Karina Gibert

Using ontologies for structuring organizational knowledge in Home Care assistance

PURPOSE Information Technologies and Knowledge-based Systems can significantly improve the management of complex distributed health systems, where supporting multidisciplinarity is crucial and communication and synchronization between the different professionals and tasks becomes essential. This work proposes the use of the ontological paradigm to describe the organizational knowledge of such complex healthcare institutions as a basis to support their management. The ontology engineering process is detailed, as well as the way to maintain the ontology updated in front of changes. The paper also analyzes how such an ontology can be exploited in a real healthcare application and the role of the ontology in the customization of the system. The particular case of senior Home Care assistance is addressed, as this is a highly distributed field as well as a strategic goal in an ageing Europe. MATERIALS AND METHODS The proposed ontology design is based on a Home Care medical model defined by an European consortium of Home Care professionals, framed in the scope of the K4Care European project (FP6). Due to the complexity of the model and the knowledge gap existing between the - textual - medical model and the strict formalization of an ontology, an ontology engineering methodology (On-To-Knowledge) has been followed. RESULTS After applying the On-To-Knowledge steps, the following results were obtained: the feasibility study concluded that the ontological paradigm and the expressiveness of modern ontology languages were enough to describe the required medical knowledge; after the kick-off and refinement stages, a complete and non-ambiguous definition of the Home Care model, including its main components and interrelations, was obtained; the formalization stage expressed HC medical entities in the form of ontological classes, which are interrelated by means of hierarchies, properties and semantically rich class restrictions; the evaluation, carried out by exploiting the ontology into a knowledge-driven e-health application running on a real scenario, showed that the ontology design and its exploitation brought several benefits with regards to flexibility, adaptability and work efficiency from the end-user point of view; for the maintenance stage, two software tools are presented, aimed to address the incorporation and modification of healthcare units and the personalization of ontological profiles. CONCLUSIONS The paper shows that the ontological paradigm and the expressiveness of modern ontology languages can be exploited not only to represent terminology in a non-ambiguous way, but also to formalize the interrelations and organizational structures involved in a real and distributed healthcare environment. This kind of ontologies facilitates the adaptation in front of changes in the healthcare organization or Care Units, supports the creation of profile-based interaction models in a transparent and seamless way, and increases the reusability and generality of the developed software components. As a conclusion of the exploitation of the developed ontology in a real medical scenario, we can say that an ontology formalizing organizational interrelations is a key component for building effective distributed knowledge-driven e-health systems.

Ontology-driven web-based semantic similarity

Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge—such as the structure of a taxonomy—or implicit knowledge—such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies –like specific domain ontologies- and massive corpus –like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures’ dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities.

Semantic similarity estimation from multiple ontologies

The estimation of semantic similarity between words is an important task in many language related applications. In the past, several approaches to assess similarity by evaluating the knowledge modelled in an ontology have been proposed. However, in many domains, knowledge is dispersed through several partial and/or overlapping ontologies. Because most previous works on semantic similarity only support a unique input ontology, we propose a method to enable similarity estimation across multiple ontologies. Our method identifies different cases according to which ontology/ies input terms belong. We propose several heuristics to deal with each case, aiming to solve missing values, when partial knowledge is available, and to capture the strongest semantic evidence that results in the most accurate similarity assessment, when dealing with overlapping knowledge. We evaluate and compare our method using several general purpose and biomedical benchmarks of word pairs whose similarity has been assessed by human experts, and several general purpose (WordNet) and biomedical ontologies (SNOMED CT and MeSH). Results show that our method is able to improve the accuracy of similarity estimation in comparison to single ontology approaches and against state of the art related works in multi-ontology similarity assessment.

Knowledge discovery with clustering based on rules by states: A water treatment application

This work presents advances in the design of a hybrid methodology that combines artificial intelligence and statistical tools to induce a model of explicit knowledge in relation to the dynamics of a wastewater treatment plant. The methodology contributes to problem solving under the paradigm of knowledge discovery from data in which the pre-process, the automatic interpretation of results and the explicit production of knowledge play a role as important as the analysis itself. The data mining step is performed using clustering based on rules by states, which integrates the knowledge discovered separately at each step of the process into a single model of global operation of the phenomenon. This provides a more accurate model for the dynamics of the system than one obtained by analyzing the whole dataset with all the steps taken together.

Short Communication: GESCONDA: An intelligent data analysis system for knowledge discovery and management in environmental databases

In this work the GESCONDA software is presented. It is a tool for intelligent data analysis and implicit knowledge management of databases, with special focus on environmental databases. Differing from existing commercial systems, the more relevant aspects of this proposal are the incorporation of the statistical data filtering and pre-processing in the same software tool together with the intelligent data analysis techniques as well as the interaction of different data mining methods. Either statistical techniques or artificial intelligence techniques or even mixed techniques are combined and used to extract the knowledge contained within data.

Knowledge Discovery with Clustering Based on Rules. Interpreting Results

It is clear that nowadays analysis of complex systems is an important handicap in Statistics, Artificial Intelligence, Information Systems, Data visualization, and other fields.

Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists

BackgroundAnalysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose, mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To allow performing phenotype analyses of inherited disorder related genes we implemented new original modules within GFINDerhttp://www.bioinformatics.polimi.it/GFINDer/, a Web system we previously developed that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining.ResultsNew GFINDer modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications. To achieve this we exploited, normalized and structured the information present in textual form in the Clinical Synopsis sections of the Online Mendelian Inheritance in Man (OMIM) databank. Such valuable information delineates numerous signs and symptoms accompanying many genetic diseases and it is divided into phenotype location categories, either by organ system or type of finding.ConclusionSupporting phenotype analyses of inherited diseases and biomolecular functional evaluations, GFINDer facilitates a genomic approach to the understanding of fundamental biological processes and complex cellular mechanisms underlying patho-physiological phenotypes.

The data abstraction layer as knowledge provider for a medical multi-agent system

The care of senior patients requires a great amount of human and sanitary resources. The K4Care Project is developing a new European model to improve the home care assistance of these patients. This medical model will be supported by an intelligent platform. This platform has two main layers: a multi-agent system and a knowledge layer. In this paper, it is reviewed the initial design of the system, and some improvements are presented. The main contribution is the introduction of an intermediate layer between agents and knowledge: the Data Abstraction Layer. Using this additional layer agents can have a transparent access to many different knowledge sources, which have data stored in different languages. In addition, the new layer would make possible to make intelligent treatment of the queries in order to generate answers in a more effective and efficient way.

Software, Data and Modelling News: Outcomes from the iEMSs data mining in the environmental sciences workshop series

The Data Mining for Environmental Sciences workshop series started inside iEMSs in 2006 and provides a valuable opportunity for close contact between KDD and Environmental community. After several editions of the workshop, possibilities of KDD for solving very complex environmental problems seems to be better understood by environmental scientists, whereas KDD scientists receive useful inputs to better understand environmental complexity and develop appropriate tools to provide useful responses to the current problems in the area. Some scientific conclusions of this interesting exchange are presented here.

A preliminary taxonomy and a standard knowledge base for mental-health system indicators in Spain

BackgroundThere are many sources of information for mental health indicators but we lack a comprehensive classification and hierarchy to improve their use in mental health planning. This study aims at developing a preliminary taxonomy and its related knowledge base of mental health indicators usable in Spain.MethodsA qualitative method with two experts panels was used to develop a framing document, a preliminary taxonomy with a conceptual map of health indicators, and a knowledge base consisting of key documents, glossary and database of indicators with an evaluation of their relevance for Spain.ResultsA total of 661 indicators were identified and organised hierarchically in 4 domains (Context, Resources, Use and Results), 12 subdomains and 56 types. Among these the expert panels identified 200 indicators of relevance for the Spanish system.ConclusionsThe classification and hierarchical ordering of the mental health indicators, the evaluation according to their level of relevance and their incorporation into a knowledge base are crucial for the development of a basic list of indicators for use in mental health planning.