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.

Improving search engines with open Web-based SKOS vocabularies

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Consumer search of the web: An exploratory analysis of antecedents of web search satisfaction

This article explores consumer Web-search satisfaction. It commences with a brief overview of the concepts consumer information search and consumer satisfaction. Consumer Web adoption issues are then briefly discussed and the importance of consumer search satisfaction is highlighted in relation to the adoption of the Web as an additional source of consumer information. Research hypotheses are developed and the methodology of a large scale consumer experiment to record consumer Web search behaviour is described. The hypotheses are tested and the data explored in relation to post-Web-search satisfaction. The results suggest that consumer post-Web-search satisfaction judgments may be derived from subconscious judgments of Web search efficiency, an empirical calculation of which is problematic in unlimited information environments such as the Web. The results are discussed and a future research agenda is briefly outlined.

BIOMedical search engine framework: lightweight and customized implementation of domain-specific biomedical search engines

The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef

Search Interfaces on the Web: Querying and Characterizing

Current-day web search engines (e.g., Google) do not crawl and index a significant portion of theWeb and, hence, web users relying on search engines only are unable to discover and access a large amount of information from the non-indexable part of the Web. Specifically, dynamic pages generated based on parameters provided by a user via web search forms (or search interfaces) are not indexed by search engines and cannot be found in searchers’ results. Such search interfaces provide web users with an online access to myriads of databases on the Web. In order to obtain some information from a web database of interest, a user issues his/her query by specifying query terms in a search form and receives the query results, a set of dynamic pages that embed required information from a database. At the same time, issuing a query via an arbitrary search interface is an extremely complex task for any kind of automatic agents including web crawlers, which, at least up to the present day, do not even attempt to pass through web forms on a large scale. In this thesis, our primary and key object of study is a huge portion of the Web (hereafter referred as the deep Web) hidden behind web search interfaces. We concentrate on three classes of problems around the deep Web: characterization of deep Web, finding and classifying deep web resources, and querying web databases. Characterizing deep Web: Though the term deep Web was coined in 2000, which is sufficiently long ago for any web-related concept/technology, we still do not know many important characteristics of the deep Web. Another matter of concern is that surveys of the deep Web existing so far are predominantly based on study of deep web sites in English. One can then expect that findings from these surveys may be biased, especially owing to a steady increase in non-English web content. In this way, surveying of national segments of the deep Web is of interest not only to national communities but to the whole web community as well. In this thesis, we propose two new methods for estimating the main parameters of deep Web. We use the suggested methods to estimate the scale of one specific national segment of the Web and report our findings. We also build and make publicly available a dataset describing more than 200 web databases from the national segment of the Web. Finding deep web resources: The deep Web has been growing at a very fast pace. It has been estimated that there are hundred thousands of deep web sites. Due to the huge volume of information in the deep Web, there has been a significant interest to approaches that allow users and computer applications to leverage this information. Most approaches assumed that search interfaces to web databases of interest are already discovered and known to query systems. However, such assumptions do not hold true mostly because of the large scale of the deep Web – indeed, for any given domain of interest there are too many web databases with relevant content. Thus, the ability to locate search interfaces to web databases becomes a key requirement for any application accessing the deep Web. In this thesis, we describe the architecture of the I-Crawler, a system for finding and classifying search interfaces. Specifically, the I-Crawler is intentionally designed to be used in deepWeb characterization studies and for constructing directories of deep web resources. Unlike almost all other approaches to the deep Web existing so far, the I-Crawler is able to recognize and analyze JavaScript-rich and non-HTML searchable forms. Querying web databases: Retrieving information by filling out web search forms is a typical task for a web user. This is all the more so as interfaces of conventional search engines are also web forms. At present, a user needs to manually provide input values to search interfaces and then extract required data from the pages with results. The manual filling out forms is not feasible and cumbersome in cases of complex queries but such kind of queries are essential for many web searches especially in the area of e-commerce. In this way, the automation of querying and retrieving data behind search interfaces is desirable and essential for such tasks as building domain-independent deep web crawlers and automated web agents, searching for domain-specific information (vertical search engines), and for extraction and integration of information from various deep web resources. We present a data model for representing search interfaces and discuss techniques for extracting field labels, client-side scripts and structured data from HTML pages. We also describe a representation of result pages and discuss how to extract and store results of form queries. Besides, we present a user-friendly and expressive form query language that allows one to retrieve information behind search interfaces and extract useful data from the result pages based on specified conditions. We implement a prototype system for querying web databases and describe its architecture and components design.

OpenSearch-geo: the simple standard for geographic web search engines

When publishing information on the web, one expects it to reach all the people that could be interested in. This is mainly achieved with general purpose indexing and search engines like Google which is the most used today. In the particular case of geographic information (GI) domain, exposing content to mainstream search engines is a complex task that needs specific actions. In many occasions it is convenient to provide a web site with a specially tailored search engine. Such is the case for on-line dictionaries (wikipedia, wordreference), stores (amazon, ebay), and generally all those holding thematic databases. Due to proliferation of these engines, A9.com proposed a standard interface called OpenSearch, used by modern web browsers to manage custom search engines. Geographic information can also benefit from the use of specific search engines. We can distinguish between two main approaches in GI retrieval information efforts: Classical OGC standardization on one hand (CSW, WFS filters), which are very complex for the mainstream user, and on the other hand the neogeographer’s approach, usually in the form of specific APIs lacking a common query interface and standard geographic formats. A draft ‘geo’ extension for OpenSearch has been proposed. It adds geographic filtering for queries and recommends a set of simple standard response geographic formats, such as KML, Atom and GeoRSS. This proposal enables standardization while keeping simplicity, thus covering a wide range of use cases, in both OGC and the neogeography paradigms. In this article we will analyze the OpenSearch geo extension in detail and its use cases, demonstrating its applicability to both the SDI and the geoweb. Open source implementations will be presented as well

Information sources selection methodology for recommender systems based on intrinsic characteristics and trust measure

El treball desenvolupat en aquesta tesi presenta un profund estudi i proveïx solucions innovadores en el camp dels sistemes recomanadors. Els mètodes que usen aquests sistemes per a realitzar les recomanacions, mètodes com el Filtrat Basat en Continguts (FBC), el Filtrat Col·laboratiu (FC) i el Filtrat Basat en Coneixement (FBC), requereixen informació dels usuaris per a predir les preferències per certs productes. Aquesta informació pot ser demogràfica (Gènere, edat, adreça, etc), o avaluacions donades sobre algun producte que van comprar en el passat o informació sobre els seus interessos. Existeixen dues formes d'obtenir aquesta informació: els usuaris ofereixen explícitament aquesta informació o el sistema pot adquirir la informació implícita disponible en les transaccions o historial de recerca dels usuaris. Per exemple, el sistema recomanador de pel·lícules MovieLens (http://movielens.umn.edu/login) demana als usuaris que avaluïn almenys 15 pel·lícules dintre d'una escala de * a * * * * * (horrible, ...., ha de ser vista). El sistema genera recomanacions sobre la base d'aquestes avaluacions. Quan els usuaris no estan registrat en el sistema i aquest no té informació d'ells, alguns sistemes realitzen les recomanacions tenint en compte l'historial de navegació. Amazon.com (http://www.amazon.com) realitza les recomanacions tenint en compte les recerques que un usuari a fet o recomana el producte més venut. No obstant això, aquests sistemes pateixen de certa falta d'informació. Aquest problema és generalment resolt amb l'adquisició d'informació addicional, se li pregunta als usuaris sobre els seus interessos o es cerca aquesta informació en fonts addicionals. La solució proposada en aquesta tesi és buscar aquesta informació en diverses fonts, específicament aquelles que contenen informació implícita sobre les preferències dels usuaris. Aquestes fonts poden ser estructurades com les bases de dades amb informació de compres o poden ser no estructurades com les pàgines web on els usuaris deixen la seva opinió sobre algun producte que van comprar o posseïxen. Nosaltres trobem tres problemes fonamentals per a aconseguir aquest objectiu: 1 . La identificació de fonts amb informació idònia per als sistemes recomanadors. 2 . La definició de criteris que permetin la comparança i selecció de les fonts més idònies. 3 . La recuperació d'informació de fonts no estructurades. En aquest sentit, en la tesi proposada s'ha desenvolupat: 1 . Una metodologia que permet la identificació i selecció de les fonts més idònies. Criteris basats en les característiques de les fonts i una mesura de confiança han estat utilitzats per a resoldre el problema de la identificació i selecció de les fonts. 2 . Un mecanisme per a recuperar la informació no estructurada dels usuaris disponible en la web. Tècniques de Text Mining i ontologies s'han utilitzat per a extreure informació i estructurar-la apropiadament perquè la utilitzin els recomanadors. Les contribucions del treball desenvolupat en aquesta tesi doctoral són: 1. Definició d'un conjunt de característiques per a classificar fonts rellevants per als sistemes recomanadors 2. Desenvolupament d'una mesura de rellevància de les fonts calculada sobre la base de les característiques definides 3. Aplicació d'una mesura de confiança per a obtenir les fonts més fiables. La confiança es definida des de la perspectiva de millora de la recomanació, una font fiable és aquella que permet millorar les recomanacions. 4. Desenvolupament d'un algorisme per a seleccionar, des d'un conjunt de fonts possibles, les més rellevants i fiable utilitzant les mitjanes esmentades en els punts previs. 5. Definició d'una ontologia per a estructurar la informació sobre les preferències dels usuaris que estan disponibles en Internet. 6. Creació d'un procés de mapatge que extreu automàticament informació de les preferències dels usuaris disponibles en la web i posa aquesta informació dintre de l'ontologia. Aquestes contribucions permeten aconseguir dos objectius importants: 1 . Millorament de les recomanacions usant fonts d'informació alternatives que sigui rellevants i fiables. 2 . Obtenir informació implícita dels usuaris disponible en Internet.

Search adaptations and the challenges of the web

Search has become a hot topic in Internet computing, with rival search engines battling to become the de facto Web portal, harnessing search algorithms to wade through information on a scale undreamed of by early information retrieval (IR) pioneers. This article examines how search has matured from its roots in specialized IR systems to become a key foundation of the Web. The authors describe new challenges posed by the Web's scale, and show how search is changing the nature of the Web as much as the Web has changed the nature of search

Collaborative personal profiling for web service ranking and recommendation

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Web Scale Discovery Systems – How to assess the best solution

The University of São Paulo has been experiencing the increase in contents in electronic and digital formats, distributed by different suppliers and hosted remotely or in clouds, and is faced with the also increasing difficulties related to facilitating access to this digital collection by its users besides coexisting with the traditional world of physical collections. A possible solution was identified in the new generation of systems called Web Scale Discovery, which allow better management, data integration and agility of search. Aiming to identify if and how such a system would meet the USP demand and expectation and, in case it does, to identify what the analysis criteria of such a tool would be, an analytical study with an essentially documental base was structured, as from a revision of the literature and from data available in official websites and of libraries using this kind of resources. The conceptual base of the study was defined after the identification of software assessment methods already available, generating a standard with 40 analysis criteria, from details on the unique access interface to information contents, web 2.0 characteristics, intuitive interface, facet navigation, among others. The details of the studies conducted into four of the major systems currently available in this software category are presented, providing subsidies for the decision-making of other libraries interested in such systems.

Arquitectura de sitios Web de bibliotecas universitarias : El sistema de bibliotecas de la Universidad Nacional de La Plata, Argentina

Este trabajo descriptivo exploratorio se propone analizar la arquitectura de información (AI) de sitios Web de bibliotecas de la Universidad Nacional de La Plata (UNLP), Argentina. Se analizaron 17 bibliotecas y se aplicó una grilla para recabar 10 aspectos relevantes. Los resultados fueron: 1. Ubicación del sitio Web de la biblioteca: 9 sitios incluidos en la página principal de la facultad. 2. Etiquetado de contenidos: terminología simple, sin jergas; no hay homogeneidad entre las bibliotecas. 3. Capacidad de búsqueda: 62 por ciento positiva, 38 por ciento negativa. 4. Sistema de búsqueda: simple 43 por ciento, compleja 10 por ciento, con ayudas 10 por ciento, ninguno 38 por ciento. 5. Sistemas de navegación: globales 5 por ciento, jerárquicos 79 por ciento, locales 5 por ciento, ninguno 11 por ciento. 6. Herramientas de navegación: barras 16 por ciento, frames o marcos 30 por ciento, índices 2 por ciento, mapas de sitio 7 por ciento, menús horizontales 9 por ciento, menús verticales 35 por ciento. 7. Sindicación de contenidos RSS: 3 sitios. 8. Otros servicios: chat 7 por ciento, descarga de documentos 16 por ciento, envío de formularios 14 por ciento, instructivos 21 por ciento, links a otras páginas 23 por ciento, tutoriales 5 por ciento, otros 14 por ciento. 9. Accesibilidad Web: 1 sitio. 10. Otras observaciones: ninguna. Se concluye que el desarrollo de los sitios es dispar y se recomienda considerar pautas de AI como parte de la cooperación en la red de bibliotecas de la UNLP

Arquitectura de sitios Web de bibliotecas universitarias : El sistema de bibliotecas de la Universidad Nacional de La Plata, Argentina

Este trabajo descriptivo exploratorio se propone analizar la arquitectura de información (AI) de sitios Web de bibliotecas de la Universidad Nacional de La Plata (UNLP), Argentina. Se analizaron 17 bibliotecas y se aplicó una grilla para recabar 10 aspectos relevantes. Los resultados fueron: 1. Ubicación del sitio Web de la biblioteca: 9 sitios incluidos en la página principal de la facultad. 2. Etiquetado de contenidos: terminología simple, sin jergas; no hay homogeneidad entre las bibliotecas. 3. Capacidad de búsqueda: 62 por ciento positiva, 38 por ciento negativa. 4. Sistema de búsqueda: simple 43 por ciento, compleja 10 por ciento, con ayudas 10 por ciento, ninguno 38 por ciento. 5. Sistemas de navegación: globales 5 por ciento, jerárquicos 79 por ciento, locales 5 por ciento, ninguno 11 por ciento. 6. Herramientas de navegación: barras 16 por ciento, frames o marcos 30 por ciento, índices 2 por ciento, mapas de sitio 7 por ciento, menús horizontales 9 por ciento, menús verticales 35 por ciento. 7. Sindicación de contenidos RSS: 3 sitios. 8. Otros servicios: chat 7 por ciento, descarga de documentos 16 por ciento, envío de formularios 14 por ciento, instructivos 21 por ciento, links a otras páginas 23 por ciento, tutoriales 5 por ciento, otros 14 por ciento. 9. Accesibilidad Web: 1 sitio. 10. Otras observaciones: ninguna. Se concluye que el desarrollo de los sitios es dispar y se recomienda considerar pautas de AI como parte de la cooperación en la red de bibliotecas de la UNLP