Andrea Turbati

Semantic turkey: a browser-integrated environment for knowledge acquisition and management

With the continued growth of online semantic information, the processes of searching and managing this massive scale and heterogeneous content have become increasingly challenging. In this work, we present PowerAqua, an ontologybased Question Answering system that is able to answer queries by locating and integrating information, which can be distributed across heterogeneous semantic resources. We provide a complete overview of the system including: the research challenges that it addresses, its architecture, the evaluations that have been conducted to test it, and an in-depth discussion showing how PowerAqua effectively supports users in querying and exploring Semantic Web content.Born four years ago as a Semantic Web extension for the web browser Firefox, Semantic Turkey pushed forward the traditional concept of links&folders-based bookmarking to a new dimension, allowing users to keep track of relevant information from visited web sites and to organize the collected content according to standard or personally defined ontologies. Today, the tool has broken the boundaries of its original intents and can be considered, under every aspect, an extensible platform for knowledge management and acquisition. The semantic bookmarking and annotation facilities of Semantic Turkey are now supporting just a part of a whole methodology where different actors, from domain experts to knowledge engineers, can cooperate in developing, building and populating ontologies while navigating the Web.

VocBench: A Web Application for Collaborative Development of Multilingual Thesauri

We introduce VocBench, an open source web application for editing thesauri complying with the SKOS and SKOS-XL standards. VocBench has a strong focus on collaboration, supported by workflow management for content validation and publication. Dedicated user roles provide a clean separation of competences, addressing different specificities ranging from management aspects to vertical competences on content editing, such as conceptualization versus terminology editing. Extensive support for scheme management allows editors to fully exploit the possibilities of the SKOS model, as well as to fulfill its integrity constraints. We discuss thoroughly the main features of VocBench, detail its architecture, and evaluate it under both a functional and user-appreciation ground, through a comparison with state-of-the-art and user questionnaires analysis, respectively. Finally, we provide insights on future developments.

CODA: computer-aided ontology development architecture

This paper introduces CODA (Computer-aided Ontology Development Architecture), which is both an architecture and an associated framework supporting the transformation of unstructured and semi-structured content into RDF (Resource Description Framework) datasets. The purpose of CODA is to support the entire process that ranges from data extraction and transformation to identity resolution. The final objective is to feed semantic repositories with knowledge extracted from unstructured content. The motivation behind CODA lies in the large effort and design issues required for developing knowledge acquisition systems using content analytics frameworks such as UIMA™ (Unstructured Information Management Architecture) and GATE (General Architecture for Text Engineering). Therefore, CODA extends UIMA with facilities and a powerful language for projection and transformation of UIMA-annotated content into RDF. The proposed platform is oriented towards a wide range of beneficiaries, from semantic applications developers to final users that can easily plug CODA components into compliant desktop tools. We describe and discuss the features of CODA through the article, and we conclude by reporting on the adoption of the CODA framework in the context of a usage scenario, related to knowledge acquisition in the agricultural domain.

An Architecture for Data and Knowledge Acquisition for the Semantic Web: The AGROVOC Use Case

We are surrounded by ever growing volumes of unstructured and weakly-structured information, and for a human being, domain expert or not, it is nearly impossible to read, understand and categorize such information in a fair amount of time. Moreover, different user categories have different expectations: final users need easy-to-use tools and services for specific tasks, knowledge engineers require robust tools for knowledge acquisition, knowledge categorization and semantic resources development, while semantic applications developers demand for flexible frameworks for fast and easy, standardized development of complex applications. This work represents an experience report on the use of the CODA framework for rapid prototyping and deployment of knowledge acquisition systems for RDF. The system integrates independent NLP tools and custom libraries complying with UIMA standards. For our experiment a document set has been processed to populate the AGROVOC thesaurus with two new relationships.

Sheet2RDF: a Flexible and Dynamic Spreadsheet Import&Lifting Framework for RDF

In this paper, we introduce Sheet2RDF, a platform for the acquisition and transformation of spreadsheets into RDF datasets. Based on Apache UIMA and CODA, two wider-scoped frameworks respectively aimed at knowledge acquisition from unstructured information and RDF triplification, Sheet2RDF narrows down their capabilities in order to restrict the domain of acquisition to spreadsheets, thus taking into consideration their peculiarities and providing informed solutions facilitating the transformation process, while still exploiting their full potentialities. Sheet2RDF comes also bundled in the form of a plugin for two RDF management platforms: Semantic Turkey and VocBench. The integration with such platforms enhances the level of automatism in the process, thanks to a human-computer interface that can exploit suggestions by users and translate them into proper transformation rules. In addition, it strengthens this interaction by direct contact with the data/vocabularies edited in the platform.

A Suite of Semantic Web Tools Supporting Development of Multilingual Ontologies

The multilingual aspects which characterize the (Semantic) Web and the constant demand for more understandable and easy-to-share forms of knowledge representation, push for a more “linguistically aware” approach to ontology development and foresees an environment where formal semantics could coexist with natural language, contributing to improve “shareability” of the content they describe. As a consequence ontologies should be enriched to both cover formally expressed conceptual knowledge as well as to expose content in a linguistically motivated fashion. In this paper we present a suite of tools, libraries and ontolo-gies, ranging from ontology development to language resources access and man-agement, supporting the development of multilingual ontologies. The contribution of this work, going beyond mere tool presentation, is two-fold: the presented tools implicitly embody a new way (methodology?) of rethinking the development of ontologies in terms of making their content easy reusable and comprehensible; moreover, they represent living proofs of software engineering principles asso-ciated to software reuse, documentation, modularity, interaction analysis, applied to the domain of Knowledge Management Software.

Semi-automatic Knowledge Acquisition through CODA

In this paper, we illustrate the benefits deriving from the adoption of CODA Computer-aided Ontology Development Architecture for the semi-automatic acquisition of knowledge from unstructured information. Based on UIMA for the orchestration of analytics, CODA promotes the reuse of independently developed information extractors, while providing dedicated capabilities for projecting their output as RDF triples conforming to a user provided vocabulary. CODA introduces a clear workflow for the coordination of concurrently working teams through the incremental definition of a limited number of shared interfaces. In the proposed semi-automatic knowledge acquisition process, humans can validate the automatically produced triples, or refine them to increase their relevance to a specific domain model. An experimental user interface tries to raise efficiency and effectiveness of human involvement. For instance, candidate refinements are provided based on metadata about the triples to be refined, and the already assessed knowledge in the target semantic repository.

GENOMA: GENeric Ontology Matching Architecture

Even though a few architectures exist to support the difficult ontology matching task, it happens often they are not reconfigurable (or just a little) related to both ontology features and applications needs.

ART Lab infrastructure for semantic Big Data processing

In this paper we briefly describe the ART Lab infrastructure for semantic Big Bata processing. Our most relevant contribution is the definition of an architecture supporting ontology development driven by knowledge acquired from heterogeneous resources, such as documents and web pages. The overall perspective is to propose a gluing architecture driving and supporting the entire flow of information, from data acquisition from external heterogeneous resources to their exploitation for RDF triplification. In such an architecture, the unstructured content analysis capabilities of frameworks such as UIMA are integrated in a coordinated environment supporting the processing, transformation and projection of produced metadata into RDF semantic repositories, which are managed by Semantic Turkey, our platform for Knowledge Acquisition and Management. Further contributions relate to the possibility of easily managing high dimension repositories (e.g., thesauri, vocabularies, etc.), and supporting end users for sharing the “logics” under the reasoning processes!

Version Control and Change Validation for RDF Datasets

The dynamic and distributed nature of the Semantic Web demands for methodologies and systems fostering collective participation to the evolution of datasets. In collaborative and iterative processes for dataset development, it is important to keep track of individual changes for provenance. Different scenarios may require mechanisms to foster consensus, resolve conflicts between competing changes, reversing or ignoring changes etc. In this paper, we perform a landscape analysis of version control for RDF datasets, emphasizing the importance of change reversion to support validation. Firstly, we discuss different representations of changes in RDF datasets and introduce higher-level perspectives on change. Secondly, we analyze diverse approaches to version control. We conclude by focusing on validation, characterizing it as a separate need from the mere preservation of different versions of a dataset.