Agent‐oriented and ontology‐driven digital libraries: the IndianaMAS experience

Abstract

This paper describes IndianaMAS, a multiagent system able to automatically classify and manage images, sketches, and multilingual documents in a cultural heritage domain. The latter has been formalized by means of an ontology, which enables the semantic integration of heterogeneous data from different sources, drives the agent communication with the internal and external environment, and provides an abstract and human- readable interface between the system and the user. IndianaMAS is able to expose to the world the classified data via a digital library. Modularity and reusability are the key engineering principles followed in the system design and implementation. We present the details of the IndianaMAS system and discuss how its archi- tecture can be generalized to create – with the minimal effort – systems addressing similar classification, storage, and management problems, but operating in different domains and driven by different ontologies. The concrete problems we faced and their solutions are described to share our lesson learned and, at the same time, to show the applicability and reusability of our modular approach based on ontologies, agents, and digital libraries

A platform for P2P agent‐based collaborative applications

“…As part of future work, we plan to exploit the extended version of JADE to build distributed applications operating directly in the end‐user environment, further demonstrating the suitability of the combined agent‐ and peer‐to‐peer paradigms for the development of collaborative applications that assist end‐users in case of problems. With this perspective, we will exploit MAS runtime verification techniques (for example, see the works of Briola et al) and runtime enforcement techniques (for example, see the works of Riganelli et al) to improve the reliability of our layer, and we will further exploit and improve the ontological support to the services modeling and invocation (as, for example, done in the work of Briola et al).…”

Enabling the generation of web applications from mockups

“…In the future, we would like to develop user help and guidance components to assist EU developers during the visual specification process, by extending the proposed architecture with intelligent agents, 39 capable of understanding the intent of the user. 40 Moreover, following the design pattern philosophy, we would like to create and maintain a repository of hierarchically organized UI components for commonly used business entities to improve the effectiveness of the proposed visual modeling process.…”

OntoScene, A Logic-Based Scene Interpreter: Implementation and Application in the Rock Art Domain

“…As far as the classification of images in the rock art domain is concerned, we refer to our previous work within the IndianaMAS project, where ad hoc detection and classification algorithms were developed (Briola et al . 2017;Mascardi et al .…”

“…2014;Briola et al . 2014;Briola 2016;Briola et al . 2017) where agents and MASs devoted to multilingual text understanding, hand-drawn sketch recognition, human interaction, and integration of digital libraries, cooperate and coordinate with the OntoScene framework to classify heterogeneous digital objects.…”

“…• Detector and Classifier, two external modules (whose functioning is outside the scope of this paper, and which could be based on our own previous proposals (Briola et al . 2017) or on more recent deep learning techniques) that partition the input image into tokens and associate a list of classifications with them, respectively; • SceneInterpreter, the Prolog core of OntoScene; it reasons on a symbolic representation of images that make up a scene and returns their interpretations;…”