasingly important challenge. We use ontology and human cognitive principles (i.e., classification theory) to formally define the concept of attribute lattice. An attribute lattice is a graph-based, schema-free conceptual model that represents attributes of instances in the domain of interest and precedence relations among them. The class structure of the domain can be inferred from the precedence relations in the lattice. In other words, in an attribute lattice, both properties and classes are represented as attributes – they are distinguished only by the pattern of arcs and nodes that surround them (the semantic neighbourhood). We propose that this form of representation offers a unified framework for modeling data that can be used to resolve semantic data heterogeneity.