Miriam Baglioni

Preprocessing and Mining Web Log Data for Web Personalization

We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site.

How you move reveals who you are: understanding human behavior by analyzing trajectory data

The widespread use of mobile devices is producing a huge amount of trajectory data, making the discovery of movement patterns possible, which are crucial for understanding human behavior. Significant advances have been made with regard to knowledge discovery, but the process now needs to be extended bearing in mind the emerging field of behavior informatics. This paper describes the formalization of a semantic-enriched KDD process for supporting meaningful pattern interpretations of human behavior. Our approach is based on the integration of inductive reasoning (movement pattern discovery) and deductive reasoning (human behavior inference). We describe the implemented Athena system, which supports such a process, along with the experimental results on two different application domains related to traffic and recreation management.

An Ontology-Based Approach for the Semantic Modelling and Reasoning on Trajectories

In this paper we present a methodology for the semantic enrichment of trajectories. The objective of this process is to provide a semantic interpretation of a trajectory in term of behaviour. This has been achieved by enhancing raw trajectories with semantic information about moves and stops and by exploiting some domain knowledge encoded in an ontology. Furthermore, the reasoning mechanisms provided by the OWL ontology formalism have been exploited to accomplish a further semantic enrichment step that puts together the different levels of knowledge of the domain. A final example application shows the added power of the enrichment process in characterizing people behaviour.

Towards Semantic Interpretation of Movement Behavior

In this paper we aim at providing a model for the conceptual representation and deductive reasoning of trajectory patterns obtained from mining raw trajectories. This has been achieved by means of a semantic enrichment process, where raw trajectories are enhanced with semantic information and integrated with geographical knowledge encoded in an ontology. The reasoning mechanisms provided by the chosen ontology formalism are exploited to accomplish a further semantic enrichment step that gives a possible interpretation of discovered patterns in terms of movement behaviour. A sketch of the realised system, called Athena, is given, along with some examples to demonstrate the feasibility of the approach.

Building geospatial ontologies from geographical databases

The last few years have seen a growing interest in approaches that define methodologies to automatically extract semantics from databases by using ontologies. Geographic data are very rarely collected in a well organized way, quite often they lack both metadata and conceptual schema. Extracting semantic information from data stored in a geodatabase is complex and an extension of the existing methodologies is needed. We describe an approach to extracting a geospatial ontology from geographical data stored in spatial databases. To provide geospatial semantics we introduce new relations which define geospatial ontology that can serve as a basis for an advanced user querying system. Some examples of use of the methodology in the urban domain are presented.

DrC4.5: Improving C4.5 by means of prior knowledge

Classification is one of the most useful techniques for extracting meaningful knowledge from databases. Classifiers, e.g. decision trees, are usually extracted from a table of records, each of which represents an example. However, quite often in real applications there is other knowledge, e.g. owned by experts of the field, that can be usefully used in conjunction with the one hidden inside the examples. As a concrete example of this kind of knowledge we consider causal dependencies among the attributes of the data records. In this paper we discuss how to use such a knowledge to improve the construction of classifiers. The causal dependencies are represented via Bayesian Causal Maps (BCMs), and our method is implemented as an adaptation of the well known C4.5 algorithm.

MQL: An Algebraic Query Language for Knowledge Discovery

MQL is a system supporting the process of Knowledge Discovery. The central step of knowledge discovery, i.e. the application and combination of data mining steps, is expressed via queries written in an algebraic query language. The query processing engine exploits an XML based representation of queries and data mining models to favor the interoperability of different data mining tools and the expandibility of the system.

Improving Geodatabase Semantic Querying Exploiting Ontologies

Geospatial semantic querying to geographical databases has been recognized as an hot topic in GIS research. Most approaches propose to adopt an ontology as a knowledge representation structure on top of the database, representing the concepts the user can query. These concepts are typically directly mapped to database tables. In this paper we propose a methodology where the ontology is further exploited mapping axioms to spatial SQL queries. The main advantage of this approach is that semantic-rich geospatial queries can be abstractly represented in the ontology and automatically translated into spatial SQL queries.

Ontology-Based Business Plan Classification

The problem of providing small and medium enterprises (SMEs) with good self-assessment tools is becoming more and more urgent every day, not only because of increasing market competition, but also because of new rules for credit granting, as for example the ones referred to as Basel II.One of the critical issues in designing supporting tools is the quality of the knowledge embedded in them. We maintain that a better quality of decisions can be obtained by exploiting not only quantitative information but also qualitative information and expert knowledge. Here we present a system able to classify the quality of innovation plans of SMEs by exploiting both quantitative and qualitative knowledge embedded in ontology. The ontological approach allows representing qualitative knowledge in a very natural way and, as a consequence, we are able to elicit it by means that are natural for SME officers, as for example questionnaires.

Improving the Business Plan Evaluation Process: the Role of Intangibles

Abstract One of the main objectives of the European MUSING project is to design and test software tools in order to support the activities of small and medium sized businesses. In this paper we examine financial risk management and, more specifically, the self-assessment of business plans. The role of intangible assets is discussed, and we report on how intangible assets can be collected, how they can be represented, taking into account their semantic relationships, and how they can be used to build an analytical tool for business plans. The basic technology embedded in the tool is the construction of classification trees, a well-known technique in inductive learning. We show how using knowledge of intangible assets can improve the construction of the classifier, as proved by the testing carried out so far.