title: Attribute lattice: a graph-based conceptual modeling grammar for heterogeneous data
Creators: name: Asgari, Mojtaba
Publisher: name: Memorial University of Newfoundland
Year: 2020

description: 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
 and interoperable data semantics, which is essential for meaningful use of data, is an increasingly
 important challenge. I argue, conceptual models, in contrast to their traditional roles in
 the Information System development, can be used to represent data semantics as perceived by the
 user of data. In this thesis, I use principles from philosophical ontology, human cognition (i.e.,
 classification theory), and graph theory to offer a theory-based conceptual modeling grammar for
 this purpose. This grammar reflects data from users of data perspective and independent from data
 source schema. I formally define the concept of attribute lattice as a graph-based, schema-free
 conceptual modeling grammar that represents attributes of instances in the domain of interest and
 precedence relations among them. Each node in an attribute lattice represents an attribute - a true
 statement (predicate) about some instances in the domain. Each directed arc represents a precedence
 relation indicating that possessing one attribute implies possessing another attribute.
 In this thesis, based on the premise that inherent classification is a barrier that hinders semantic
 interoperation of heterogeneous data sources, a human cognition based conceptual modeling
 grammar is introduced as an effective way to resolve semantic heterogeneity. This grammar
 represents the precedence relationship among attributes as perceived by human user and provides
 a mechanism to infer classes based on the pattern of precedences. Hence, a key contribution of
 attribute lattice is semantic relativism – that is, the classification in this grammar relies on the pattern of precedence relationship among attributes rather than fixed classes. This modeling grammar
 uses the immediate and semantic neighbourhoods of an attribute to designate an attribute as
 either a category, a class or a property and to specify the expansion of an attribute – attributes
 which are semantically equal to the given attribute. The introduced conceptual modeling grammar
 is implemented as an artifact to store and manage attribute lattices, to graphically represent them,
 and integrate lattices from various heterogeneous sources.
 With the ever-increasing amount of unstructured data (mostly text data) from various data
 sources such as social media, integrating text data with other data sources has gained considerable
 attention. This massive amount of data, however, makes finding the data relevant to a topic of
 interest a new challenge. I argue that the attribute lattice provides a robust semantic foundation to
 address this information retrieval challenge from unstructured data sources. Hence, a topic modeling
 approach based on the attribute lattice is proposed for Twitter. This topic model conceptualizes
 topic structure of tweets related to the domain of interest and enhances information retrieval
 by improving the semantic interpretability of hashtags.