Paolo Ceravolo

Bottom-Up Extraction and Trust-Based Refinement of Ontology Metadata

We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run

Knowledge Extraction from Semi-structured Data Based on Fuzzy Techniques

In this work we propose a fuzzy technique to compare XML documents belonging to a semi-structured flow and sharing a common vocabulary of tags. Our approach is based on the idea of representing documents as fuzzy bags and, using a measure of comparison, evaluating structural similarities be-tween them. Then we suggest how to organize the extracted knowledge in a class hierarchy, choosing a technique related to the domain of interest, later to be converted into a user ontology.

Modeling Semantics of Business Rules

Organizations are showing growing interest in paradigms where business models and services compatibility is adaptively tested, e.g. by applying automatic systems to check business rules consistency. In this paper, we build on the original proposal by OMG of using first-order logics for representing business vocabularies and propose an approach based on description logics (DL) as formal logic support for business rules. By translating SBVR business vocabularies and rules into OWL DL ontologies, standard inference procedures of DL can be applied to check the business model consistency in the open-world, which is the default interpretation of SBVR models. Moreover, SBVR facts that cannot be expressed with OWL DL are translated into SWRL rules so that they can then be integrated with the starting ontology and evaluated, albeit within the boundaries of the closed-world made of known facts. We exemplify this process by translating a fragment of the EU-Rent example, drawn from the SBVR specification, into a OWL+SWRL knowledge base.

Toward Sensor-Based Context Aware Systems

This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information.

Chapter 13 Bottom-up extraction and maintenance of ontology-based metadata

Abstract In this chapter, several flexible techniques aimed at extracting, maintaining and enriching semantic-web style metadata are discussed. Such techniques were designed for being applied in the framework of dynamic Communities of Practice (CoP) interactions. Namely, we present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete objects. Unlike huge, “supply-side” normative ontologies, our bottom-up ontologies are based on use of implicit and, therefore, parsimonious part-whole and is-a relations. This makes them suitable for the ad-hoc style of conceptualization used within communities of practice and peer-to-peer (P2P) communities. Also we discuss how metadata based on bottom-up ontologies can be associated with a flexible degree of trust by collecting user feedback. Our bottom-up extraction method complements current practice, where, as a rule, ontologies are built top-down. It is not claimed that bottom-up construction is a generally valid recipe; rather, the approach is intended to enrich the ontology developers palette when designing and implementing Semantic Web applications.

Adding a Trust Layer to Semantic Web Metadata

We outline the architecture of a modular Trust Layer that can be superimposed to generic semantic Web-style metadata generation facilities. Also, we propose an experimental setting to generate and validate trust assertions on classification metadata generated by different tools (including our ClassBuilder) after a process of metadata standardization. Our experimentation is aimed at validating the role of our Trust Layer as a non-intrusive, user-centered quality improver for automatically generated metadata.

Business Metrics Discovery by Business Rules

This work contributes to the results of the TEKNE projec, a project aimed at developing a framework for Business Process Management (BPM), supporting the designer with a set of performance indicators. The indicators drive the designer in estimating if the process comply to the objectives and when necessary enable re-engineering of the process. In particular this paper discuses how to derive performance indicators directly from requirements expressed in a Business Rules (BR) format.

A ontology-based process modelling for XP

We describe the Extreme Programming Ontology (XPO), a formal model specifying the main concepts used in the extreme programming methodology and their properties. XPOs modular structure was developed using the usual normative top down approach to software engineering process modeling. It relies on a set of core components rooted in three main concepts: organisational role, product and phase. Besides being useful for indexing relevant documents and XP artifacts such as user stories and Wiki pages, XPO is aimed at being a sound basis for nonintrusive analysis of agile processes, mining process data about programmers activity and repositories content in order to extract new concepts potentially identifying critical factors in agile software development. Extension to XPO are also discussed, including other agile methodologies and more general software engineering concepts.

Exploiting Participatory Design in Open Innovation Factories

In this paper we describe a methodology and a set of tools that support the exploitation of ideas, suggestions and proposals coming from different sources, internal and external to the organization (e.g. customers and employees). Items extracted from incoming message flows are used as a basis of a participatory design process. In this context, we discuss the design principles of an environment we call Open Innovation Factory, supporting collaborative design of new products and services.

Towards an agent-based architecture for managing uncertainty in situation awareness

In computing, Ambient Intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. The ambient intelligence paradigm is characterized by systems and technologies founded on a situational computing and, more generally, situation awareness substratum dealing with situational context representation and reasoning. At the same time, the global information infrastructure is becoming more and more pervasive and human computer interactions are performed in diverse situations, using a variety of mobile devices and across multiple communication channels. Nevertheless, recent advances in multi-sensors systems, multimodal access has yet to develop its full potential, due to imperfect observations, time-dependence of multimedia predicates, and to difficulties in conjoining facts coming from different modal streams. Hence, the knowledge upon which the context/situation aware paradigm is built is rather vague. To deal with this shortcoming, in this paper we propose a distributed architecture aimed at identifying and reasoning about the current situation of involved entities. Specifically, this work presents an hybrid architecture attaining a synergy among Agent Paradigm (AP), Situation Theory (ST) and semantic fuzzy modeling to efficiently support situation awareness in uncertain environments.