PARLEN: uno strumento modulare per l’analisi di articoli e il riconoscimento di entità

La tesi descrive PARLEN, uno strumento che permette l'analisi di articoli, l'estrazione e il riconoscimento delle entità - ad esempio persone, istituzioni, città - e il collegamento delle stesse a risorse online. PARLEN è inoltre in grado di pubblicare i dati estratti in un dataset basato su principi e tecnologie del Semantic Web.

Learning Meta-Descriptions of the FOAF Network

Postprint

A Role for Provenance in Social Computation

Postprint

It's always been about the links

Abstract The World Wide Web Consortium, W3C, is known for standards like HTML and CSS but there's a lot more to it than that. Mobile, automotive, publishing, graphics, TV and more. Then there are horizontal issues like privacy, security, accessibility and internationalisation. Many of these assume that there is an underlying data infrastructure to power applications. In this session, W3C's Data Activity Lead, Phil Archer, will describe the overall vision for better use of the Web as a platform for sharing data and how that translates into recent, current and possible future work. What's the difference between using the Web as a data platform and as a glorified USB stick? Why does it matter? And what makes a standard a standard anyway? Speaker Biography Phil Archer Phil Archer is Data Activity Lead at W3C, the industry standards body for the World Wide Web, coordinating W3C's work in the Semantic Web and related technologies. He is most closely involved in the Data on the Web Best Practices, Permissions and Obligations Expression and Spatial Data on the Web Working Groups. His key themes are interoperability through common terminology and URI persistence. As well as work at the W3C, his career has encompassed broadcasting, teaching, linked data publishing, copy writing, and, perhaps incongruously, countryside conservation. The common thread throughout has been a knack for communication, particularly communicating complex technical ideas to a more general audience.

Top-K Query Processing in Edge-Labeled Graph Data

Edge-labeled graphs have proliferated rapidly over the last decade due to the increased popularity of social networks and the Semantic Web. In social networks, relationships between people are represented by edges and each edge is labeled with a semantic annotation. Hence, a huge single graph can express many different relationships between entities. The Semantic Web represents each single fragment of knowledge as a triple (subject, predicate, object), which is conceptually identical to an edge from subject to object labeled with predicates. A set of triples constitutes an edge-labeled graph on which knowledge inference is performed. Subgraph matching has been extensively used as a query language for patterns in the context of edge-labeled graphs. For example, in social networks, users can specify a subgraph matching query to find all people that have certain neighborhood relationships. Heavily used fragments of the SPARQL query language for the Semantic Web and graph queries of other graph DBMS can also be viewed as subgraph matching over large graphs. Though subgraph matching has been extensively studied as a query paradigm in the Semantic Web and in social networks, a user can get a large number of answers in response to a query. These answers can be shown to the user in accordance with an importance ranking. In this thesis proposal, we present four different scoring models along with scalable algorithms to find the top-k answers via a suite of intelligent pruning techniques. The suggested models consist of a practically important subset of the SPARQL query language augmented with some additional useful features. The first model called Substitution Importance Query (SIQ) identifies the top-k answers whose scores are calculated from matched vertices' properties in each answer in accordance with a user-specified notion of importance. The second model called Vertex Importance Query (VIQ) identifies important vertices in accordance with a user-defined scoring method that builds on top of various subgraphs articulated by the user. Approximate Importance Query (AIQ), our third model, allows partial and inexact matchings and returns top-k of them with a user-specified approximation terms and scoring functions. In the fourth model called Probabilistic Importance Query (PIQ), a query consists of several sub-blocks: one mandatory block that must be mapped and other blocks that can be opportunistically mapped. The probability is calculated from various aspects of answers such as the number of mapped blocks, vertices' properties in each block and so on and the most top-k probable answers are returned. An important distinguishing feature of our work is that we allow the user a huge amount of freedom in specifying: (i) what pattern and approximation he considers important, (ii) how to score answers - irrespective of whether they are vertices or substitution, and (iii) how to combine and aggregate scores generated by multiple patterns and/or multiple substitutions. Because so much power is given to the user, indexing is more challenging than in situations where additional restrictions are imposed on the queries the user can ask. The proposed algorithms for the first model can also be used for answering SPARQL queries with ORDER BY and LIMIT, and the method for the second model also works for SPARQL queries with GROUP BY, ORDER BY and LIMIT. We test our algorithms on multiple real-world graph databases, showing that our algorithms are far more efficient than popular triple stores.

Una aproximación para la descripción semántica de requisitos para categorización docentes de investigadores Ecuatorianos

La nueva generación de la Web, la Web Semántica, plantea potenciales oportunidades para dotar de significado a los contenidos Web. Las ontologías constituyen una de las principales herramientas para especificar explícitamente los conceptos de un dominio concreto, sus propiedades y sus relaciones; de manera que la información se publique en formatos que sean inteligibles automáticamente por agentes máquinas que pueden localizar y gestionar de forma precisa la información. En este trabajo se presenta un marco de trabajo para una red de ontologías para representar conceptos, atributos, operaciones y restricciones, en relación a los ítems curriculares que se usan en procesos nacionales de categorización de docentes universitarios ecuatorianos. En una primera parte se muestra el contexto del dominio, trabajos relacionados, luego se describe el proceso seguido, la abstracción del modelo ontológico y finalmente se presenta una ontología. Es una ontología de dominio debido a que proporciona el significado de los conceptos y sus relaciones dentro del dominio de ítems curriculares producidos por docentes universitarios, que son requisitos de los proceso de categorización docente universitaria en Ecuador.

Una aproximación basada en Linked Data para la detección de potenciales redes de colaboración científica a partir de la anotación semántica de producción científica: Piloto aplicado con producción científica de investigadores ecuatorianos

En este documento se propone un marco de trabajo basado en tecnologías de la Web Semántica para detectar potenciales redes de colaboración, mediante el enriquecimiento semántico de artículos científicos producidos por investigadores que publican con afiliaciones ecuatorianas. El marco de trabajo se describe a través de un ciclo de publicación de datos enlazados. Como alcance se consideraron publicaciones que tienen al menos un autor con afiliación ecuatoriana. Las redes de colaboración detectadas son un insumo importante para fortalecer los esfuerzos del gobierno ecuatoriano y las autoridades universitarias del país, priorizar los esfuerzos y recursos invertidos en investigación y determinar la pertinencia o coherencia de los programas de investigación.

RCOS: Real time context sharing across a fleet of smart mobile devices

Today, biodiversity is endangered by the currently applied intensive farming methods imposed on food producers by intermediate actors (e.g.: retailers). The lack of a direct communication technology between the food producer and the consumer creates dependency on the intermediate actors for both producers and the consumers. A tool allowing producers to directly and efficiently market produce that meets customer demands could greatly reduce the dependency enforced by intermediate actors. To this end, in this thesis, we propose, develop, implement and validate a Real Time Context Sharing (RCOS) system. RCOS takes advantage of the widely used publish/subscribe paradigm to exchange messages between producers and consumers, directly, according to their interest and context. Current systems follow a topic-based model or a content-based model. With RCOS, we propose a context-awareness approach into the matching process of publish/subscribe paradigm. Finally, as a proof of concept, we extend the Apache ActiveMQ Artemis software and create a client prototype. We evaluate our proof of concept for larger scale deployment.

RSLT: trasformazione di Open LinkedData in testi in linguaggio naturaletramite template dichiarativi

La diffusione del Semantic Web e di dati semantici in formato RDF, ha creato la necessità di un meccanismo di trasformazione di tali informazioni, semplici da interpretare per una macchina, in un linguaggio naturale, di facile comprensione per l'uomo. Nella dissertazione si discuterà delle soluzioni trovate in letteratura e, nel dettaglio, di RSLT, una libreria JavaScript che cerca di risolvere questo problema, consentendo la creazione di applicazioni web in grado di eseguire queste trasformazioni tramite template dichiarativi. Verranno illustrati, inoltre, tutti i cambiamenti e tutte le modi�che introdotte nella versione 1.1 della libreria, la cui nuova funzionalit�à principale �è il supporto a SPARQL 1.0.

Linked Data in the Digital Humanities: Examples, Projects, and Tools

Harnessing the potential of semantic web technologies to support and diversify scholarship is gaining popularity in the digital humanities. This talk describes a number of projects utilising Linked Data ranging from musicology and library metadata, to the representation of the narrative structure, philological, bibliographical, and museological data of ancient Mesopotamian literary compositions.

Healthcare Computer Reasoning Addressing Chronically Ill Societies Using IoT: Deep Learning AI to the Rescue of Home-Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare. These environments unite the chronical patient clinical history sematic representation with the ability of monitoring the living conditions and events recurring to a fully managed Semantic Web of Things (SWoT). Several levels of acquired knowledge and the case based reasoning that is possible by knowledge representation of the health-disease history and acquisition of the scientific evidence will deliver, through various voice based natural interfaces, the adequate support systems for disease auto management but prominently by activating the less differentiated caregiver for any specific need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators.

Digital 3D reconstruction as a research environment in art and architecture history: uncertainty classification and visualisation

The dissertation addresses the still not solved challenges concerned with the source-based digital 3D reconstruction, visualisation and documentation in the domain of archaeology, art and architecture history. The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation and documentation in the planning, construction and facility management process. The introduction and development of the Semantic Web (Web 3.0), spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering and cultural heritage, academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators. Since the 1990s, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. However, these standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and continue to fill the growing digital cemeteries. This study focuses, therefore, on the evaluation of the source-based digital 3D reconstructions and, especially, on uncertainty assessment in the case of hypothetical reconstructions of destroyed or never built artefacts according to scientific principles, making the models shareable and reusable by a potentially wide audience. The work initially focuses on terminology and on the definition of a workflow especially related to the classification and visualisation of uncertainty. The workflow is then applied to specific cases of 3D models uploaded to the DFG repository of the AI Mainz. In this way, the available methods of documenting, visualising and communicating uncertainty are analysed. In the end, this process will lead to a validation or a correction of the workflow and the initial assumptions, but also (dealing with different hypotheses) to a better definition of the levels of uncertainty.

Ethics in the flesh: formalizing moral values in embodied cognition

Values are beliefs or principles that are deemed significant or desirable within a specific society or culture, serving as the fundamental underpinnings for ethical and socio-behavioral norms. The objective of this research is to explore the domain encompassing moral, cultural, and individual values. To achieve this, we employ an ontological approach to formally represent the semantic relations within the value domain. The theoretical framework employed adopts Fillmore’s frame semantics, treating values as semantic frames. A value situation is thus characterized by the co-occurrence of specific semantic roles fulfilled within a given event or circumstance. Given the intricate semantics of values as abstract entities with high social capital, our investigation extends to two interconnected domains. The first domain is embodied cognition, specifically image schemas, which are cognitive patterns derived from sensorimotor experiences that shape our conceptualization of entities in the world. The second domain pertains to emotions, which are inherently intertwined with the realm of values. Consequently, our approach endeavors to formalize the semantics of values within an embodied cognition framework, recognizing values as emotional-laden semantic frames. The primary ontologies proposed in this work are: (i) ValueNet, an ontology network dedicated to the domain of values; (ii) ISAAC, the Image Schema Abstraction And Cognition ontology; and (iii) EmoNet, an ontology for theories of emotions. The knowledge formalization adheres to established modeling practices, including the reuse of semantic web resources such as WordNet, VerbNet, FrameNet, DBpedia, and alignment to foundational ontologies like DOLCE, as well as the utilization of Ontology Design Patterns. These ontological resources are operationalized through the development of a fully explainable frame-based detector capable of identifying values, emotions, and image schemas generating knowledge graphs from from natural language, leveraging the semantic dependencies of a sentence, and allowing non trivial higher layer knowledge inferences.

Esprimere incertezza, ipotesi, dubbi in RDF con le Congetture

La semantica di RDF non permette di esprimere punti di vista contraddittori sullo stesso set di dati. Il problema consiste sostanzialmente nell’impossibilità di esprimere, in RDF, affermazioni il cui valore di verità sia sconosciuto, oppure in contrasto con quello di altre affermazioni, senza però asserirle, poichè questo le renderebbe indubbiamente vere. Nel corso del tempo, partendo dalla necessità di esprimere statement su altri statement, sono stati prodotti diversi approcci, nessuno dei quali sembra dare una risposta convincente all’esigenza che potremmo riassumere nel poter esprimere senza asserire. Nel presente lavoro, dopo un'analisi dei differenti approcci al problema, e dei relativi risultati, verranno presentate le "Congetture": una nuova proposta di estensione di RDF 1.1 che permette l’espressione di grafi il cui valore di verità è sconosciuto. Le Congetture sono una notazione per esprimere, senza asserire, named graphs in RDF, unitamente ad un meccanismo per affermarne la verità chiamato "collasso alla realtà". Una Congettura collassata è allo stesso tempo un grafo congetturale e un grafo asserito, ed è un modo semplice per gestire situazioni che, espresse inizialmente sotto forma di congetture, devono successivamente essere considerare vere. La proposta è costruita attorno a due concetti principali: 1) la Congettura: un concetto il cui valore di verità non è disponibile; 2) il collasso alla realtà: un meccanismo per asserire pienamente, in RDF, quando necessario, il valore di verità della Congettura. Verranno analizzati scenari avanzati quali Congetture di Congetture, Congetture di collassi e collassi a cascata. Verrà delineata la semantica formale completa della proposta, estendendo la simple interpretation di RDF 1.1, dimostrando che le Congetture sono pienamente compatibili con RDF. Le Congetture, con un'estensione minima del modello, aggiungono ad RDF la possibilità di esprimere, senza asserire, incertezze, ipotesi e dubbi.

Benchmarking ’Expressing Without Asserting’ approaches on a large Wikidata dump

Semantic Web technologies provide the means to express the knowledge in a formal and standardized manner, enabling machines to automatically derive meaning from the data. Often this knowledge is uncertain or different degrees of certainty may be assigned to the same statements. This is the case in many fields of study such as in Digital Humanities, Science and Arts. The challenge relies on the fact that our knowledge about the surrounding world is dynamic and may evolve based on new data coming from the latest discoveries. Furthermore we should be able to express conflicting, debated or disputed statements in an efficient, effective and consistent way without the need of asserting them. We call this approach 'Expressing Without Asserting' (EWA). In this work we identify all existing methods that are compatible with actual Semantic Web standards and enable us to express EWA. In our research we were able to prove that existing reification methods such as Named Graphs, Singleton Properties, Wikidata Statements and RDF-Star are the most suitable methods to represent in a reliable way EWA. Next we compare these methods with our own method, namely Conjectures from a quantitative perspective. Our main objective was to put Conjectures into stress tests leveraging enormous datasets created ad hoc using art-related Wikidata dumps and measure the performance in various triplestores in relation with similar concurrent methods. Our experiments show that Conjectures are a formidable tool to express efficiently and effectively EWA. In some cases, Conjectures outperform state of the art methods such as singleton and Rdf-Star exposing their great potential. Is our firm belief that Conjectures represent a suitable solution to EWA issues. Conjectures in their weak form are fully compatible with Semantic Web standards, especially with RDF and SPARQL. Furthermore Conjectures benefit from comprehensive syntax and intuitive semantics that make them easy to learn and adapt.