1,720,993 research outputs found
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
Semantic 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
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
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
Holistic graph-based document representation and management for open science
No full textWhile most previous research focused only on the textual content of documents, advanced support for document management in digital libraries, for open science, requires handling all aspects of a document: from structure, to content, to context. These different but inter-related aspects cannot be handled separately and were traditionally ignored in digital libraries. We propose a graph-based unifying representation and handling model based on the definition of an ontology that integrates all the different perspectives and drives the document description in order to boost the effectiveness of document management. We also show how even simple algorithms can profitably use our proposed approach to return relevant and personalized outcomes in different document management tasks
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
A formal model of the Semantic Web Service Ontology (WSMO)
No full textSemantic Web Service, one of the most significant research areas within the Semantic Web vision, has attracted increasing attention from both the research community and industry. The Web Service Modelling Ontology (WSMO) has been proposed as an enabling framework for the total/partial automation of the tasks (e.g., discovery, selection, composition, mediation, execution, monitoring, etc.) involved in both intra- and inter-enterprise integration of Web services. To support the standardisation and tool support of WSMO, a formal model of the language is highly desirable. As several variants of WSMO have been proposed by the WSMO community, which are still under development, the syntax and semantics of WSMO should be formally defined to facilitate easy reuse and future development. In this paper, we present a formal Object-Z formal model of WSMO, where different aspects of the language have been precisely defined within one unified framework. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in this paper, can be used to identify and eliminate errors present in existing documentation. © 2011 Elsevier Ltd. All rights reserved
An Ontology-driven Architecture for Intelligent Tutoring Systems with an Application to Learning Object Recommendation
No full textThe state-of-the-art in Artificial Intelligence (AI), the Internet, and the computational power reached by current technologies, allow much more advanced thinking about Intelligent Tutoring Systems than their original definition. The KEPLAIR project envisions an online platform, designed to help all players involved in educational endeavors, especially learners, to improve performance and effectiveness of their activities. Using leading edge AI solutions, KEPLAIR will act as a personalized assistant, helping its users in the entire educational experience, from goal elicitation through learning path definition, selection of materials, performance/attainment testing, analytics and report building. This paper introduces the architecture and functionalities of KEPLAIR as well as illustrating a new methodology for Learning Object (LO) suggestion based on personal profile informatio
XIX IRCDL: The Conference on Information and Research science Connecting to Digital and Library science 2023
Full text linkKnowledge Graphs for the Web Economy: The CHiPS&BITS Project on Cultural Heritage
No full textThe growing demand for Cultural Heritage fruition is making it a major economic driver, also in connection with its tourism-related aspects. The current solutions available on the Web are still unable to provide satisfactory support to the various kinds of stakeholders and to the different applications. The History of Computing, as a peculiar, relevant and currently underinvestigated branch of Cultural Heritage, raises additional challenges and provides new opportunities, also in connection with a significant economic flow it generates. The variety and complexity of issues connected to this domain call for even more advanced solutions. In this paper we introduce the CHIPS&BITS project, which tackles these problems using a knowledge-based approach and leverages a novel framework that can meet the needs associated to this specific domain in a better way compared to standard Semantic Web approaches. We describe the contributions of the project to overcome the limitations of the state of the art, and focus on some of its more peculiar and original features, among which the definition of suitable ontologies to describe the complexity of the domain and the use of Artificial Intelligence algorithms for giving the users an advanced and personalized experience
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