Intersubjectivity and sentiment:from language to knowledge

Intersubjectivity is an important concept in psychology and sociology. It refers to sharing conceptualizations through social interactions in a community and using such shared conceptualization as a resource to interpret things that happen in everyday life. In this work, we make use of intersubjectivity as the basis to model shared stance and subjectivity for sentiment analysis. We construct an intersubjectivity network which links review writers, terms they used, as well as the polarities of the terms. Based on this network model, we propose a method to learn writer embeddings which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the IMDB, Yelp 2013 and Yelp 2014 datasets show that the proposed approach has achieved the state-of-the-art performance.

Fringe Types and KOS Systematics: Examining the Limits of the Population Perspective of Knowledge Organization Systems

We find ourselves, after the close of the twentieth century, looking back at a mass of responses to the knowledge organization problem. Many institutions, such as the Dewey Decimal Classification (Furner, 2007), have grown up to address it. Increasingly, many diverse discourses are appropriating the problem and crafting a wide variety of responses. This includes many artistic interpretations of the act and products of knowledge organization. These surface as responses to the expressive power or limits of the Library and Information Studies institutions (e.g., DDC) and their often primarily utilitarian gaze.One way to make sense of this diversity is to approach the study from a descriptive stance, inventorying the population of types of KOS. This population perspective approaches the phenomenon of types and boundaries of Knowledge Organization Systems (KOS) as one that develops out of particular discourses, for particular purposes. For example, both DDC and Martianus Capella, a 5th Century encyclopedist, are KOS in this worldview. Both are part of the population of KOS. Approaching the study of KOS from the population perspective allows the researcher a systematic look at the diversity emergent at the constellation of different factors of design and implementation. However, it is not enough to render a model of core types, but we have to also consider the borders of KOS. Fringe types of KOS inform research, specifically to the basic principles of design and implementation used by others outside of the scholarly and professional discourse of Library and Information Studies.Four examples of fringe types of KOS are presented in this paper. Applying a rubric developed in previous papers, our aim here is to show how the conceptual anatomy of these fringe types relates to more established KOS, thereby laying bare the definitions of domain, purpose, structure, and practice. Fringe types, like Beghtol’s examples (2003), are drawn from areas outside of Library and Information Studies proper, and reflect the reinvention of structures to fit particular purposes in particular domains. The four fringe types discussed in this paper are (1) Roland Barthes’ text S/Z which “indexes” a text of an essay with particular “codes” that are meant to expose the literary rhythm of the work; (2) Mary Daly’s Wickedary, a reference work crafted for radical liberation theology – and specifically designed to remove patriarchy from the language used by what the author calls “wild women”; (3) Luigi Serafini’s Codex Seraphinianus a work of book art that plays on the trope of universal encyclopedia and back-of- the book index; and (4) Martinaus Capella – and his Marriage of Mercury and Philology, a fifth century encyclopedia. We compared these using previous analytic taxonomies (Wright, 2008; Tennis, 2006; Tudhope, 2006, Soergel, 2001, Hodge, 2000).

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Development of new immunochemical and spectroscopic analytical methods for the characterization and conservation of works of art

Cultural heritage is constituted by complex and heterogenous materials, such as paintings but also ancient remains. However, all ancient materials are exposed to external environment and their interaction produces different changes due to chemical, physical and biological phenomena. The organic fraction, especially the proteinaceous one, has a crucial role in all these materials: in archaeology proteins reveal human habits, in artworks they disclose technics and help for a correct restoration. For these reasons the development of methods that allow the preservation of the sample as much as possible and a deeper knowledge of the deterioration processes is fundamental. The research activities presented in this PhD thesis have been focused on the development of new immunochemical and spectroscopic approaches in order to detect and identify organic substances in artistic and archaeological samples. Organic components could be present in different cultural heritage materials as constituent element (e.g., binders in paintings, collagen in bones) and their knowledge is fundamental for a complete understanding of past life, degradation processes and appropriate restauration approaches. The combination of immunological approach with a chemiluminescence detection and Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry allowed a sensitive and selective localization of collagen and elements in ancient bones and teeth. Near-infrared spectrometer and hyper spectral imaging have been applied in combination with chemometric data analysis as non-destructive methods for bones prescreening for the localization of collagen. Moreover, an investigation of amino acids in enamel has been proposed, in order to clarify teeth biomolecules survival overtime through the optimization and application of High-Performance Liquid Chromatography on modern and ancient enamel powder. New portable biosensors were developed for ovalbumin identification in paintings, thanks to the combination between biocompatible Gellan gel and electro-immunochemical sensors, to extract and identify painting binders with the contact only between gel and painting and between gel and electrodes.

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.

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.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.

[between The Country And The Laboratory: The Dynamics Of The Production Of Knowledge At The Menopause Outpatients Clinic At Caism, Unicamp.]

This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.

[between The Country And The Laboratory: The Dynamics Of The Production Of Knowledge At The Menopause Outpatients Clinic At Caism, Unicamp].

This study investigates the practices involved in the production of knowledge about menopause at Caism, Unicamp, a reference center for public policies for women's health. Gynecological appointments and psychological support meetings were observed, and women and doctors were interviewed in order to identify what discourse circulates there and how different actors are brought in to ensure that the knowledge produced attains credibility and travels beyond the boundaries of the teaching hospital to become universal. The analysis is based on localized studies aligned with social studies of science and technology.

Engineered Nanoparticles And Organic Matter: A Review Of The State-of-the-art.

Growth in the development and production of engineered nanoparticles (ENPs) in recent years has increased the potential for interactions of these nanomaterials with aquatic and terrestrial environments. Carefully designed studies are therefore required in order to understand the fate, transport, stability, and toxicity of nanoparticles. Natural organic matter (NOM), such as the humic substances found in water, sediment, and soil, is one of the substances capable of interacting with ENPs. This review presents the findings of studies of the interaction of ENPs and NOM, and the possible effects on nanoparticle stability and the toxicity of these materials in the environment. In addition, ENPs and NOM are utilized for many different purposes, including the removal of metals and organic compounds from effluents, and the development of new electronic sensors and other devices for the detection of active substances. Discussion is therefore provided of some of the ways in which NOM can be used in the production of nanoparticles. Although there has been an increase in the number of studies in this area, further progress is needed to improve understanding of the dynamic interactions between ENPs and NOM.

Química Analítica de Processos

Process Analytical Chemistry (PAC) is an important and growing area in analytical chemistry, that has received little attention in academic centers devoted to the gathering of knowledge and to optimization of chemical processes. PAC is an area devoted to optimization and knowledge acquisition of chemical processes, to reducing costs and wastes and to making an important contribution to sustainable development. The main aim of this review is to present to the Brazilian community the development and state of the art of PAC, discussing concepts, analytical techniques currently employed in the industry and some applications.

A ginastica geral na sociedade contemporanea : pespectivas para a Educação Fisica escolar

Universidade Estadual de Campinas. Faculdade de Educação Física

Corpo-sujeito Kadiweu : jogo e esporte

Universidade Estadual de Campinas . Faculdade de Educação Física