Browsing by Author "Manna, Marco"

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A Logic-based Framework for Characterizing Nexus of Similarity within Knowledge Bases
(Università della Calabria, 2024-04-04) Ricioppo, Aldo; Terracina, Giorgio; Manna, Marco
Complex challenges frequently necessitate the establishment of significant connections among diverse entities. For instance, selecting the optimal CV, finding a suitable apartment or deciphering the causal factors behind a specific disorder are just a few examples, and each of them requires deep understanding of what characterizes a set of entities. In addition to the aforementioned examples, having to recognize similarities between entities is a recurring phenomenon in a multitude of real-world scenarios. Researchers from different fields have presented various methodologies over the last century to evaluate these similarities, commonly called similarity. In recent times, momentum has increased with the advent of “Google Sets”, leading to the fervent development of strategies to amplify a given set of entities while preserving their original shared interconnected properties. As a consequence, current methodologies can encompass, in a way or another, relevant interconnected properties shared by entities, a concept we term the nexus of similarity. Machines have demonstrated considerable prowess in handling similarity evaluations, often returning numerical scores as a result, and set expansions, thus giving the end user the opportunity to observe entities similar to those he was looking for. However, there is a notable gap in formally characterizing the nexus of similarity in a way that is intelligible by machines and interpretable by human intellect, especially considering that the attempts that have gained the most traction thus far are often bound to cases very specific, such as those made with respect to RDF graphs. To address these gaps, our endeavor contributes significantly to the existing literature. We aim to construct a novel framework grounded in logical constructs, designed to systematically and autonomously delineate the nexus of similarity. Our framework extends not only to pairs of entities but also to sets of tuples of entities, which we term anonymous relations, within a knowledge base. Furthermore, our analysis encompasses an in-depth examination of the computational complexity inherent in the proposed framework. Such an investigation affords a thorough insight into its feasibility and a subsequent evaluation of scalability. Both are critical components for the framework’s practical application. In summary, our study pioneers a novel, knowledge-driven approach capable of characterizing nexus of similarity and that can be used as a means to perform entity set expansion in a manner clear and intelligible to humans. Two of the principal components integral to our framework will be the semantic resources, specifically selective knowledge bases, that in their essence are knowledge bases equipped with a particular supplementary algorithm, and the explanation languages, of which we will take into consideration one in particular, which in our opinion has all the ideal characteristics to be considered a language suitable to reveal the nexus of similarity as best as possible. During the work, we will also justify our design choices. With the help of these means we aim to fill the perceptible gap in the characterization of nexus of similarity. At the same time, we will show how some of the current approaches to entity set expansion do not notice that by their very nature this kind of expansions should take the form of a taxonomy rather than a chain. To resolve this other gap, we will introduce the concept of expansion graph.
Dyadic TGDs - A new paradigm for ontological query answering
(Università della Calabria, 2022-03-11) Marte, Cinzia; Greco, Gianluigi; Manna, Marco; Guerriero, Francesca; Leone, Nicola
Ontology-BasedQueryAnswering(OBQA)consistsinqueryingdata– bases bytakingontologicalknowledgeintoaccount.Wefocusona logical frameworkbasedonexistentialrulesor tuple generatingdepen- dencies (TGDs), alsoknownasDatalog±, whichcollectsthebasicde- cidable classesofTGDs,andgeneralizesseveralontologyspecification languages. While thereexistlotsofdifferentclassesintheliterature,inmost cases eachofthemrequiresthedevelopmentofaspecificsolverand, only rarely,thedefinitionofanewclassallowstheuseofexisting systems. Thisgapbetweenthenumberofexistentparadigmsandthe numberofdevelopedtools,promptedustodefineacombinationof Shy and Ward (twowell-knownclassesthatenjoygoodcomputational properties)withtheaimofexploitingthetooldevelopedfor Shy. Nevertheless,studyinghowtomergethesetwoclasses,wehavereal- ized thatitwouldbepossibletodefine,inamoregeneralway,the combinationofexistingclasses,inordertomakethemostofexisting systems. Hence, inthiswork,startingfromtheanalysisofthetwoaforemen- tioned existingclasses,wedefineamoregeneralclass,named Dyadic TGDs, thatallowstoextendinauniformandelegantwayallthede- cidable classes,whileusingtheexistentrelatedsystems.Atthesame time, wedefinealsoacombinationof Shy and Ward, named Ward+, and weshowthatitcanbeseenasaDyadicsetofTGDs. Finally,tosupportthetheoreticalpartofthethesis,weimplementa BCQ evaluationalgorithmfortheclass Ward+, thattakesadvantage of anexistingsolverdevelopedfor Shy.
Enhancing DLV for reasoning over streams: the LDSR language and its expressiveness
(Università della Calabria, 2024-04-09) Morelli, Maria Concetta; Terracina, Giorgio; Manna, Marco; Perri, Simona
Large-scale ontology-mediated query answering over OWL 2 RL ontologies
(Università della Calabria, 2022-03-11) Fiorentino, Alessio; Greco, Gianluigi; Manna, Marco
Ontology-mediated query answering (OMQA) is an emerging paradigm at the basis of many semantic-centric applications. In this setting, a conjunctive query has to be evaluated against a logical theory (knowledge base) consisting of an extensional database paired with an ontology, which provides a semantic conceptual view of the data. Among the formalisms that are capable to express such a conceptual layer, the Web Ontology Language OWL is certainly the most popular one. Reasoning over OWL is a very expensive task, in general. For that reason, expressive yet decidable fragments of OWL have been identi ed. Among them, we focus on OWL 2 RL, which o ers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources|such as DBpedia|fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. This thesis aims to provide a contribution in this setting. Primarily, we present DaRLing: an open-source Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. We describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability. Then, to reduce memory consumption and possibly optimize execution times of Datalog queries over large databases, we introduce novel techniques to determine an optimal indexing schema together with suitable body-orderings for Datalog rules, based on the concept of optimal evaluation plan. The ASP encoding of a planner for the computation of such plans is provided and explained in detail. The new approach is then compared with the standard execution plans implemented in stat-of-the-art Datalog systems over widely used ontological benchmarks.
A logic-based decision support system for the diagnosis of headache disorders according to the ichd - 3 international classification
(Università della Calabria, 2022-04-21) Costabile, Roberta; Manna, Marco; Greco, Gianluigi
Ontology-driven information extraction
(2017-07-20) Adrian, Weronika Teresa; Leone, Nicola; Manna, Marco
Information Extraction consists in obtaining structured information from unstructured and semi-structured sources. Existing solutions use advanced methods from the field of Natural Language Processing and Artificial Intelligence, but they usually aim at solving sub-problems of IE, such as entity recognition, relation extraction or co-reference resolution. However, in practice, it is often necessary to build on the results of several tasks and arrange them in an intelligent way. Moreover, nowadays, Information Extraction faces new challenges related to the large-scale collections of documents in complex formats beyond plain text. An apparent limitation of existing works is the lack of uniform representation of the document analysis from multiple perspectives, such as semantic annotation of text, structural analysis of the document layout and processing of the integrated knowledge. The recent proposals of ontology-based Information Extraction do not fully exploit the possibilities of ontologies, using them only as a reference model for a single extraction method, such as semantic annotation, or for defining the target schema for the extraction process. In this thesis, we address the problem of Information Extraction from homogeneous collections of documents i.e., sets of files that share some common properties with respect to the content or layout. We observe that interleaving semantic and structural analysis can benefit the results of the IE process and propose an ontology-driven approach that integrates and extends existing solutions. The contributions of this thesis are of theoretical and practical nature. With respect to the first, we propose a model and a process of Semantic Information Extraction that integrates techniques from semantic annotation of text, document layout analysis, object-oriented modeling and rule-based reasoning. We adapt existing solutions to enable their integration under a common ontological view and advance the state-of-the-art in the field of semantic annotation and document layout analysis. In particular, we propose a novel method for automatic lexicon generation for semantic annotators, and an original approach to layout analysis, based on common labels identification and structure recognition. We design and implement a framework named KnowRex that realize the proposed methodology and integrates the elaborated solutions.