Collaborative semantic web browsing with Magpie

Web browsing is often a collaborative activity. Users involved in a joint information gathering exercise will wish to share knowledge about the web pages visited and the contents found. Magpie is a suite of tools supporting the interpretation of web pages and semantically enriched web browsing. By automatically associating an ontology-based semantic layer to web resources, Magpie allows relevant services to be invoked as well as remotely triggered within a standard web browser. In this paper we describe how Magpie trigger services can provide semantic support to collaborative browsing activities

Magpie: towards a semantic web browser

Web browsing involves two tasks: finding the right web page and then making sense of its content. So far, research has focused on supporting the task of finding web resources through ‘standard’ information retrieval mechanisms, or semantics-enhanced search. Much less attention has been paid to the second problem. In this paper we describe Magpie, a tool which supports the interpretation of web pages. Magpie offers complementary knowledge sources, which a reader can call upon to quickly gain access to any background knowledge relevant to a web resource. Magpie automatically associates an ontologybased semantic layer to web resources, allowing relevant services to be invoked within a standard web browser. Hence, Magpie may be seen as a step towards a semantic web browser. The functionality of Magpie is illustrated using examples of how it has been integrated with our lab’s web resources

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive

A visual analytics platform for competitive intelligence

Silva, D., & Bação, F. (2023). MapIntel: A visual analytics platform for competitive intelligence. Expert Systems, [e13445]. https://doi.org/https://www.authorea.com/doi/full/10.22541/au.166785335.50477185, https://doi.org/10.1111/exsy.13445 --- Funding Information: This work was supported by the (research grant under the DSAIPA/DS/0116/2019 project). Fundação para a Ciência e Tecnologia of Ministério da Ciência e Tecnologia e Ensino SuperiorCompetitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.preprintauthorsversionepub_ahead_of_prin

Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves

Using Linked Data for Prosopographical Research of Historical Persons : Case U.S. Congress Legislators

This paper shows how biographical registries can be represented as Linked Data, enriched by data linking to related data sources, and used in Digital Humanities. As a use case, a database of 11 987 historical U.S. Congress Legislators in 1789–2018 was transformed into a knowledge graph. The data was published as a Linked Data service, including a SPARQL endpoint, on top of which tools for biographical and prosopographical research are implemented. A faceted browser named U.S. Congress Prosopographer with visualization tools for knowledge discovery is presented to provide new insights in political history.Peer reviewe

DART: the distributed agent based retrieval toolkit

The technology of search engines is evolving from indexing and classification of web resources based on keywords to more sophisticated techniques which take into account the meaning and the context of textual information and usage. Replying to query, commercial search engines face the user requests with a large amount of results, mostly useless or only partially related to the request; the subsequent refinement, operated downloading and examining as much pages as possible and simply ignoring whatever stays behind the first few pages, is left up to the user. Furthermore, architectures based on centralized indexes, allow commercial search engines to control the advertisement of online information, in contrast to P2P architectures that focus the attention on user requirements involving the end user in search engine maintenance and operation. To address such wishes, new search engines should focus on three key aspects: semantics, geo-referencing, collaboration/distribution. Semantic analysis lets to increase the results relevance. The geo-referencing of catalogued resources allows contextualisation based on user position. Collaboration distributes storage, processing, and trust on a world-wide network of nodes running on users’ computers, getting rid of bottlenecks and central points of failures. In this paper, we describe the studies, the concepts and the solutions developed in the DART project to introduce these three key features in a novel search engine architecture