Concept discovery is the problem of finding definitions of target relation in terms or other relation given as a background knowledge. Inductive Logic Programming (ILP)-based and graph-based approaches are two competitors in concept discovery problem. Although ILP-based systems have long dominated the area, graph-based systems have recently gained popularity as they overcome certain shortcomings of ILP-based systems. While having applications in numerous domains, ILP-based concept discovery systems still sustain scalability and efficiency issues. These issues generally arose due to the large search spaces such systems build. In this work we propose memoization-based and parallelization-based methods that modify the search space construction step and the evaluation step of ILP-based concept discovery systems to overcome these problem. In this work we propose three memoization-based methods, called Tabular CRIS, Tabular CRIS-wEF, and Selective Tabular CRIS. In these methods, basically, evaluation queries are stored in look-up tables for later uses. While preserving some core functions in common, each proposed method improves e_ciency and scalability of its predecessor by introducing constraints on what kind of evaluation queries to store in look-up tables and for how long. The proposed parallelization method, called pCRIS, parallelizes the search space construction and evaluation steps of ILP-based concept discovery systems in a data-parallel manner. The proposed method introduces policies to minimize the redundant work and waiting time among the workers at synchronization points. Graph-based approaches were first introduced to the concept discovery domain to handle the so called local plateau problem. Graph-based approaches have recently gained more popularity in concept discovery system as they provide convenient environment to represent relational data and are able to overcome certain shortcomings of ILP-based concept discovery systems. Graph-based approaches can be classified as structure-based approaches and path-finding approaches. The first class of approaches need to employ expensive algorithms such as graph isomorphism to find frequently appearing substructures. The methods that fall into the second class need to employ sophisticated indexing mechanisms to find out the frequently appearing paths that connect some nodes in interest. In this work, we also propose a hybrid method for graph-based concept discovery which does not require costly substructure matching algorithms and path indexing mechanism. The proposed method builds the graph in such a way that similar facts are grouped together and paths that eventually turn to be concept descriptors are build while the graph is constructed.

Multi Relational Data Mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. However, as patterns involve multiple relations, the search space of possible hypothesis becomes intractably complex. In order to cope with this problem, several relational knowledge discovery systems have been developed employing various search strategies, heuristics and language pattern limitations. In this thesis, Inductive Logic Programming (ILP) based concept discovery is studied and two systems based on a hybrid methodology employing ILP and APRIORI, namely Confidence-based Concept Discovery and Concept Rule Induction System, are proposed. In Confidence-based Concept Discovery and Concept Rule Induction System, the main aim is to relax the strong declarative biases and user-defined specifications. Moreover, this new method directly works on relational databases. In addition to this, the traditional definition of confidence from relational database perspective is modified to express Closed World Assumption in first-order logic. A new confidence-based pruning method based on the improved definition is applied in the APRIORI lattice. Moreover, a new hypothesis evaluation criterion is used for expressing the quality of patterns in the search space. In addition to this, in Concept Rule Induction System, the constructed rule quality is further improved by using an improved generalization metod. Finally, a set of experiments are conducted on real-world problems to evaluate the performance of the proposed method with similar systems in terms of support and confidence.

As recently as the last decade or two, data-driven science workflows have become increasingly popular and semantic technology has been relied on to help align often parallel research efforts in the different domains and foster interoperability and data sharing. However, a key challenge is the size of the data and the pace at which it is being generated, so much that manual procedures lag behind. Thus, eliciting automation of most workflows. In this study, the effort is to continue investigating ways by which some tasks performed by experts in the nanotechnology domain, specifically in ontology engineering, could benefit from automation. An approach, featuring phrase-based topic modelling and formal topical concept analysis is further motivated, together with formal implication rules, to uncover new concepts and axioms relevant to two nanotechnology-related ontologies. A corpus of 2,715 nanotechnology research articles helps showcase that the approach can scale, as seen in a number of experiments conducted. The usefulness of document text ranking as an alternative form of input to topic models is highlighted as well as the benefit of implication rules to the task of concept discovery. In all, a total of 203 new concepts are uncovered by the approach to extend the referenced ontologies

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