Luigi Asprino

Framester: A Wide Coverage Linguistic Linked Data Hub

Semantic web applications leveraging NLP can benefit from easy access to expressive lexical resources such as FrameNet. However, the usefulness of FrameNet is affected by its limited coverage and non-standard semantics. The access to existing linguistic resources is also limited because of poor connectivity among them. We present some strategies based on Linguistic Linked Data to broaden FrameNet coverage and formal linkage of lexical and factual resources. We created a novel resource, Framester, which acts as a hub between FrameNet, WordNet, VerbNet, BabelNet, DBpedia, Yago, DOLCE-Zero, as well as other resources. Framester is not only a strongly connected knowledge graph, but also applies a rigorous formal treatment for Fillmores frame semantics, enabling full-fledged OWL querying and reasoning on a large frame-based knowledge graph. We also describe Word Frame Disambiguation, an application that reuses Framester data as a base in order to perform frame detection from text, with results comparable in precision to the state of the art, but with a much higher coverage.

The Role of Ontology Design Patterns in Linked Data Projects

The contribution of this paper is twofold: (i) a UML stereotype for component diagrams that allows for representing ontologies as a set of interconnected Ontology Design Patterns, aimed at supporting the communication between domain experts and ontology engineers; (ii) an analysis of possible approaches to ontology reuse and the definition of four methods according to their impact on the sustainability and stability of the resulting ontologies and knowledge bases. To conceptually prove the effectiveness of our proposals, we present two real LOD projects.

Semantic Web for Cultural Heritage Valorisation

Cultural heritage consists of heterogeneous resources: archaeological artefacts, monuments, sites, landscapes, paintings, photos, books and expressions of human creativity, often enjoyed in different forms: tangible, intangible or digital. Each resource is usually documented, conserved and managed by cultural institutes like museums, libraries or holders of archives. These institutes make available a detailed description of the objects as catalog records. In this context, the chapter proposes both a classification of cultural heritage data types and a process for cultural heritage valorisation through the well-known Linked Open Data paradigm. The classification and process have been defined in the context of a collaboration between the Semantic Technology Laboratory of the National Research Council (STLab) and the Italian Ministry of Cultural Heritage and Activities and Tourism (MIBACT) that the chapter describes, although we claim they are sufficiently general to be adopted in every cultural heritage scenario. In particular, the chapter introduces both a suite of ontology modules named Cultural-ON to model the principal elements identified in the cultural heritage data type classification, and the process we employed for data valorisation purposes. To this end, semantic technologies are exploited; that is, technologies that allow us to conceptualise and describe the meaning of data forming the cultural heritage and including such entities as places, institutions, cultural heritage events, availability, etc. These entities have special characteristics and are connected with each other in a profound way. The result is a knowledge base consisting of semantic interconnections with also other data available in the Web to be exploited according to different tasks and users preferences. By navigating the semantic relationships between the various objects of the knowledge base, new semantic paths can be revealed and utilised with the aim to develop innovative services and applications. The process is compliant with Linked Open Data and W3C Semantic Web best practices so that to enable a wider promotion of cultural heritage, and of sharing and reuse of cultural heritage data in the Web. The chapter concludes presenting a number of methodological principles and lessons learnt from the STLab/MIBACT collaboration that are applicable to any cultural heritage context and, in some cases, also to other domains.

FOOD: FOod in Open Data

This paper describes the outcome of an e-government project named FOOD, FOod in Open Data, which was carried out in the context of a collaboration between the Institute of Cognitive Sciences and Technologies of the Italian National Research Council, the Italian Ministry of Agriculture (MIPAAF) and the Italian Digital Agency (AgID). In particular, we implemented several ontologies for describing protected names of products (wine, pasta, fish, oil, etc.). In addition, we present the process carried out for producing and publishing a LOD dataset containing data extracted from existing Italian policy documents on such products and compliant with the aforementioned ontologies.

Matching Ontologies Using a Frame-Driven Approach

The need of handling semantic heterogeneity of resources is a key problem of the Semantic Web. State of the art techniques for ontology matching are the key technology for addressing this issue. However, they only partially exploit the natural language descriptions of ontology entities and they are mostly unable to find correspondences between entities having different logical types (e.g. mapping properties to classes). We introduce a novel approach aimed at finding correspondences between ontology entities according to the intensional meaning of their models, hence abstracting from their logical types. Lexical linked open data and frame semantics play a crucial role in this proposal. We argue that this approach may lead to a step ahead in the state of the art of ontology matching, and positively affect related applications such as question answering and knowledge reconciliation.

Addressing Knowledge Integration with a Frame-Driven Approach

Given a knowledge-based system running virtually forever able to acquire and automatically store new open-domain knowledge, one of the challenges is to evolve by continuously integrating new knowledge. This needs to be done while handling conflicts, redundancies and linking existing knowledge to the incoming one. We refer to this task with the name Knowledge integration. In this paper we define the problem by discussing its challenges, we propose an approach for tackling the problem, and, we suggest a methodology for the evaluation of results.