Normalization Techniques For Improving The Performance Of Knowledge Graph Creation Pipelines

With the rapid growth of data within the web, demands on discovering information within data and consecutively exploiting knowledge graphs rise much more than we think it does. Data integration systems can be of great help to meet this precious demand in that they offer transformation of data from various sources and with different volumes. To this end, a data integration system takes advantage of utilizing mapping rules-- specified in a language like RML -- to integrate data collected from various data sources into a knowledge graph. However, large data sources may suffer from various data quality issues, being redundant one of them. Regarding this, the Semantic Web community contributes to Knowledge Engineering with techniques to create a knowledge graph efficiently. The thesis reported in this document tackles creating knowledge graphs in the presence of data sources with redundant data, and a novel normalization theory is proposed to solve this problem. This theory covers not only the characteristics of the data sources but also mapping rules used to integrate the data sources into a knowledge graph. Based on this, three normal forms are proposed and an algorithm for transforming mapping rules and data sources into these normal forms. The proposed approach's performance is evaluated in different testbeds composed of real-world data and synthetic data. The observed results suggest that the proposed techniques can dramatically reduce the execution time of knowledge graph creation. Therefore, this thesis's normalization theory contributes to the repertoire of tools that facilitate the creation of knowledge graphs at scale

Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

Why is the snowflake schema a good data warehouse design?

Database design for data warehouses is based on the notion of the snowflake schema and its important special case, the star schema. The snowflake schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a snowflake schema in terms of an acyclic database schema whose join tree satisfies certain structural properties. We then define a normal form for snowflake schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that snowflake schemas in this normal form are independent as well as separable when the relation schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the snowflake schema and show that the redundancy of the primary key of the fact table is zero

Transformations of Check Constraint PIM Specifications

Platform independent modeling of information systems and generation of their prototypes play an important role in software development process. However, not all tasks in this process have been covered yet, i.e. not all pieces of an information system can be designed using platform independent artifacts that are later transformable into the executable code. One of the examples is modeling of database check constraints, for which there is a lack of appropriate mechanisms to formally specify them on a platform independent level. In order to provide formal specification of check constraints at platform independent level, we developed a domain specific language and embedded it into a tool for platform independent design and automated prototyping of information systems, named Integrated Information Systems CASE (IIS*Case). In this paper, we present algorithms for transformation of check constraints specified at the platform independent level into the relational data model, and further transformation into the executable SQL/DDL code for several standard and commercial platforms: ANSI SQL-2003, Oracle 9i and 10g, and MS SQL Server 2000 and 2008. We have also implemented these algorithms in IIS*Case as a part of the process of generation of relational database schema