Giuseppe Pirrò

Università della Calabria | Università della Calabria · Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica

This paper studies the novel problem of influence-based community deception. Tackling this problem amounts to devising tools to protect the users of a community from being discovered by community detection algorithms. The novel setting considers networks that have both edge directions and models the influence of nodes as edge weights. We present a...

This paper introduces an end-to-end learning framework called LoGNet (Local and Global Triple Embedding Network) for triple-centric tasks in knowledge graphs (KGs). LoGNet is based on graph neural networks (GNNs) and combines local and global triple embedding information. Local triple embeddings are learned by treating triples as sequences. Global...

Community deception is about hiding a target community that wants to remain below the radar of community detection algorithms. The goal is to devise algorithms that, given a maximum number of updates (e.g., edge additions and removal), strive to find the best way to perform such updates in order to hide the target community inside the community str...

Community deception tackles the following problem: given a target community \(\mathcal {C}\) inside a network \(G\) and a budget of updates \(\beta \) (e.g., edge removal and additions), what is the best way (i.e., optimization of some function \(\phi _{G}({\mathcal {C}})\)) to perform such updates in a way that \(\mathcal {C}\) can escape to a det...

Community detection algorithms that analyze networks to identify communities of nodes are an essential part of the network analysis toolkit used daily by different analysts (e.g., data scientists and law enforcement). However, there is not enough awareness that members of a community $\comH$ (either revealed or not) inside a network $G$ could a...

Knowledge graphs (KGs) are a useful source of background knowledge to (dis)prove facts of the form (s, p, o). Finding paths between s and o is the cornerstone of several fact-checking approaches. While paths are useful to (visually) explain why a given fact is true or false, it is not completely clear how to identify paths that are most relevant to...

Existing network embedding approaches tackle the problem of learning low-dimensional node representations. However, networks can also be seen in the light of edges interlinking pairs of nodes. The broad goal of this paper is to introduce edge-centric network embeddings. We present an approach called ECNE, which instead of computing node embeddings...

There is a variety of available approaches to learn graph node embeddings. One of their common underlying task is the generation of (biased) random walks that are then fed into representation learning techniques. Some techniques generate biased random walks by using structural information. Other approaches, also rely on some form of semantic inform...

Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the pro...

Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes in a (knowledge) graph. To the best of our knowledge, none of them has tackled the problem of embed...

We tackle fact checking using Knowledge Graphs (KGs) as a source of background knowledge. Our approach leverages the KG schema to generate candidate evidence patterns, that is, schema-level paths that capture the semantics of a target fact in alternative ways. Patterns verified in the data are used to both assemble semantic evidence for a fact and...

We research the problem of building knowledge maps of graph-like information. We live in the digital era and similarly to the Earth, the Web is simply too large and its interrelations too complex for anyone to grasp much of it through direct observation. Thus, the problem of applying cartographic principles also to digital landscapes is intriguing....

In this paper, we research the community deception problem. Tackling this problem consists in developing techniques to hide a target community (C) from community detection algorithms. This need emerges whenever a group (e.g., activists, police enforcements, or network participants in general) want to observe and cooperate in a social network while...

This paper investigates meta structures, schema-level graphs that abstract connectivity information among a set of entities in a knowledge graph. Meta structures are useful in a variety of knowledge discovery tasks ranging from relatedness explanation to data retrieval. We formalize the meta structure computation problem and devise efficient automa...

We demonstrate RECAP, a tool that explains relatedness between entities in Knowledge Graphs (KGs) and implements a query by relatedness paradigm that allows to retrieve entities related to those in input. One of the peculiarities of RECAP is that it does not require any data preprocessing and can combine knowledge from multiple KGs. The underlying...

Graph navigational languages allow to specify pairs of nodes in a graph subject to the existence of paths satisfying a certain regular expression. Under this evaluation semantics, connectivity information in terms of intermediate nodes/edges that contributed to the answer is lost. The goal of this paper is to introduce the GeL language, which provi...

The community deception problem is about how to hide a target community C from community detection algorithms. The need for deception emerges whenever a group of entities (e.g., activists, police enforcements) want to cooperate while concealing their existence as a community. In this paper we introduce and formalize the community deception problem....

We research the problem of building knowledge maps of graph-like information. There exist well-consolidated cartographic principles and techniques for mapping physical landscapes. However, we live in the digital era and similarly to the Earth, the Web is simply too large and its interrelations too complex for anyone to grasp much of it through dire...

We demonstrate S+EPPs, a system that provides fast construction of bisimulation summaries using graph analytics platforms, and then enhances existing SPARQL engines to support summary-based exploration and navigational query optimization. The construction component adds a novel optimization to a parallel bisimulation algorithm implemented on a mult...

We demonstrate S+EPPs, a system that provides fast construction of bisimulation summaries using graph analytics platforms, and then enhances existing SPARQL engines to support summary-based exploration and navigational query optimization. The construction component adds a novel optimization to a parallel bisimulation algorithm implemented on a mult...

As of today, there exists no standard language for querying Linked Data on the Web, where navigation across distributed data sources is a key feature. A natural candidate seems to be SPARQL, which recently has been enhanced with navigational capabilities thanks to the introduction of property paths (PPs). However, the semantics of SPARQL restricts...

We introduce Extended Property Paths (EPPs), a significant enhancement of SPARQL property paths. EPPs allow to capture in a succinct way a larger class of navigational queries than property paths. We present the syntax and formal semantics of EPPs and introduce two different evaluation strategies. The first is based on an algorithm implemented in a...

The Web of Linked Data is a huge graph of distributed and interlinked datasources fueled by structured information. This new environment calls for formal languages and tools to automatize navigation across datasources (nodes in such graph) and enable semantic-aware and Web-scale search mechanisms. In this article we introduce a declarative navigati...

We present the MaGe system, which helps users and devel-opers to build maps of the Web graph. Maps abstract and represent in a concise and machine-readable way regions of information on the Web.

This paper presents GuLP a graph query language that enables to declaratively express preferences. Preferences enable to order the answers to a query and can be stated in terms of nodes/edge attributes and complex paths. We present the formal syntax and semantics of GuLP and a polynomial time algorithm for evaluating GuLP expressions. We describe a...

The normative version of RDF Schema (RDFS) gives non-standard (intensional) interpretations to some standard notions such as classes and properties, thus departing from standard set-based semantics. In this paper we develop a standard set-based (extensional) semantics for the RDFS vocabulary while preserving the simplicity and computational complex...

A map is an abstract visual representation of a region, taken from a given space, usually designed for final human consumption. Traditional cartography focuses on the mapping of Euclidean spaces by using some distance metric. In this paper we aim at mapping the Web space by leveraging its relational nature. We introduce a general mathematical frame...

There is renewed interest in graph query languages, where querying Web data (such as linked data, or on-line social networks) is considered an important application scenario. Implementing responsive evaluation techniques for queries on Web graphs (where navigation causes additional data to be discovered on the fly) demands a judicious choice of lan...

The main goal of current Web navigation languages is to retrieve set of nodes reachable from a given node. No information is provided about the fragments of the Web navigated to reach these nodes. In other words, information about their connections is lost. This paper presents an efficient algorithm to extract relevant parts of these Web fragments...

Wikipedia is a huge source of multilingual knowledge curated by human contributors. Wiki articles are independently written in the various languages and may cover different perspectives about a given subject. The aim of this paper is to exploit Wikipedia multilingual information for knowledge enrichment and summarization. Investigating the link str...

The number of available Internet services increases every day. This trend demands distributed models and architectures to support scalability as well as semantics to enable efficient publication and retrieval of services. Two common approaches toward this goal are semantic overlay networks (SONs) and distributed hash tables (DHTs) with semantic ext...

In an open context such as the Semantic Web, information providers usually rely on different ontologies to semantically characterize contents. In order to enable interoperability at a semantic level, ontologies underlying information sources must be linked by discovering alignments, that is, set of correspondences or mappings. The aim of this paper...

Bioinformatics tasks may become very complex and usually require to manually integrate both data and results from different knowledge sources and tools. In this scenario, an integrated environment for designing and executing complex biological workflows is a must. Even though several efforts are trying to cope with this aspects, they mostly focus o...

Semantic similarity between words aims at establishing resemblance by interpreting the meaning of the words being compared. The Semantic Web can benefit from semantic similarity in several ways: ontology alignment and merging, automatic ontology construction, semantic-search, to cite a few. Current approaches mostly focus on computing similarity be...

Ontology mapping, or matching, aims at identifying correspondences among entities in different ontologies. Several strands of research come up with algorithms often combining multiple mapping strategies to improve the mapping accuracy. However, few approaches have systematically investigated the requirements of a mapping system both from the functi...

This paper describes a framework for implementing distributed ontology-based knowledge management systems (DOKMS). The framework, in particular, focuses on knowledge management within organizations. It investigates the functional requirements to enable Individual Knowledge Workers (IKWs) and distributed communities (e.g., project teams) to create,...

The increasing number of available online services demands distributed architectures to promote scalability as well as semantics to enable their precise and efficient retrieval. Two common approaches toward this goal are Semantic Overlay Networks (SONs) and Distributed Hash Tables (DHTs) with semantic extensions. This paper presents ERGOT, a system...

This chapter introduces a distributed framework for OKM (Organizational Knowledge Management) which allows IKWs (Individual Knowledge Workers) to build virtual communities that manage and share knowledge within workspaces. The proposed framework, called K-link+, supports the emergent way of doing business of IKWs, which allows users to work at any...

In many research fields such as Psychology, Linguistics, Cognitive Science and Artificial Intelligence, computing semantic similarity between words is an important issue. In this paper a new semantic similarity metric, that exploits some notions of the feature-based theory of similarity and translates it into the information theoretic domain, which...

Ontology Mapping is a mandatory requirement for enabling semantic interoperability among different agents and services relying on different ontologies. This aspect becomes more critical in Peer-to-Peer (P2P) networks for several reasons: (i) the number of different ontologies can dramatically increase; (ii) mappings among peer ontologies have to be...

The soaring number of available online services calls for distributed architectures to promote scalability, fault- tolerance and semantics; to provide meaningful descriptions of services; and to support their efficient retrieval. Current approaches exploit either Semantic Overlay Networks (SONs) or Distributed Hash Tables (DHTs) sweetened with some...

In many research fields such as Psychology, Linguistics, Cognitive Science, Biomedicine, and Artificial Intelligence, computing semantic similarity between words is an important issue. In this paper we present a new semantic similarity metric that exploits some notions of the early work done using a feature based theory of similarity, and translate...

The Grid has rapidly moved from a toolkit-centered approach, composed of a set of middleware tools, toward a more application-oriented Service Oriented Architecture in which resources are exposed as services. The soaring number of available services advocates distributed and semantic-based discovery architectures. Distribution promotes scalability...

Ontology Mapping is mandatory for enabling semantic interoperability among different agents and services making use of different ontologies. The ontology mapping problem becomes more critical in P2P systems since: (i) the number of different ontologies can dramatically increase; (ii) ontology mapping must be performed on the fly and only on parts o...

Semantic search, one of the ideas underpinning the Semantic Web vision, is receiving attention from the scientific community since it can significantly improve keyword-based search. In this paper we present the SECCO ontology mapping algorithm that enables distributed semantic search of Knowledge Objects (e.g., textual documents, emails) within Pee...

Ontology mapping is a key problem to be solved for the success of the Semantic Web and related technologies. An ontology mapping algorithm aims at finding correspondences (or mappings) between entities of the source and target ontologies by combining several matching components, i.e., individual matchers, that exploit one or more sources of informa...

Peer-to-Peer architectures for content and knowledge management foster the creation of communities of workers in which effective knowledge and information sharing takes place. In such communities, workers have similar capabilities in providing other workers with data and/or services and are autonomous in managing their own knowledge objects. Since...

In recent years, organisations are blurring their boundaries interacting with other organisations. This process fostered new business paradigms and organisational forms that transcend the previous static and closed competitive models and move to flexible and collaborative ways of working. Examples of new models are the extended enterprise (EE) and...

Peer-to-Peer architectures for content and knowledge management foster the creation of communities of workers in which effective knowledge and information sharing takes place. In such communities, workers have similar capabilities in providing other workers with data and/or services and are autonomous in managing their own knowledge objects. Since...

Peer-to-Peer (P2P) architectures for content and knowledge management enable autonomous peers to interoperate in a decentralized and distributed fashion for fulfilling individual and/or common goals. These architectures foster the creation of communities of Individual Knowledge Workers (IKWs) in which effective knowledge and information sharing tak...

Recently the Ontology Mapping Problem (OMP) has been identified as a key factor towards the success of the Semantic Web and related applications. This problem arises since it is possible for different people to give, through ontologies, different conceptualizations of the same (or overlapping) knowledge domain. In order to tackle the OMP several al...

Peer-to-peer (P2P) systems enable users to build individual and cooperating autonomous communities. Recently the peer-to-peer paradigm has been proposed as a technological support for Distributed Knowledge Management (DKM). DKM solutions allow an easy sharing of knowledge created by Individual Knowledge Workers (IKWs) inside communities. In such dy...

In a semantic P2P network, peers use separate ontologies and rely on alignments between their ontologies for translating queries. Nonetheless, alignments may be limited —unsound or incomplete— and generate flawed translations, leading to unsatisfactory answers. In this paper we present a trust mechanism that can assist peers to select those in the...

This paper introduces a semantically-driven recursive RDF link-based navigation system for the Web of Data. It combines the power of regular expressions with triggers based on SPARQL queries, to perform controlled exploration and retrieval of semantic data sources.