1,721,088 research outputs found
Integration Strategy and Tool between Formal Ontology and Graph Database Technology
Full text linkOntologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling
Proc. 1st AI*IA Workshop on Intelligent Techniques At LIbraries and Archives (IT@LIA 2015)
No full textSemantic Web Services Ingestion in a Process Mining Framework
No full textProcess mining can be applied to systems for the management of Workflow, Business Processes and, in general, Process-Aware Information to discover and analyse implicit processes. In recent times, semantic interoperability has also become of crucial importance in the area of business processes. In particular, interoperability enables the discovery of new knowledge about processes by exploiting automatic reasoning on information originating from external formal descriptions. To this end, the use of Semantic Web technologies could be one possible solution. Given the different paradigms underpinning the two fields of research, adaptations are needed to realise this solution. In this paper, a possible mapping between Inductive Logic Programming and Semantic Web rules is proposed to discover additional knowledge that can be integrated into the process mining techniques outcomes
Experiences on the Improvement of Logic-Based Anaphora Resolution in English Texts
No full textAnaphora resolution is a crucial task for information extraction. Syntax-based approaches are based on the syntactic structure of sentences. Knowledge-poor approaches aim at avoiding the need for further external resources or knowledge to carry out their task. This paper proposes a knowledge-poor, syntax-based approach to anaphora resolution in English texts. Our approach improves the traditional algorithm that is considered the standard baseline for comparison in the literature. Its most relevant contributions are in its ability to handle differently different kinds of anaphoras, and to disambiguate alternate associations using gender recognition of proper nouns. The former is obtained by refining the rules in the baseline algorithm, while the latter is obtained using a machine learning approach. Experimental results on a standard benchmark dataset used in the literature show that our approach can significantly improve the performance over the standard baseline algorithm used in the literature, and compares well also to the state-of-the-art algorithm that thoroughly exploits external knowledge. It is also efficient. Thus, we propose to use our algorithm as the new baseline in the literature
Towards a general model for abstract argumentation frameworks
No full textIn its original definition, the Abstract Argumentation framework considers atomic claims and a binary attack relationship among them, based on which different semantics would select subsets of claims consistently supporting the same position in a dispute or debate. While attack is obviously the core relationship in this setting, in more complex (and in many real-world) situations additional information may help, or might even be crucial, in determining such positions, and especially those that are going to win the debate. Examples are bipolarity (considering also the support relationship between pairs of claims) and weights (assigning different importance to different elements of the framework). These additional features have often been considered separately, yielding incompatible or anyhow disjoint models for argumentation frameworks. In this paper we propose a model that unifies all these perspectives, and further extends them by allowing to express contextual information associated to the arguments, in addition to their relationships
The GraphBRAIN System for Knowledge Graph Management and Advanced Fruition
No full textThe possibility of inter-relating different information items is crucial in the perspective of enhanced storage, handling and fruition of knowledge. GraphBRAIN is a general-purpose tool that allows to design and collaboratively populate knowledge graphs, and provides advanced solutions for their fruition, consultation and analysis. Its functionalities are also provided as Web services to other applications. A peculiarity of GraphBRAIN is its fusion of methods and tools coming from different research areas: ontologies to describe such variated knowledge, collaborative tools to collect the knowledge scattered across many people, graph databases to store the knowledge base, data mining and social network analysis tools for personalized fruition of the collected knowledge. It is currently used as the knowledge management platform in a tourism-related project
Unsupervised author identification and characterization
No full textAuthor identification is a hot topic, especially in the Internet age. Following our previous work in which we proposed a novel approach to this problem, based on relational representations that take into account the structure of sentences, here we present a tool that computes and visualizes a numerical and graphical characterization of the authors/texts based on several linguistic features. This tool, that extends a previous language analysis tool, is the ideal complement to the author identification technique, that is based on a clustering procedure whose outcomes (i.e., the authors’ models) are not human-readable. Both approaches are unsupervised, which allows them to tackle problems to which other state-of-the-art systems are not applicable
Alla ricerca dell’arca perduta. Ovvero: dov’è il digital cultural heritage?, in: Conferenza GARR 2018 "Data Revolution" - Università di Cagliari - Dipartimento di Filologia, Letteratura e Linguistica Selected Papers - Cagliari, 3-5 ottobre 2018
No full textL’identificazione del digital cultural heritage (DCH) asserita nell’art. 2 delle Conclusioni del Consiglio dell'UE del 21 maggio 2014 sul Patrimonio culturale come risorsa strategica per un'Europa sostenibile (2014 / C 183/08) rende indispensabile ripensare i processi di digitalizzazione e di co-creazione digitale, al fine di individuare approcci e metodi che consentano di individuare cosa e quanto possa essere riconoscibile come patrimonio culturale tra le risorse digitali create fino a oggi e in produzione. Questo paper intende proporre un approccio metodologico innovativo ai processi di digitalizzazione e di co-creazione delle entità digitali che, garantendone fin dalla progettazione la conservazione a lungo termine, conferisca loro il ruolo e la funzione di memoria dell’Evo Digitale contemporaneo e, in tal modo, li caratterizzi quali potenziale DCH
Toward automatic floor plan interpretation
No full textBeyond exploitation and management of document collections based on the syntactic level, approaching the semantic level can open new perspectives for the collection users. This is particularly challenging in technical documents, where most relevant information is implicit in the graphic. This paper deals with architectural floorplans, proposing an approach based on formal representation and reasoning for their understanding and interpretation. The results of our study show that it is a viable and promising line of research
LPG-Based Knowledge Graphs: A Survey, a Proposal and Current Trends
Full text linkA significant part of the current research in the field of Artificial Intelligence is devoted to knowledge bases. New techniques and methodologies are emerging every day for the storage, maintenance and reasoning over knowledge bases. Recently, the most common way of representing knowledge bases is by means of graph structures. More specifically, according to the Semantic Web perspective, many knowledge sources are in the form of a graph adopting the Resource Description Framework model. At the same time, graphs have also started to gain momentum as a model for databases. Graph DBMSs, such as Neo4j, adopt the Labeled Property Graph model. Many works tried to merge these two perspectives. In this paper, we will overview different proposals aimed at combining these two aspects, especially focusing on possibility for them to add reasoning capabilities. In doing this, we will show current trends, issues and possible solutions. In this context, we will describe our proposal and its novelties with respect to the current state of the art, highlighting its current status, potential, the methodology, and our prospect
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