A communication infrastructure to support knowledge level agents on the web

Agent Communication Languages (ACLs) have been developed to provide a way for agents to communicate with each other supporting cooperation in Multi-Agent Systems. In the past few years many ACLs have been proposed for Multi-Agent Systems, such as KQML and FIPA-ACL. The goal of these languages is to support high-level, human like communication among agents, exploiting Knowledge Level features rather than symbol level ones. Adopting these ACLs, and mainly the FIPA-ACL specifications, many agent platforms and prototypes have been developed. Despite these efforts, an important issue in the research on ACLs is still open and concerns how these languages should deal (at the Knowledge Level) with possible failures of agents. Indeed, the notion of Knowledge Level cannot be straightforwardly extended to a distributed framework such as MASs, because problems concerning communication and concurrency may arise when several Knowledge Level agents interact (for example deadlock or starvation). The main contribution of this Thesis is the design and the implementation of NOWHERE, a platform to support Knowledge Level Agents on the Web. NOWHERE exploits an advanced Agent Communication Language, FT-ACL, which provides high-level fault-tolerant communication primitives and satisfies a set of well defined Knowledge Level programming requirements. NOWHERE is well integrated with current technologies, for example providing full integration for Web services. Supporting different middleware used to send messages, it can be adapted to various scenarios. In this Thesis we present the design and the implementation of the architecture, together with a discussion of the most interesting details and a comparison with other emerging agent platforms. We also present several case studies where we discuss the benefits of programming agents using the NOWHERE architecture, comparing the results with other solutions. Finally, the complete source code of the basic examples can be found in appendix.

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.