Easier : An Approach to Automatically Generate Active Ontologies for Intelligent Assistants

Intelligent assistants are ubiquitous and will grow in importance. Apple\u27s well-known assistant Siri uses Active Ontologies to process user input and to model the provided functionalities. Supporting new features requires extending the ontologies or even building new ones. The question is no longer "How to build an intelligent assistant?" but "How to do it efficiently?" We propose EASIER, an approach to automate building and extending Active Ontologies. EASIER identifies new services automatically and classifies unseen service providers with a clustering-based approach. It proposes ontology elements for new service categories and service providers respectively to ease ontology building. We evaluate EASIER with 292 form-based web services and two different clustering algorithms from Weka, DBScan and spectral clustering. DBScan achieves a F1 score of 51% in a ten-fold cross validation but is outperformed by spectral clustering, which achieves a F1 score of even 70%

Design of a web-based LBS framework addressing usability, cost, and implementation constraints

This research investigates barriers that prevent Location Based Services (LBS) from reaching its full potential. The different constraints, including poor usability, lack of positioning support, costs, and integration difficulties are highlighted. A framework was designed incorporating components based on existing and new technologies that could help address the constraints of LBS and increase end-user acceptance. This research proposes that usability constraints can be addressed by adapting a system to user characteristics which are inferred on the basis of captured user context and interaction data. A prototype LBS system was developed to prove the feasibility and benefit of the framework design, demonstrating that constraints of positioning, cost, and integration can be overcome. Volunteers were asked to use the system, and to answer questions in relation to their proficiency and experience. User-feedback showed that the proposed combination of functionality was well-received, and the prototype was appealing to many users. Ground-truths from the survey were related back to data captured with a user monitoring component in order to investigate whether users can be classified according to their context and how they interact. The results have shown that statistically significant relationships exist, and that by using the C4.5 decision-tree, computer proficiency can be estimated within one class-width in 76.7% of the cases. These results suggest that it may be possible to build a user-model to estimate computer proficiency on the basis of user-interaction data. The user model could then used to improve usability through adaptive user-specific customisations

Completeness and Ambiguity of Schema Cover

Given a schema and a set of concepts, representative of entities in the domain of discourse, schema cover defines correspondences between concepts and parts of the schema. Schema cover aims at interpreting the schema in terms of concepts and thus, vastly simplifying the task of schema integration. In this work we investigate two properties of schema cover, namely completeness and ambiguity. The former measures the part of a schema that can be covered by a set of concepts and the latter examines the amount of overlap between concepts in a cover. To study the tradeoffs between completeness and ambiguity we define a cover model to which previous frameworks are special cases. We analyze the theoretical complexity of variations of the cover problem, some aim at maximizing completeness while others aim at minimizing ambiguity. We show that variants of the schema cover problem are hard problems in general and formulate an exhaustive search solution using integer linear programming. We then provide a thorough empirical analysis, using both real-world and simulated data sets, showing empirically that the integer linear programming solution scales well for large schemata. We also show that some instantiations of the general schema cover problem are more effective than others

Ontology mapping: a logic-based approach with applications in selected domains

In advent of the Semantic Web and recent standardization efforts, Ontology has quickly become a popular and core semantic technology. Ontology is seen as a solution provider to knowledge based systems. It facilitates tasks such as knowledge sharing, reuse and intelligent processing by computer agents. A key problem addressed by Ontology is the semantic interoperability problem. Interoperability in general is a common problem in different domain applications and semantic interoperability is the hardest and an ongoing research problem. It is required for systems to exchange knowledge and having the meaning of the knowledge accurately and automatically interpreted by the receiving systems. The innovation is to allow knowledge to be consumed and used accurately in a way that is not foreseen by the original creator. While Ontology promotes semantic interoperability across systems by unifying their knowledge bases through consensual understanding, common engineering and processing practices, it does not solve the semantic interoperability problem at the global level. As individuals are increasingly empowered with tools, ontologies will eventually be created more easily and rapidly at a near individual scale. Global semantic interoperability between heterogeneous ontologies created by small groups of individuals will then be required. Ontology mapping is a mechanism for providing semantic bridges between ontologies. While ontology mapping promotes semantic interoperability across ontologies, it is seen as the solution provider to the global semantic interoperability problem. However, there is no single ontology mapping solution that caters for all problem scenarios. Different applications would require different mapping techniques. In this thesis, we analyze the relations between ontology, semantic interoperability and ontology mapping, and promote an ontology-based semantic interoperability solution. We propose a novel ontology mapping approach namely, OntoMogic. It is based on first order logic and model theory. OntoMogic supports approximate mapping and produces structures (approximate entity correspondence) that represent alignment results between concepts. OntoMogic has been implemented as a coherent system and is applied in different application scenarios. We present case studies in the network configuration, security intrusion detection and IT governance & compliance management domain. The full process of ontology engineering to mapping has been demonstrated to promote ontology-based semantic interoperability