Browsing by Subject "Inductive Logic Programming"

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    Conducting Inductive Logic Programming Directly in Database Management Systems
    (2015-07) Koppula, Akshay
    Inductive logic programming (ILP) is a research area formed at the intersection of machine learning and logic programming. Given a set of background knowledge as well as positive and negative examples of a concept, an ILP system attempts to learn rules that cover all the positive examples and none of the negative examples by using the background knowledge. Over the years, ILP is being used extensively in medical applications. Existing ILP systems are implemented in Prolog, using first­-order logic. But, Prolog does not integrate well with database systems, where a lot of the data of interest is stored. Prolog is also not often used in business applications. This thesis presents a novel approach of storing the facts (background knowledge, examples) required for ILP in databases and using Java for easy access and retrieval of the stored knowledge. Since most of the ILP machine learning data sets can be stored easily in databases, this approach provides an easier to use technique. Facts are stored in the form of tables in database and rules are stored as database views by using a database join on the multiple predicates in a fact. A Sequential covering algorithm that uses the best­ first search approach to learn rules for ILP problems is implemented in this thesis. The results obtained on two real­-world test data sets by using this approach are compared with traditional systems. The accuracy of the system presented in this thesis is on par with the accuracy of the traditional systems. These results are very assuring and the system provides an easy-­to-­use approach for the ILP users.
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    Efficiently Storing and Discovering Knowledge in Databases via Inductive Logic Programming Implemented Directly in Databases
    (2015-07) Repaka, Ravikanth
    Inductive Logic Programming (ILP) uses inductive, statistical techniques to generate hypotheses which incorporate the given background knowledge to induce concepts that cover most of the positive examples and few of the negative examples. ILP uses techniques from both logic programming and machine learning. Research has been evolving from several years in this field and many systems are developed to solve ILP problems and most of these systems are developed in Prolog and take the input in the form of text files or other similar formats. This thesis proposes to use a relational database to store background knowledge, positive and negative examples in the form of database entities. This information is then manipulated directly uses ILP techniques efficiently in the process of generating hypotheses. The database does the heavy lifting by efficiently handling and storing a very large number of intermediate rules which are generated in the process of finding the required hypotheses. The proposed system will be helpful to generate hypotheses from relational databases. The system also provides a mechanism to store the given data into a database which exists in text files. Sequential covering algorithm is used to find the hypotheses which cover all positive examples and few or none of the negative examples. The proposed system is tested on real world datasets, Mutagenesis and Chess Endgame, and the generated hypotheses and its accuracy are similar to the results of existing systems which were tested on the same datasets. The results are promising and this encourages researchers to use the system in future to discover the knowledge for other datasets or in relational databases.
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    A GUI For Defining Inductive Logic Programming Tasks For Novice Users
    (2017-03) Basak, Priyankana
    Inductive logic programming, which involves learning a solution to a problem where data is more naturally viewed as multiple tables with relationships between the tables, is an extremely powerful learning method. But these methods have suffered from the fact that very few are written in languages other than Prolog and because describing such problems is difficult. To describe an inductive logic programming problem the user needs to designate many tables and relationships and often provide some knowledge about the relationships in order for the techniques to work well. The goal of this thesis is to develop a Java-based Graphical User Interface (GUI) for novice users that will allow them to define ILP problems by connecting to an existing database and allowing users to define such a problem in an understandable way, perhaps with the assistance of data exploration techniques from the GUI.