Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

Unifying context with labeled property graph: A pipeline-based system for comprehensive text representation in NLP

Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed approach introduces specialized patterns and algorithms to enhance specific NLP tasks, including nominal mention detection, named entity disambiguation, event enrichments, event participant detection, and temporal link detection. The evaluation of the proposed approach, using the MEANTIME corpus comprising manually annotated documents, provides encouraging results and valuable insights into the system\u27s strengths. The proposed pipeline-based framework serves as a solid foundation for future research, aiming to refine and optimize LPG-based graph structures to generate comprehensive and semantically rich text representations, addressing the challenges associated with efficient information extraction and analysis in NLP

Human exploration of complex knowledge spaces

Driven by need or curiosity, as humans we constantly act as information seekers. Whenever we work, study, play, we naturally look for information in spaces where pieces of our knowledge and culture are linked through semantic and logic relations. Nowadays, far from being just an abstraction, these information spaces are complex structures widespread and easily accessible via techno-systems: from the whole World Wide Web to the paramount example of Wikipedia. They are all information networks. How we move on these networks and how our learning experience could be made more efficient while exploring them are the key questions investigated in the present thesis. To this end concepts, tools and models from graph theory and complex systems analysis are borrowed to combine empirical observations of real behaviours of users in knowledge spaces with some theoretical findings of cognitive science research. It is investigated how the knowledge space structure can affect its own exploration in learning-type tasks, and how users do typically explore the information networks, when looking for information or following some learning paths. The research approach followed is exploratory and moves along three main lines of research. Enlarging a previous work in algorithmic education, the first contribution focuses on the topological properties of the information network and how they affect the \emph{efficiency} of a simulated learning exploration. To this end a general class of algorithms is introduced that, standing on well-established findings on educational scheduling, captures some of the behaviours of an individual moving in a knowledge space while learning. In exploring this space, learners move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. To investigate the effect of networked information structures on the dynamics, both synthetic and real-world graphs are considered, such as subsections of Wikipedia and word-association graphs. The existence is revealed of optimal topological structures for the defined learning dynamics. They feature small-world and scale-free properties with a balance between the number of hubs and of the least connected items. Surprisingly the real-world networks analysed turn out to be close to optimality. To uncover the role of semantic content of the bit of information to be learned in a information-seeking tasks, empirical data on user traffic logs in the Wikipedia system are then considered. From these, and by means of first-order Markov chain models, some users paths over the encyclopaedia can be simulated and treated as proxies for the real paths. They are then analysed in an abstract semantic level, by mapping the individual pages into points of a semantic reduced space. Recurrent patterns along the walks emerge, even more evident when contrasted with paths originated in information-seeking goal oriented games, thus providing some hints about the unconstrained navigation of users while seeking for information. Still, different systems need to be considered to evaluate longer and more constrained and structured learning dynamics. This is the focus of the third line of investigation, in which learning paths are extracted from advances scientific textbooks and treated as they were walks suggested by their authors throughout an underlying knowledge space. Strategies to extract the paths from the textbooks are proposed, and some preliminary results are discussed on their statistical properties. Moreover, by taking advantages of the Wikipedia information network, the Kauffman theory of adjacent possible is formalized in a learning context, thus introducing the adjacent learnable to refer to the part of the knowledge space explorable by the reader as she learns new concepts by following the suggested learning path. Along this perspective, the paths are analysed as particular realizations of the knowledge space explorations, thus allowing to quantitatively contrast different approaches to education

The process of building the upper-level hierarchy for the aircraft structure ontology to be integrated in FunGramKB

In this article we collect a corpus of texts which operate with a controlled language (ASD Simplified Technical English) in order to facilitate the development of a new domain-specific ontology (the aircraft structure) based on a technical discipline (aeronautical engineering) included in the so called “hard” sciences. This new repository should be compatible with the Core Ontology and the corresponding English Lexicon in FunGramKB (a multipurpose lexico-conceptual knowledge base for natural language processing (NLP)), and, in the same vein, should eventually give support to aircraft maintenance management systems. By contrast, in previous approaches we applied a stepwise methodology for the construction of a domain-specific subontology compatible with FunGramKB systems in criminal law, but the high occurrence of terminological banalisation and the scarce number of specific terms, due to the social nature of the discipline, were added problems to the most common NLP difficulties (polysemy and ambiguity). Taking into consideration previous results and the complexity of this task, here we only intend to take the first step towards the modelling of the aircraft ontology: the development of its taxonomic hierarchy. Consequently, the hierarchy starts with the whole system (i.e., an aircraft) and follows the traditional decomposition of the system down to the elementary components (top-down approach). At the same time, we have collected a corpus of 2,480 files of aircraft maintenance instructions, courtesy of Airbus in Seville. For the bottom-up approach (under construction), we consult specialised references end explore the corpus through the identification and extraction of term candidates with DEXTER, an online multilingual workbench especially designed for the discovery and extraction of terms

Community-Driven Engineering of the DBpedia Infobox Ontology and DBpedia Live Extraction

The DBpedia project aims at extracting information based on semi-structured data present in Wikipedia articles, interlinking it with other knowledge bases, and publishing this information as RDF freely on the Web. So far, the DBpedia project has succeeded in creating one of the largest knowledge bases on the Data Web, which is used in many applications and research prototypes. However, the manual effort required to produce and publish a new version of the dataset – which was already partially outdated the moment it was released – has been a drawback. Additionally, the maintenance of the DBpedia Ontology, an ontology serving as a structural backbone for the extracted data, made the release cycles even more heavyweight. In the course of this thesis, we make two contributions: Firstly, we develop a wiki-based solution for maintaining the DBpedia Ontology. By allowing anyone to edit, we aim to distribute the maintenance work among the DBpedia community. Secondly, we extend DBpedia with a Live Extraction Framework, which is capable of extracting RDF data from articles that have recently been edited on the English Wikipedia. By making this RDF data automatically public in near realtime, namely via SPARQL and Linked Data, we overcome many of the drawbacks of the former release cycles

A hybrid quantum approach to leveraging data from HTML tables

The Web provides many data that are encoded using HTML tables. This facilitates rendering them, but obfuscates their structure and makes it difficult for automated business processes to leverage them. This has motivated many authors to work on proposals to extract them as automatically as possible. In this article, we present a new unsupervised proposal that uses a hybrid approach in which a standard computer is used to perform pre and post-processing tasks and a quantum computer is used to perform the core task: guessing whether the cells have labels or values. The problem is addressed using a clustering approach that is known to be NP using standard computers, but our proposal can solve it in polynomial time, which implies a significant performance improvement. It is novel in that it relies on an entropy-preservation metaphor that has proven to work very well on two large collections of real-world tables from the Wikipedia and the Dresden Web Table Corpus. Our experiments prove that our proposal can beat the state-of-the-art proposal in terms of both effectiveness and efficiency; the key difference is that our proposal is totally unsupervised, whereas the state-of-the-art proposal is supervised.Ministerio de Economía y Competitividad TIN2016-75394-RMinisterio de Ciencia e Innovación PID2020-112540RB-C44Junta de Andalucía P18-RT-106

Web Mediators for Accessible Browsing

We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. K-means clustering is used to create unique thresholds to differentiate index pages and article pages on individual web sites. Index pages contain mostly links to articles and other indices, while article pages contain mostly text. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface, which shows that we can vastly improve the rate of communication for users with disabilities. We used web page classification and link grouping to alter web page display on an accessible web browser that we developed to make a usable browsing interface for users with disabilities. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0% on web sites with well-formed or slightly malformed HTML, compared with 80.1% accuracy for the baseline classifier.National Science Foundation (IIS-0308213, IIS-039009, IIS-0093367, P200A01031, EIA-0202067

Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization

In the past decades, massive efforts involving companies, non-profit organizations, governments, and others have been put into supporting the concept of data democratization, promoting initiatives to educate people to confront information with data. Although this represents one of the most critical advances in our free world, access to data without concrete facts to check or the lack of an expert to help on understanding the existing patterns hampers its intrinsic value and lessens its democratization. So the benefits of giving full access to data will only be impactful if we go a step further and support the Data Analytics Democratization, assisting users in transforming findings into insights without the need of domain experts to promote unconstrained access to data interpretation and verification. In this paper, we present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings, automatically generating plausible explanations for observed or selected findings using an external (textual) source of information, avoiding or reducing the need for domain experts. ExPatt applicability is confirmed via different use-cases involving world demographics indicators and Wikipedia as an external source of explanations, showing how it can be used in practice towards the data analytics democratization.Comment: 8 Figures, 10 pages, submitted to VIS 202