Luigi Ceccaroni

Artificial Intelligence and Environmental Decision Support Systems

An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems.

OntoWEDSS: augmenting environmental decision-support systems with ontologies

Abstract This paper characterizes part of an interdisciplinary research effort on AI techniques applied to environmental decision-support systems. The architectural design of the OntoWEDSS decision-support system for wastewater management is presented. This system augments classic rule-based reasoning and case-based reasoning with a domain ontology, which provides a more flexible management capability to OntoWEDSS. The construction of the decision-support system is based on a specific case study. But the system is also of general interest, given that its ontology-underpinned architecture can be applied to any wastewater treatment plant and, at an appropriate level of abstraction, to other environmental domains. The OntoWEDSS system helps improve the diagnosis of faulty states of a treatment plant, provides support for complex problem-solving and facilitates knowledge modeling and reuse. In particular, the following issues are dealt with: (1) modeling information about wastewater treatment processes, (2) clarifying part of the existing terminological confusion in the domain, (3) incorporating ontology-modeled microbiologic knowledge related to the treatment process into the reasoning process and (4) creating a decision-support system that combines information through a novel integration between knowledge-based systems and ontologies.

Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation

Context-aware recommender systems aim at outperforming traditional context-free recommenders by exploiting information about the context under which the users’ ratings are acquired. In this paper we present a novel contextual pre-filtering approach that takes advantage of the semantic similarities between contextual situations. For assessing context similarity we rely only on the available users’ ratings and we deem as similar two contextual situations that are influencing in a similar way the user’s rating behavior. We present an extensive comparative evaluation of the proposed approach using several contextually-tagged ratings data sets. We show that it outperforms state-of-the-art context-aware recommendation techniques.

Circles of Health

OBJECTIVES This research is concerned with the study of a new social-network platform, which (1) provides people with disabilities of neurological origin, their relatives, health professionals, therapists, carers and institutions with an interoperable platform that supports standard indicators, (2) promotes knowledge democratization and user empowerment, and (3) allows making decisions with a more informed opinion. METHODS A new social network, Circles of Health, has been designed, developed and tested by end-users. To allow monitoring the evolution of peoples health status and comparing it with other users and with their cohort, anonymized data of 2675 people from comprehensive and multidimensional medical evaluations, carried out yearly from 2006 to 2010, have been standardized to the International Classification of Functioning, Disability and Health, integrated into the corresponding medical health records and then used to automatically generate and graphically represent multidimensional indicators. These indicators have been integrated into Circles of Healths social environment, which has been then evaluated via expert and user-experience analyses. RESULTS Patients used Circles of Health to exchange bio-psycho-social information (medical and otherwise) about their everyday lives. Health professionals remarked that the use of color-coding in graphical representations is useful to quickly diagnose deficiencies, difficulties or barriers in rehabilitation. Most people with disabilities complained about the excessive amount of information and the difficulty in interpreting graphical representations. CONCLUSIONS Health professionals found Circles of Health useful to generate a more integrative understanding of health based on a comprehensive profile of individuals instead of being focused on patients diseases and injuries. People with disabilities found enriching personal knowledge with the experiences of other users helpful. The number of descriptors used at the same time in the graphical interface should be reduced in future versions of the social-network platform.

Context-Awareness as an Enhancement of Brain-Computer Interfaces

Ambient intelligence has acquired a relevant presence in assistive technologies. Context-awareness, the ability to perceive situations and to act providing suitable responses, plays a key role in such presence. BrainAble, an ongoing European project, aims at raising the autonomy of people with functional diversity, facilitating and enhancing the interaction with their environment. Brain-computer interfaces are applied as communication means to allow users to perform actions by using their electroencephalogram signals. Multiple approaches are studied and combined in order to provide the best set of brain signals which specifies a concrete event or action. In this setting, we propose the application of context-awareness to extend the traditional proactive and pervasive nature of ambient intelligence in a way which enhances the brain-computer interface. One practical example is the dynamic personalization of available options in the user interface, based on user’s current context.

Generation of OWL Ontologies from Concept Maps in Shallow Domains

A proposal is presented for integration between a graphical model, such as conceptual maps, and ontologies codified in the OWL language. Conceptual maps are a flexible form of knowledge representation, very useful in education-related collaborative environments; OWL is a language of knowledge representation oriented to semantic analysis and processing carried out by machines. Integration consists of a set of formal transformation applied to conceptual maps and the semantic analysis of the relations linking concepts. The proposed method is based on a concept sense-disambiguation procedure, also defined by the authors, and in the WordNet lexical database. It applies to conceptual maps of shallow domains with labels in the Spanish language.

A graphical environment for ontology development

This paper presents current research on a semantically rich, graphical representation of ontologies and its utility for collaborative construction based on requirements outlined by the Agentcities initiative. A new tool, called the Visual Ontology Modeler, is described and evaluated in the context of Agentcities. Its distinguishing qualities include: ease of use, multi-user configuration management, integrated consistency and completeness checking, automated export of DAML+OIL code. The application domain is an open, dynamic test-bed for agent deployment; the ontologies are encoded in DAML+OIL and explicitly designed to be shared by several agent-based services within this environment.

Distributional semantic pre-filtering in context-aware recommender systems

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user’s ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.

Knowledge Representation for Prognosis of Health Status in Rehabilitation

Abstract: In this article, key points are discussed concerning knowledge representation for clinical decision support systems in the domain of physical medicine and rehabilitation. Information models, classifications and terminologies, such as the “virtual medical record” (vMR), the “international classification of functioning, disability and health” (ICF), the “international classification of diseases” (ICD) and the “systematized nomenclature of medicine—clinical terms” (SNOMED CT), are used for knowledge integration and reasoning. A system is described that supports the measuring of functioning status, diversity, prognosis and similarity between patients in the post-acute stage, thus helping health professionals’ prescription of recommendations. Keywords: representation methods; clinical decision support systems; knowledge systems; rehabilitation; information models; classifications; terminologies 1. Introduction In the domain of medicine in general, and physical medicine and rehabilitation in particular, several standard terminologies and classifications exist [1] that can be used for knowledge representation and integration. Some examples are: the

Citizen Bio-Optical Observations from Coast- and Ocean and Their Compatibility with Ocean Colour Satellite Measurements

Marine processes are observed with sensors from both the ground and space over large spatio-temporal scales. Citizen-based contributions can fill observational gaps and increase environmental stewardship amongst the public. For this purpose, tools and methods for citizen science need to (1) complement existing datasets; and (2) be affordable, while appealing to different user and developer groups. In this article, tools and methods developed in the 7th Framework Programme of European Union (EU FP 7) funded project Citclops (citizens’ observatories for coast and ocean optical monitoring) are reviewed. Tools range from a stand-alone smartphone app to devices with Arduino and 3-D printing, and hence are attractive to a diversity of users; from the general public to more specified maker- and open labware movements. Standardization to common water quality parameters and methods allows long-term storage in regular marine data repositories, such as SeaDataNet and EMODnet, thereby providing open data access. Due to the given intercomparability to existing remote sensing datasets, these tools are ready to complement the marine datapool. In the future, such combined satellite and citizen observations may set measurements by the engaged public in a larger context and hence increase their individual meaning. In a wider sense, a synoptic use can support research, management authorities, and societies at large.