Paola Carrara

Fuzzy Approaches to Extend Boolean Information Retrieval

Many commercial information retrieval systems use the Boolean information retrieval model. Some extensions have been designed to provide for the limitations this model presents without proceeding to its complete redesign. Fuzzy set theory has been employed as a suitable tool for this purpose, especially for dealing with imprecision and vagueness in the information retrieval activity. The first extensions of the Boolean query language within this formal framework introduced numeric query weights. Recently, the authors have proposed linguistic extensions which manage user vagueness in formulating queries directly. They have also defined a document representation which more effectively expresses documents’ information content.

A linguistic decision making approach to assess the quality of volunteer geographic information for citizen science

The paper analyses the challenges and problems posed by the use of Volunteered Geographic Information (VGI) in citizen science and a proposal is formulated for assessing VGI quality based on a linguistic decision making approach so as to allow its feasible use for scientific purposes. VGI quality is represented by indicators at distinct levels of granularity which take into account the distinct components of the VGI items. The quality indicators represent both the extrinsic quality, depending on the characteristics and reputation of the sources of information; the intrinsic quality, depending on the distinct accuracy and precision of information; and, last but not least, the pragmatic quality, depending on the user needs and intended purposes. In order to assess the pragmatic quality of VGI items, a linguistic decision making approach is defined that allows users to rank and finally filter the VGI items based on the satisfaction of distinct criteria expressed by means of both linguistic terms, defining soft constraints on the distinct quality indicators, and linguistic aggregators, defining fuzzy operators which combine the satisfaction degrees of the soft constraints at distinct hierarchical levels to yield the final satisfaction of the VGI items. Finally, an example of quality assessment in a glaciological citizen science project is discussed.

Toward overcoming culture, skill and situation hurdles in Human-Computer Interaction

Abstract.This paper proposes a new effective strategy for designing and implementing interactive systems overcoming culture, skill and situation hurdles in Human-Computer Interaction (HCI). The strategy to identify and reduce these hurdles is developed in the framework of a methodology based on a recently introduced model of HCI, and exploits the technological innovations of XML (Extensible Markup Language). HCI is modelled as a cyclic process in which the user and the interactive system communicate by materializing and interpreting a sequence of messages. The interaction process is formalized by specifying both the physical message appearance and the computational aspect of the interaction. This formalization allows the adoption of notation traditionally adopted by users in their workplaces as the starting point of the interactive system design. In this way, the human–system interaction language takes into account the users’ culture. Moreover, the methodology permits user representatives to build a hierarchy of systems progressively adapted to users’ situations, skills and habits, according to the work organization in the domain considered. The strategy is proved to be effective by describing how to implement it using BANCO (Browsing Adaptive Network for Changing user Operativity), a feasibility prototype based on XML, which allows the hierarchy implementation and system adaptations. Several examples from an environmental case under study are used throughout the paper to illustrate the methodology and the effectiveness of the technology adopted.

A flexible multi-source spatial-data fusion system for environmental status assessment at continental scale

The monitoring of the environments status at continental scale involves the integration of information derived by the analysis of multiple, complex, multidisciplinary, and large‐scale phenomena. Thus, there is a need to define synthetic Environmental Indicators (EIs) that concisely represent these phenomena in a manner suitable for decision‐making. This research proposes a flexible system to define EIs based on a soft fusion of contributing environmental factors derived from multi‐source spatial data (mainly Earth Observation data). The flexibility is twofold: the EI can be customized based on the available data, and the system is able to cope with a lack of expert knowledge. The proposal allows a soft quantifier‐guided fusion strategy to be defined, as specified by the user through a linguistic quantifier such as ‘most of’. The linguistic quantifiers are implemented as Ordered Weighted Averaging operators. The proposed approach is applied in a case study to demonstrate the periodical computation of anomaly indicators of the environmental status of Africa, based on a 7‐year time series of dekadal Earth Observation datasets. Different experiments have been carried out on the same data to demonstrate the flexibility and robustness of the proposed method.

Extending Boolean information retrieval: a fuzzy model based on linguistic variables

In information retrieval systems the vagueness in user requests for information is mainly managed by the use of numeric weights present the formal definition of a retrieval model in which linguistic descriptors are used in the query language both to express the importance that a term must have in the desired documents and to label the retrieved documents in relevance classes. By attaching a numeric weight to a term, a user provides a quantitative description of the importance of that term in the documents sought. If the introduction of weights reduces the vagueness in query formulation, the use of numeric weights requires a clear knowledge of their semantics and the translation of a fuzzy concept in a precise numeric value. Based on these problems and starting from an existing weighted Boolean retrieval model, the authors formalize within fuzzy set theory a new model that allows the interpretation of a user query in which a linguistic descriptor is attached to each term.<<ETX>>

Positive and Negative Information for Assessing and Revising Scores of Burn Evidence

A straightforward way to map burned areas from remotely sensed imagery is to integrate partial evidence of burn provided by multiple spectral indices (SIs). Our approach relies on fuzzy set theory to generate integrated layers of overall positive evidence (PE) and negative evidence (NE) scores. In order to reduce commission errors, we propose the use of NE for revising the overall PE. Revised layers are input for a region growing algorithm to produce a map of burned areas. Thematic Mapper (TM) images, acquired over the Mediterranean area, were used to derive the SIs and to define the soft constraints (membership functions). The performance of the revision process is tested for a TM image acquired over Portugal: The revision decreases the commission error from 59.5% to 1.3% and increases the overall accuracy from 42.6% up to 91.3%.

RITMARE: Semantics-aware Harmonisation of Data in Italian Marine Research

Abstract RITMARE is a Flagship Project by the Italian Ministero dell’Istruzione, dell’Universita e della Ricerca (MIUR) and coordinated by the National Research Council (CNR). It aims at the interdisciplinary integration of national marine research. In pursuing a Linked Open Data (LOD) vocation, the RITMARE sub-project 7 is building the necessary domain-related data structures by leveraging existing RDF-based schemata and sources. These data structures are grounding semantics-aware profiling of end users, data providers, and resources. The goal is designing a flexible infrastructure that adapts to the audiences specificities.

Sensor metadata blueprints and computer-aided editing for disciplined SensorML

The need for continuous, accurate, and comprehensive environmental knowledge has led to an increase in sensor observation systems and networks. The Sensor Web Enablement (SWE) initiative has been promoted by the Open Geospatial Consortium (OGC) to foster interoperability among sensor systems. The provision of metadata according to the prescribed SensorML schema is a key component for achieving this and nevertheless availability of correct and exhaustive metadata cannot be taken for granted. On the one hand, it is awkward for users to provide sensor metadata because of the lack in user-oriented, dedicated tools. On the other, the specification of invariant information for a given sensor category or model (e.g., observed properties and units of measurement, manufacturer information, etc.), can be labor- and timeconsuming. Moreover, the provision of these details is error prone and subjective, i.e., may differ greatly across distinct descriptions for the same system. We provide a user-friendly, template-driven metadata authoring tool composed of a backend web service and an HTML5/javascript client. This results in a form-based user interface that conceals the high complexity of the underlying format. This tool also allows for plugging in external data sources providing authoritative definitions for the aforementioned invariant information. Leveraging these functionalities, we compiled a set of SensorML profiles, that is, sensor metadata blueprints allowing end users to focus only on the metadata items that are related to their specific deployment. The natural extension of this scenario is the involvement of end users and sensor manufacturers in the crowd-sourced evolution of this collection of prototypes. We describe the components and workflow of our framework for computer-aided management of sensor metadata.

A Holistic, Semantics-aware Approach to Spatial Data Infrastructures

We present a novel approach to the management of Spatial Data Infrastrutures that leverages semantics-aware context information to model the distinct aspects involved in the management of spatial data. RDF-based schemata are employed for encoding information on the user community, the terminologies in use in a specific research domain, gazetteer information representing the physical landscape hosting data and, last but not least, spatial resource metadata. The data structures are then interconnected to enable seamless exploitation for metadata creation and resource discovery, which we demonstrate through a worked-out example of SPARQL query on RDF graph data. The methodology is being applied by by the National Research Council (CNR) of Italy to support creation of a distributed infrastructure for marine data in the context of the RITMARE Flagship Project.

The RITMARE Starter Kit bottom-up capacity building for geospatial data providers

Capacity building by data providers is a fundamental task in the creation of a decentralized Spatial Data Infrastructure. This challenge has been tackled in the RITMARE Flagship Project by providing the Starter Kit, a comprehensive set of domain-oriented software components that exposes standard services for the management of geospatial information. We report on the characteristics of this toolkit, developed by the National Research Council of Italy (CNR), particularly with regard to the underlying service-oriented architecture and the novel semantics-aware methodology that is proposed for metadata editing.