Carmen De Maio

Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis

In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.

Towards an automatic fuzzy ontology generation

In recent years, the success of Semantic Web is strongly related to the diffusion of numerous distributed ontologies enabling shared machine readable contents. Ontologies vary in size, semantic, application domain, but often do not foresee the representation and manipulation of uncertain information. Here we describe an approach for automatic fuzzy ontology elicitation by the analysis of web resources collection. The approach exploits a fuzzy extension of Formal Concept Analysis theory and defines a methodological process to generate an OWL-based representation of concepts, properties and individuals. A simple case study in the Web domain validates the applicability and the flexibility of this approach.

Unfolding social content evolution along time and semantics

Abstract In the context of social media, the unstructured and dynamic nature of exchanged data and the information overload contribute to the growth of the number of research works proposing methods to improve performance of intelligent analytics services considering both time and semantics of the shared content. The presented paper focuses on the definition of a knowledge tracking framework to answer questions, such as “What is the semantic evolution of a topic (or news) along the time?”, “How did we arrive to a specific event?”, “What is the evolution of the topics of interest of a user?”, and so on. Our interest is about the elicitation of temporal patterns revealing the evolution of concepts along the time from a social media data stream; we focus on Twitter. Such patterns can be extracted at different levels of abstraction by considering different-sized time intervals and different scopes driven by the conceptualization of users’ queries. To address the proposed aim, we extend Temporal Concept Analysis and we use Description Logic to reason on semantically represented tweet streams. The evaluation activity reveals promising results from both sides quantitative and qualitative.

A framework for context-aware heterogeneous group decision making in business processes

In Business Process Management great attention is given to Computational Intelligence for supporting process life-cycle. Several approaches have been defined to support human decision making. The main drawback is that there are no solid criteria for determining optimal decisions since context, matter of discussion, and involved actors may differ at each execution. This work focuses on the definition of a framework to support and trace human decision making activities, in business processes, when heterogeneous decision-makers have to find a consensus to select most promising alternative to follow. The framework relies on Fuzzy Consensus Model and implements Reinforcement Learning algorithm to learn weight of the decision-makers through the analysis of past process executions considering context and performances of business processes. Context awareness relies on semantic web technologies enabling ontological reasoning to evaluate context similarity used to assign the right weight to the involved decision-makers also in the case when more general or more specific context occurs. The framework has been instantiated in the case study of Supply Chain Management. The analysis of the simulation results reveal that the proposed weight learning algorithm and the considered initial weight association strategies (Starting Weight and Training Executions), even if the cold start, give to decision-makers the chance to fill the gap with respect to more experienced decision makers.

Biomedical data integration and ontology-driven multi-facets visualization

With the proliferation of different heterogeneous biomedical data sources and with the growing amount of their content available over the Web, there is, on one side, the need to support mashing and data integration and, on the other side, the more urgent need to relate literature and research results that are often enclosed in unstructured textual documents. Nowadays, ontologies have been used as a common access knowledge layer playing a crucial role to support categorized access to the information resources. Moreover, manual construction of a domain-specific ontology and content categorization is a labor intensive and a time-consuming process. This work focuses on the development of a novel biomedical ontology-driven multi-facets visualization to support categorized access to heterogeneous and unstructured biomedical data sources (e.g., PubMed, WikiGenes). Specifically, the framework relies on: knowledge extraction methodology, to automatically extract ontology exploiting the Fuzzy Formal Concept Analysis theory; and ontology matching strategy to find relation between extracted ontology and the available ones in the field of biomedicine (e.g., Ontology of Gene and Genomes, Gene Ontology, Protein Ontology). The evaluation will be shown in terms of Precision and Recall by using biomedical ontology concepts as input query to the multi-facets visualization engine.

Towards Perception-Oriented Situation Awareness Systems

This paper proposes a new approach for identifying situations from sensor data by using a perception-based mechanism that has been borrowed from humans: sensation, perception and cognition. The proposed approach is based on two phases: low-level perception and high-level perception. The first one is realized by means of semantic technologies and allows to generate more abstract information from raw sensor data by also considering knowledge about the environment. The second one is realized by means of Fuzzy Formal Concept Analysis and allows to organize and classify abstract information, coming from the first phase, by generating a knowledge representation structure, namely lattice, that can be traversed to obtain information about occurring situation and augment human perception. The work proposes also a sample scenario executed in the context of an early experimentation.

A Methodology based on Commonsense Knowledge and Ontologies for the Automatic Classification of Legal Cases

We describe a methodology for the automatic classification of legal cases expressed in natural language, which relies on existing legal ontologies and a commonsense knowledge base. This methodology is founded on a process consisting of three phases: an enrichment of a given legal ontology by associating its terms with topics retrieved from the Wikipedia knowledge base; an extraction of relevant concepts from a given textual legal case; and a matching between the enriched ontological terms and the extracted concepts. Such a process has been successfully implemented in a corresponding tool that is part of a larger framework for self-litigation and legal support for the Italian law.

Fuzzy knowledge approach to automatic disease diagnosis

Applying best available evidences to clinical decision making requires medical research sharing and (re)using. Recently, computer assisted medical decision making is taking advantage of Semantic Web technologies. In particular, the power of ontologies allows to share medical research and to provide suitable support to the physicians practices. This paper describes a system, named ODINO (Ontological DIsease kNOwledge), aimed at supporting medical decision making through semantic based modeling of medical knowledge base. The system defines an ontology model able to represent relations between medical disease and its symptomatology in a qualitative manner by using fuzzy labels. Medical knowledge is defined according with physician experts members of INMP1 (National Institute for Health Migration and Poverty). The main aim of ODINO is to provide an effective user interface by using ontologies and controlled vocabularies and by allowing faceted search of diseases. In particular, this work mashes the capabilities of Description Logic reasoners and information retrieval techniques in order to answer to physicians requests. Some experimental results are given in the field of dermatological diseases.

Towards OLAP Analysis of Multidimensional Tweet Streams

Social media and networks are used by millions of people to share with their friends across the world: tastes, opinions, ideas, etc. The volume and the speed at which these data are produced make it a challenging task to discover meaningful patterns in the data. Nevertheless, very interesting business goals could be achieved collecting these data and performing analytics on social media data streams, such as: addressing marketing strategies, targeting advertisements, and so forth. We emphasize that there is a need to investigate and define suitable knowledge mining approaches to go beyond explicitly available metadata by analyzing unstructured data to provide intelligent analytics services. Specifically, in this paper we provide first results on applying OLAP analysis to multidimensional Tweet streams.

Making sense of cloud-sensor data streams via Fuzzy Cognitive Maps and Temporal Fuzzy Concept Analysis

Definition of an hybrid approach for situation awareness that tries to balance the application of unsupervized data analysis techniques and the use of human expert knowledge to make sense of such analysis results.Definition of methodology that integrates Temporal Fuzzy Concept Analysis and Fuzzy Cognitive Maps, also supported by semantic technologies, to realize the aforementioned approach.As a real-world scenario we consider the recognition of human activities and the projection of them in the near future to address, for instance, energy saving, safety issues, and so on. Understanding situations occurring within the physical world by analyzing streams of sensor data is a complex task for both human and software agents. In the area of situation awareness, the observer is typically overwhelmed by information overload and by intrinsic difficulties of making sense of spatially distributed and temporal-ordered sensor observations. Thus, it is desirable to design effective decision-support systems and develop efficient methods to handle sensor data streams. The proposed work is for the comprehension of the situations evolving along the timeline and the projection of recognized situations in the near future. The system analyzes semantic sensor streams, it extracts temporal pattern describing events flow and provides useful insights with respect to the operators goals. We implement a hybrid solution for situation comprehension and projection that combines data-driven approach, by using temporal extension of Fuzzy Formal Concept Analysis, and goal-driven approach, by using Fuzzy Cognitive Maps. The cloud-based architecture integrates a distributed algorithm to perform Fuzzy Formal Concept Analysis enabling to deal with deluge of sensor data stream acquired through a sensor-cloud architecture. We discuss the results in terms of prediction accuracy by simulating sensor data stream to early recognize daily life activities inside an apartment.