Semantic under-determinacy, Content and Context

The aim of the thesis is to investigate the topic of semantic under-determinacy, i.e. the failure of the semantic content of certain expressions to determine a truth-evaluable utterance content. In the first part of the thesis, I engage with the problem of setting apart semantic under-determinacy as opposed to other phenomena such as ambiguity, vagueness, indexicality. As I will argue, the feature that distinguishes semantic under-determinacy from these phenomena is its being explainable solely in terms of under-articulation. In the second part of the thesis, I discuss the topic of how communication is possible, despite the semantic under-determinacy of language. I discuss a number of answers that have been offered: (i) the Radical Contextualist explanation which emphasises the role of pragmatic processes in utterance comprehension; (ii) the Indexicalist explanation in terms of hidden syntactic positions; (iii) the Relativist account, which regards sentences as true or false relative to extra coordinates in the circumstances of evaluation (besides possible worlds). In the final chapter, I propose an account of the comprehension of utterances of semantically under-determined sentences in terms of conceptual constraints, i.e. ways of organising information which regulate thought and discourse on certain matters. Conceptual constraints help the hearer to work out the truth-conditions of an utterance of a semantically under-determined sentence. Their role is clearly semantic, in that they contribute to “what is said” (rather than to “what is implied”); however, they do not respond to any syntactic constraint. The view I propose therefore differs, on the one hand, from Radical Contextualism, because it stresses the role of semantic-governed processes as opposed to pragmatics-governed processes; on the other hand, it differs from Indexicalism in its not endorsing any commitment as to hidden syntactic positions; and it differs from Relativism in that it maintains a monadic notion if truth.

Searching and retrieving in content-based repositories of formal mathematical knowledge

In this thesis, the author presents a query language for an RDF (Resource Description Framework) database and discusses its applications in the context of the HELM project (the Hypertextual Electronic Library of Mathematics). This language aims at meeting the main requirements coming from the RDF community. in particular it includes: a human readable textual syntax and a machine-processable XML (Extensible Markup Language) syntax both for queries and for query results, a rigorously exposed formal semantics, a graph-oriented RDF data access model capable of exploring an entire RDF graph (including both RDF Models and RDF Schemata), a full set of Boolean operators to compose the query constraints, fully customizable and highly structured query results having a 4-dimensional geometry, some constructions taken from ordinary programming languages that simplify the formulation of complex queries. The HELM project aims at integrating the modern tools for the automation of formal reasoning with the most recent electronic publishing technologies, in order create and maintain a hypertextual, distributed virtual library of formal mathematical knowledge. In the spirit of the Semantic Web, the documents of this library include RDF metadata describing their structure and content in a machine-understandable form. Using the author's query engine, HELM exploits this information to implement some functionalities allowing the interactive and automatic retrieval of documents on the basis of content-aware requests that take into account the mathematical nature of these documents.

Machine learning tools for protein annotation: the cases of transmembrane β-barrel and myristoylated proteins

Biology is now a “Big Data Science” thanks to technological advancements allowing the characterization of the whole macromolecular content of a cell or a collection of cells. This opens interesting perspectives, but only a small portion of this data may be experimentally characterized. From this derives the demand of accurate and efficient computational tools for automatic annotation of biological molecules. This is even more true when dealing with membrane proteins, on which my research project is focused leading to the development of two machine learning-based methods: BetAware-Deep and SVMyr. BetAware-Deep is a tool for the detection and topology prediction of transmembrane beta-barrel proteins found in Gram-negative bacteria. These proteins are involved in many biological processes and primary candidates as drug targets. BetAware-Deep exploits the combination of a deep learning framework (bidirectional long short-term memory) and a probabilistic graphical model (grammatical-restrained hidden conditional random field). Moreover, it introduced a modified formulation of the hydrophobic moment, designed to include the evolutionary information. BetAware-Deep outperformed all the available methods in topology prediction and reported high scores in the detection task. Glycine myristoylation in Eukaryotes is the binding of a myristic acid on an N-terminal glycine. SVMyr is a fast method based on support vector machines designed to predict this modification in dataset of proteomic scale. It uses as input octapeptides and exploits computational scores derived from experimental examples and mean physicochemical features. SVMyr outperformed all the available methods for co-translational myristoylation prediction. In addition, it allows (as a unique feature) the prediction of post-translational myristoylation. Both the tools here described are designed having in mind best practices for the development of machine learning-based tools outlined by the bioinformatics community. Moreover, they are made available via user-friendly web servers. All this make them valuable tools for filling the gap between sequential and annotated data.

Connecting works of art within the semantic web of symbolic meanings

My doctoral research is about the modelling of symbolism in the cultural heritage domain, and on connecting artworks based on their symbolism through knowledge extraction and representation techniques. In particular, I participated in the design of two ontologies: one models the relationships between a symbol, its symbolic meaning, and the cultural context in which the symbol symbolizes the symbolic meaning; the second models artistic interpretations of a cultural heritage object from an iconographic and iconological (thus also symbolic) perspective. I also converted several sources of unstructured data, a dictionary of symbols and an encyclopaedia of symbolism, and semi-structured data, DBpedia and WordNet, to create HyperReal, the first knowledge graph dedicated to conventional cultural symbolism. By making use of HyperReal's content, I showed how linked open data about cultural symbolism could be utilized to initiate a series of quantitative studies that analyse (i) similarities between cultural contexts based on their symbologies, (ii) broad symbolic associations, (iii) specific case studies of symbolism such as the relationship between symbols, their colours, and their symbolic meanings. Moreover, I developed a system that can infer symbolic, cultural context-dependent interpretations from artworks according to what they depict, envisioning potential use cases for museum curation. I have then re-engineered the iconographic and iconological statements of Wikidata, a widely used general-domain knowledge base, creating ICONdata: an iconographic and iconological knowledge graph. ICONdata was then enriched with automatic symbolic interpretations. Subsequently, I demonstrated the significance of enhancing artwork information through alignment with linked open data related to symbolism, resulting in the discovery of novel connections between artworks. Finally, I contributed to the creation of a software application. This application leverages established connections, allowing users to investigate the symbolic expression of a concept across different cultural contexts through the generation of a three-dimensional exhibition of artefacts symbolising the chosen concept.

One archive, many readings. Personal archives as complex networks in the semantic web

Personal archives are the archives created by individuals for their own purposes. Among these are the library and documentary collections of writers and scholars. It is only recently that archival literature has begun to focus on this category of archives, emphasising how their heterogeneous nature necessitates the conciliation of different approaches to archival description, and calling for a broader understanding of the principle of provenance, recognising that multiple creators, including subsequent researchers, can contribute to shaping personal archives over time by adding new layers of contexts. Despite these advances in the theoretical debate, current architectures for archival representation remain behind. Finding aids privilege a single point of view and do not allow subsequent users to embed their own, potentially conflicting, readings. Using semantic web technologies this study aims to define a conceptual model for writers' archives based on existing and widely adopted models in the cultural heritage and humanities domains. The model developed can be used to represent different types of documents at various levels of analysis, as well as record content and components. It also enables the representation of complex relationships and the incorporation of additional layers of interpretation into the finding aid, transforming it from a static search tool into a dynamic research platform.  The personal archive and library of Giuseppe Raimondi serves as a case study for the creation of an archival knowledge base using the proposed conceptual model. By querying the knowledge graph through SPARQL, the effectiveness of the model is evaluated. The results demonstrate that the model addresses the primary representation challenges identified in archival literature, from both a technological and methodological standpoint. The ultimate goal is to bring the output par excellence of archival science, i.e. the finding aid, more in line with the latest developments in archival thinking.