OntoLex-Lemon is a collection of RDF vocabularies for specifying the verbalization of ontologies in natural language. Beyond its original scope, OntoLex-Lemon, as well as its predecessor Monnet lemon, found application in the Linguistic Linked Open Data cloud to represent and interlink language resources on the Semantic Web. Unfortunately, generic ontology and RDF editors were considered inconvenient to use with OntoLex-Lemon because of its complex design patterns and other peculiarities, including indirection, reification and subtle integrity constraints. This perception led to the development of dedicated editors, trading the flexibility of RDF in combining different models (and the features already available in existing RDF editors) for a more direct and streamlined editing of OntoLex-Lemon patterns. In this paper, we investigate on the benefits gained by extending an already existing RDF editor, VocBench 3, with capabilities closely tailored to OntoLex-Lemon and on the challenges that such extension implies. The outcome of such investigation is twofold: a vertical assessment of a new editor for OntoLex-Lemon and, in the broader scope of RDF editor design, a new perspective on which flexibility and extensibility characteristics an editor should meet in order to cover new core modeling vocabularies, for which OntoLex-Lemon represents a use case.

A practical alignment service should be flexible enough to handle the varied alignment scenarios that arise in the real world, while minimizing the need for manual configuration. MAPLE, an orchestration framework for ontology alignment, supports this goal by coordinating a few loosely coupled actors, which communicate and cooperate to solve a matching task using explicit metadata about the input ontologies, other available resources and the task itself. The alignment task is thus summarized by a report listing its characteristics and suggesting alignment strategies. The schema of the report is based on several metadata vocabularies, among which the Lime module of the OntoLex-Lemon model is particularly important, summarizing the lexical content of the input ontologies and describing external language resources that may be exploited for performing the alignment. In this paper, we propose a REST API that enables the participation of downstream alignment services in the process orchestrated by MAPLE, helping them self-adapt in order to handle heterogeneous alignment tasks and scenarios. The realization of this alignment orchestration effort has been performed through two main phases: we first described its API as an OpenAPI specification (a la API-first), which we then exploited to generate server stubs and compliant client libraries. Finally, we switched our focus to the integration of existing alignment systems, with one fully integrated system and an additional one being worked on, in the effort to propose the API as a valuable addendum to any system being developed.

Ontology mediators often demand extensive configuration, or even the adaptation of the input ontologies for remedying unsupported modeling patterns. In this paper we propose MAPLE (MAPping Architecture based on Linguistic Evidences), an architecture and software platform that semi-automatically solves this configuration problem, by reasoning on metadata about the linguistic expressivity of the input ontologies, the available mediators and other components relevant to the mediation task. In our methodology mediators should access the input ontologies through uniform interfaces abstracting many low-level details, while depending on generic third-party linguistic resources providing external information. Given a pair of ontologies to reconcile, MAPLE ranks the available mediators according to their ability to exploit most of the input ontologies content, while coping with the exhibited degree of linguistic heterogeneity. MAPLE provides the chosen mediator with concrete linguistic resources and suitable implementations of the required interfaces. The resulting mediators are more robust, as they are isolated from many low-level issues, and their applicability and performance may increase over time as new and better resources and other components are made available. To sustain this trend, we foresee the use of the Web as a large scale repository.

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.

The Semantic Search research field aims to query metadata and to identify relevant subgraphs. While in traditional search engines queries are composed by lists of keywords connected through boolean operators, Semantic Search instead, requires the submission of semantic queries that are structured as a graph of concepts, entities and relations. Submission of this graph is however not trivial as while a list of keywords of interest can be provided by any user, the formulation of semantic queries is not easy as well. One of the main challenges of RDF Browsers lies in the implementation of interfaces that allow the common user to submit semantic queries by hiding their complexity. Furthermore a good semantic search algorithm is not enough to fullfil user needs, it is worthwhile to implement visualization methods which can support users in intuitively understanding why and how the results were retrieved. In this paper we present a novel solution to query RDF datasets and to browse the results of the queries in an appealing manner.

The Semantic Web is facing the important challenge to maintain its promise of a real world-wide graph of interconnected resources. Unfortunately, while URIs almost guarantee a direct reference to entities, the relation between the two is not bijective. Many different URI references to same concepts and entities can arise when -- in such a heterogeneous setting as the WWW -- people independently build new ontologies, or populate shared ones with new arbitrarily identified individuals. The proliferation of URIs is an unwanted, though natural effect strictly bound to the same principles which characterize the Semantic Web; reducing this phenomenon will improve the recall of Semantic Search engines, which could rely on explicit links between heterogeneous information sources. To address this problem, in this paper we present an integrated environment combining the semantic annotation and ontology building features available in the Semantic Turkey web browser extension, with globally unique identifiers for entities provided by the okkam Entity Name System, thus realizing a valuable resource for preventing diffusion of multiple URIs on the (Semantic) Web.

While the Web is facing interesting new changes in the way users access, interact and even participate to its growth, the most traditional applications dedicated to its fruition: web browsers, are not responding with the same euphoric boost for innovation, mostly relying on third party or open-source community-driven extensions for addressing the new Social and Semantic Web trends and technologies. This technological and decisional gap, which is probably due to the lack of a strong standardization commitment on the one side (Web 2.0/Social Web) and in the delay of massive adherence to new officially approved standards (W3C approved Semantic Web languages), has to be filled by successful stories which could lay the path for the evolution of browsers. In this work we present a novel web browser extension which combines several features coming from the worlds of terminology and information extraction, semantic annotation and knowledge management, to support users in the process of both keeping track of interesting information they find on the web, and organizing its associated content following knowledge representation standards offered by the Semantic Web

In this paper we present JMWNL, a multilingual extension of the JWNL java library, which was originally developed for accessing Princeton WordNet dictionaries. JMWNL broadens the range of JWNL’s accessible resources by covering also dictionaries produced inside the EuroWordNet project. Specific resources, such as language-dependent algorithmic stemmers, have been adopted to cover the diversities in the morphological nature of words in the addressed idioms. New semantic and lexical relations have been included to maximize compatibility with new versions of the original Princeton WordNet and to include the whole range of relations from EuroWordNet. Relations from Princeton WordNet on one side and EuroWordNet on the other one have in some cases been mapped to provide a uniform reference for coherent cross-linguistic use of the library.

The paper presents a comparative study of semantic and lexical relations defined and adopted in WordNet and EuroWordNet. This document describes the experimental observations achieved through the analysis of data from different WordNet versions and EuroWordNet distributions for different languages, during the development of JMWNL (Java Multilingual WordNet Library), an extensible multilingual library for accessing WordNet-like resources in different languages and formats. The goal of this work was to realize an operative mapping between the relations defined in the two lexical resources and to unify library access and content navigation methods for both WordNet and EuroWordNet. The analysis focused on similarities, differences, semantic overlaps or inclusions, factual misinterpretations and inconsistencies between the intended and practical use of each single relation defined in these two linguistic resources. The paper details with examples the produced mapping, discussing required operations which implied merging, extending or simply keeping separate the examined relations

Building a Linguistic Resource (LR) is a task requiring a huge quantitative of means, human resources and funds. Though finalization of the development phase and assessment of the produced resource, necessarily require human involvement, a computer aided process for building the resource’s initial structure would greatly reduce the overall effort to be undertaken. We present here a novel approach for automatizing the process of building structured (possibly multilingual) LRs, starting from already available LRs and exploiting simple vocabularies of synonyms and/or translations for different languages. A simple algorithm for clustering terms, according to their shared senses, is presented in two versions, both for separating flat list of synonyms and flat lists of translations. The algorithm is then motivated against two possible exploitations: reducing the cost for producing new LRs, and linguistically enriching the content of existing semantic resources, like SW ontologies and knowledge bases. Empirical results are provided for two experimental setups: automatic term clustering for English synonyms list, and for Italian translations of English terms