Towards VocBench 3: Pushing collaborative development of thesauri and ontologies further beyond

More than three years have passed since the release of the second edition of VocBench, an open source collaborative web platform for the development of thesauri complying with Semantic Web standards. In these years, a vibrant user community has gathered around the system, consisting of public organizations, companies and independent users looking for open source solutions for maintaining their thesauri, code lists and authority resources. The focus on collaboration, the differentiation of user roles and the workflow management for content validation and publication have been the strengths of the platform, especially for those organizations requiring a centralized and controlled publication environment. Now the time has come to widen the scope of the platform: funded by the ISA2programme of the European Commission, VocBench 3 will offer a general-purpose collaborative environment for development of any kind of RDF dataset, improving the editing capabilities of its predecessor, while still maintaining the peculiar aspects that determined its success. In this paper, we review the requirements and the new objectives set for version 3, and then introduce the new characteristics that were implemented for this next iteration of 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. © 2010 Springer-Verlag Berlin Heidelberg

Let Some Unforeseen Knowledge Emerge from Heterogeneous Documents

Data production and exchange on the Web grows at a frenetic speed. Such uncontrolled and exponential growth pushes for new researches in the area of information extraction as it is of great interest and can be obtained by processing data gathered from several heterogeneous sources. While some extracted facts can be correct at the origin, it is not possible to verify that correlations among the mare always true (e.g., they can relate to different points of time). We need systems smart enough to separate signal from noise and hence extract real value from this abundance of content accessible on the Web. In order to extract information from heterogeneous sources, we are involved into the entire process of identifying specific facts/events of interest. We propose a gluing architecture, driving the whole knowledge acquisition process, from data acquisition from external heterogeneous resources to their exploitation for RDF trip lification to support reasoning tasks. Once the extraction process is completed, a dedicated reasoner can infer new knowledge as a result of the reasoning process defined by the end user by means of specific inference rules over both extracted information and the background knowledge. The end user is supported in this context with an intelligent interface allowing to visualize either specific data/concepts, or all information inferred by applying deductive reasoning over a collection of data

PEARL: ProjEction of Annotations Rule Language, a Language for Projecting (UIMA) Annotations over RDF Knowledge Bases

In this paper we present a language, PEARL, for projecting annotations based on the Unstructured Information Management Architecture (UIMA) over RDF triples. The language offer is twofold: first, a query mechanism, built upon (and extending) the basic FeaturePath notation of UIMA, allows for efficient access to the standard annotation format of UIMA based on feature structures. PEARL then provides a syntax for projecting the retrieved information onto an RDF Dataset, by using a combination of a SPARQL-like notation for matching pre-existing elements of the dataset and of meta-graph patterns, for storing new information into it. In this paper we present the basics of this language and how a PEARL document is structured, discuss a simple use-case and introduce a wider project about automatic acquisition of knowledge, in which PEARL plays a pivotal role

HORUS: a Configurable Reasoner for Dynamic Ontology Management

This paper introduces HORUS (Human-readable Ontology Reasoner Unit System), a configurable reasoner which provides the user the motivations for every inferred knowledge in the context of a reasoning process. We describe the reasoner, how to write an inference rule and check which explicit knowledge was used to infer a new one. Real cases examples will be provided to show the capabilities of our reasoner and the associated language developed to express inference rules. We show how HORUS allows the user to understand the logical process over which each new RDF triple has been generated

Linguistic watermark 3.0: An RDF framework and a software library for bridging language and ontologies in the Semantic Web

In this paper, we present a framework for representing heterogeneous linguistic resources and for integrating their content with Semantic Web ontologies. This work, which extends and improves previous research conducted by these same authors, articulates into two main results: first, a set of coordinated RDF vocabularies providing descriptors for representing linguistic resources and their software counterparts, as well a collection of metadata for describing the linguistic enrichment of ontologies, both on quantitative and qualitative grounds. The second result is a software library for accessing resources described according to the above vocabularies and for evaluating the quality of linguistically enriched ontologies