Antonio M. Rinaldi

Towards a user based recommendation strategy for digital ecosystems

In digital ecosystems, the possibility of discovering useful digital objects based on recommendation techniques is one of the most useful tasks that still remains to realize. In this realm, it seems really powerful to have both an a priori user profiling and an a posteriori knowledge, extracted from historical and current user behavior, thus recommender systems can be easily adapted to these environments. In this paper, we present a hybrid strategy that proposes customized recommendations using semantic contents and potentially low-level features of multimedia objects, past behavior of users in terms of usage patterns and user interests expressed by ontologies. We have implemented a prototype that supports our proposed recommendation strategy and a real use of our system based on a 3D interface is shown. Finally, some preliminary experimental results are presented and discussed.

An ontological approach to represent knowledge in territorial planning science

Territorial planning science pertains to the governance of land transformations. Therefore it concerns several matters and heterogeneous subjects that use knowledge as a basis. Formal methodologies and techniques of knowledge representation are fundamental tools for the governance of land transformations and for sharing information among involved subjects. We propose and implement an ontology-based model of knowledge in the framework of landscape planning, with a particular emphasis on cultural landscape, to resolve conceptual misunderstandings and semantic ambiguities, and to provide a precise and accurate description of the current state of our knowledge.

An approach to ontology integration for ontology reuse in knowledge based digital ecosystems

In the last years, the large availability of information and knowledge models formalized by ontologies has demanded effective and efficient methodologies for reusing and integrating such models in global conceptualizations of a specific knowledge or application domain. The ability to effectively and efficiently perform knowledge reuse is a crucial factor in the development of ontologies, which are a potential solution to the problem of information standardization and a viaticum towards the realization of knowledge-based digital ecosystem. In this paper, an approach to ontology reuse based on heterogeneous matching techniques will be presented; in particular, we will show how the process of ontology building will be improved and simplified, by automating the selection and the reuse of existing data models to support the creation of digital ecosystems. The proposed approach has been applied to the food domain, specifically to food production.

A recommendation strategy based on user behavior in digital ecosystems

In this paper, we present a new vision of multimedia recommender systems based on an a novel paradigm that combines both analysis of user behavior and semantic descriptors of multimedia objects. In particular, we model recommendation as a two step process: First we propose a model of user behavior, based on a semantic network that captures the necessary knowledge of the domain of interest; second we propose a recommendation strategy based on both this kind of a-priori knowledge and on the current usage patterns, considering the actual and complex structure of multimedia data. We have implemented a prototype that supports our proposed recommendation strategy; eventually a real use of our system based on a 3D interface is presented and discussed.

Big Data: A Survey

For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big Data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big Data umbrella, throughout a qualitative analysis of their characterizing features.

Big graph-based Data visualization experiences: The WordNet case study

In the Big Data era, the visualization of large data sets is becoming an increasingly relevant task due to the great impact that data have from a human perspective. Since visualization is the closer phase to the users within the data life cycles phases, there is no doubt that an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. This paper presents an experience for importing, querying and visualizing graph database and in particular, we describe as a case study the WordNet database using Neo4J and Cytoscape. We will describe each step in this study focusing on the used strategies for overcoming the different problems mainly due to the intricate nature of the case study. Finally, an attempt to define some criteria to simplify the large-scale visualization of WordNet will be made, providing some examples and considerations which have arisen.

Improving the Visualization of WordNet Large Lexical Database through Semantic Tag Clouds

In the Big Data era, the visualization of large data sets is becoming an increasingly relevant task due to the great impact that data have from a human perspective. Since the visualization is the closer phase to the users within the data life cycles phases, there is no doubt that an effective, efficient and impressive representation of the analyzed data may result as important as the analytic process itself. Starting from previous experiences in importing, querying and visualizing WordNet database within Neo4J and Cytoscape, this work aims at improving the WordNet Graph visualization by exploiting the features and concepts behind tag clouds. The objective of this study is twofold: firstly, we argue that the proposed visualization strategy is able to put order in the messy and dense structure of nodes and edges of large knowledge bases as WordNet, showing as much as possible information from this knowledge source and in a clearer way; secondly, we think that the tag cloud approach applied to the synonyms rings reinforces the human cognition in recognizing the different usages of words in natural languages like English. In this regard, we also propose a formal strategy in order to evaluate the information perception in the use of our methodology by means of a questionnaire asked to a group of users. Finally, we compare these results with those resulting from the adoption of well known representations of WordNet within existing GUIs.

Improving Tag Clouds with Ontologies and Semantics

Tag clouds are visual representations of a set of terms which represent several document dimensions. Social and collaborative systems have greatly increased the popularity of this type of visualization but several problems arise from their knowledge base structures. In this paper we propose a novel strategy to improve tag clouds with ontological and semantic information. Our methodology is based on a general knowledge base to extract additional terms and relations. These information are combined with statistics to enhance tag clouds visualization and improve their possible application in user-based systems.

An Approach to Ontology Integration for Ontology Reuse

In the last years, the large availability of data and schema models formalized through different languages has demanded effective and efficient methodologies to reuse such models. One of the most challenging problem consists in integrating different models in a global conceptualization of a specific knowledge or application domain. This is a hard task to accomplish due to ambiguities, inconsistencies and heterogeneities, at different levels, that could stand in the way. The ability to effectively and efficiently perform knowledge reuse is a crucial factor in knowledge management systems, and it also represents a potential solution to the problem of standardization of information and a viaticum towards the realization of the Semantic web. In this paper, an approach to ontology reuse based on heterogeneous matching techniques will be presented, in particular, we will show how the process of ontology construction will be improved and simplified, by automatizing the selection and the reuse of existing data models. The proposed approach will be applied to the food domain, specifically to the food production.

Representing and sharing spatial knowledge using configurational ontology

The problems related to the management of information increase everyday in several contexts. This process needs of more and more effective and efficient knowledge management techniques. We chose as case study the issue of urban planning and design due to the cultural and scientific innovations, which led in recent years to a completely new interpretation of a city as a system of elements each other connected. In the idea that the urban phenomena are manly driven by the topological structure of the urban space, the science of complex networks is flourishing as a novel reference to include a quantitative modelling approach in territorial planning and design. The paper aim is to give a conceptual framework to integrate the knowledge of the elements composing the city, using an ontology to define, organise and manage them, with the functional potential of the urban space that connects them to each other, stressing the concept of topological structure in terms of configuration. The functional aspects revealed by the configurational analysis of the city-network will be improved and better contextualised by the implementation of an urban ontology-based model. The obtained ontologies are shared using system based on peer-to-peer P2P paradigm exposed by a web service.