Formal Semantics: Origins, Issues, Early Impact

Formal semantics and pragmatics as they have developed since the late 1960\u27s have been shaped by fruitful interdisciplinary collaboration among linguists, philosophers, and logicians, among others, and in turn have had noticeable effects on developments in syntax, philosophy of language, computational linguistics, and cognitive science.In this paper I describe the environment in which formal semantics was born and took root, highlighting the differences in ways of thinking about natural language semantics in linguistics and in philosophy and logic. With Montague as a central but not solo player in the story, I reflect on crucial developments in the 1960\u27s and 70\u27s in linguistics and philosophy, and the growth of formal semantics and formal pragmatics from there. I discuss innovations, key players, and leading ideas that shaped the development of formal semantics and its relation to syntax, to pragmatics, and to the philosophy of language in its early years, and some central aspects of its early impact on those fields

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

Incrementally resolving references in order to identify visually present objects in a situated dialogue setting

Kennington C. Incrementally resolving references in order to identify visually present objects in a situated dialogue setting. Bielefeld: Universität Bielefeld; 2016.The primary concern of this thesis is to model the resolution of spoken referring expressions made in order to identify objects; in particular, everyday objects that can be perceived visually and distinctly from other objects. The practical goal of such a model is for it to be implemented as a component for use in a live, interactive, autonomous spoken dialogue system. The requirement of interaction imposes an added complication; one that has been ignored in previous models and approaches to automatic reference resolution: the model must attempt to resolve the reference incrementally as it unfolds–not wait until the end of the referring expression to begin the resolution process. Beyond components in dialogue systems, reference has been a major player in the philosophy of meaning for longer than a century. For example, Gottlob Frege (1892) has distinguished between Sinn (sense) and Bedeutung (reference), discussed how they are related and how they relate to the meaning of words and expressions. It has furthermore been argued (e.g., Dahlgren (1976)) that reference to entities in the actual world is not just a fundamental notion of semantic theory, but the fundamental notion; for an individual acquiring a language, understanding the meaning of many words and concepts is done via the task of reference, beginning in early childhood. In this thesis, we pursue an account of word meaning that is based on perception of objects; for example, the meaning of the word red is based on visual features that are selected as distinguishing red objects from non-red ones. This thesis proposes two statistical models of incremental reference resolution. Given ex- amples of referring expressions and visual aspects of the objects to which those expressions referred, both model components learn a functional mapping between the words of the refer- ring expressions and the visual aspects. A generative model, the simple incremental update model, presented in Chapter 5, uses a mediating variable to learn the mapping, whereas a dis- criminative model, the words-as-classifiers model, presented in Chapter 6, learns the mapping directly and improves over the generative model. Both models have been evaluated in various reference resolution tasks to objects in virtual scenes as well as real, tangible objects. This thesis shows that both models work robustly and are able to resolve referring expressions made in reference to visually present objects despite realistic, noisy conditions of speech and object recognition. A theoretical and practical comparison is also provided. Special emphasis is given to the discriminative model in this thesis because of its simplicity and ability to represent word meanings. It is in the learning and application of this model that gives credence to the above claim that reference is the fundamental notion for semantic theory and that meanings of (visual) words is done through experiencing referring expressions made to objects that are visually perceivable