Claudia d’Amato

University of Bari Aldo Moro | Università di Bari · Department of Computer Science

The goal of Link Prediction is to predict missing facts inKnowledge Graphs that are inherently incomplete. Embedding Models are generally adopted for this purpose since they are effective and scalable. However, they lack both interpretability and, more importantly,explainability, which is crucial in many tasks and domains. To fill this gap, post-ho...

The paper surveys ongoing research on hyperdimensional computing and vector symbolic architectures which represent an alternative approach to neural computing with various advantages and interesting specific properties: transparency, error tolerance, sustainability. In particular, it can be demonstrated that hyperdimensional patterns are well-suite...

Tackling the problem of learning probabilistic classifiers from incomplete data in the context of Knowledge Graphs expressed in Description Logics, we describe an inductive approach based on learning simple belief networks. Specifically, we consider a basic probabilistic model, a Naive Bayes classifier, based on multivariate Bernoullis and its exte...

The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and m...

The notion of Knowledge Graph stems from scientific advancements in diverse research areas such as Semantic Web, databases, knowledge representation and reasoning, NLP, and machine learning, among others. The integration of ideas and techniques from such disparate disciplines presents a challenge to practitioners and researchers to know how current...

The Semantic Web (SW) is characterized by the availability of a vast amount of semantically annotated data collections. Annotations are provided by exploiting ontologies acting as shared vocabularies. Additionally ontologies are endowed with deductive reasoning capabilities which allow to make explicit knowledge that is formalized implicitly. Along...

Embedding models have been successfully exploited for predictive tasks on Knowledge Graphs (KGs). We propose TransROWL-HRS, which aims at making KG embeddings more semantically aware by exploiting the intended semantics in the KG. The method exploits schema axioms to encode knowledge that is observed as well as derived by reasoning. More knowledge...

As humans, we can deduce more from the data graph of Figure 2.1 than what the edges explicitly indicate. We may deduce, for example, that the am festival ((eidis)) will be located in Santiago, even though the graph does not contain an edge (eidis)— location →(santiago). We may further deduce that the cities connected by flights must have some airpo...

While deductive knowledge is characterized by precise logical consequences, inductively acquiring knowledge involves generalizing patterns from a given set of input observations, which can then be used to generate novel but potentially imprecise predictions. For example, from a large data graph with geographical and flight information, we may obser...

In this chapter, we discuss some of the most prominent knowledge graphs that have emerged in the past years. We begin by discussing open knowledge graphs, most of which have been published on the Web per the guidelines and protocols described in Chapter 9. We later discuss enterprise knowledge graphs that have been created by companies from diverse...

In this chapter we describe extensions of the data graph–relating to schema, identity, and context–that provide additional structures for accumulating knowledge. Henceforth, we refer to a data graph as a collection of data represented as nodes and edges using one of the models discussed in Chapter 2. We refer to a knowledge graph as a data graph po...

Independent of the (kinds of) source(s) from which a knowledge graph is created, the resulting initial knowledge graph will usually be incomplete, and will often contain duplicate, contradictory or even incorrect statements, especially when taken from multiple sources. After the initial creation and enrichment of a knowledge graph from external sou...

At the foundation of any knowledge graph is the principle of first applying a graph abstraction to data, resulting in an initial data graph. We now discuss a selection of graph-structured data models that are commonly used in practice to represent data graphs. We then discuss the primitives that form the basis of graph query languages used to inter...

In this chapter, we discuss the principal techniques by which knowledge graphs can be created and subsequently enriched from diverse sources of legacy data that range from plain text to structured formats (and anything in between). The appropriate methodology to follow when creating a knowledge graph depends on the actors involved, the domain, the...

Beyond assessing the quality of a knowledge graph, there exist techniques to refine the knowledge graph, in particular to (semi-)automatically complete and correct the knowledge graph [Paul-heim, 2017], aka knowledge graph completion and knowledge graph correction, respectively. As distinguished from the creation and enrichment tasks outlined in Ch...

While it may not always be desirable to publish knowledge graphs (for example, those that offer a competitive advantage to a company [Noy et al., 2019]), it maybe desirable or even required to publish other knowledge graphs, such as those produced by volunteers [Lehmann et al., 2015, Mahdisoltani et al., 2015, Vrandecic and Krotzsch, 2014], by publ...

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as lang...

This book constitutes the proceedings of the satellite events held at the 18th Extended Semantic Web Conference, ESWC 2021, in June 2021. The conference was held online, due to the COVID-19 pandemic. During ESWC 2021, the following six workshops took place: 1) the Second International Workshop on Deep Learning meets Ontologies and Natural Language...

Embedding models have been successfully exploited for Knowledge Graph refinement. In these models, the data graph is projected into a low-dimensional space, in which graph structural information are preserved as much as possible, enabling an efficient computation of solutions. We propose a solution for injecting available background knowledge (sche...

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of ent...

In the context of the Semantic Web regarded as a Web of Data, research efforts have been devoted to improving the quality of the ontologies that are used as vocabularies to enable complex services based on automated reasoning. From various surveys it emerges that many domains would require better ontologies that include non-negligible constraints f...

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languag...

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languag...

The Web of Data is one of the perspectives of the Semantic Web. In this context, concept learning services, supported by multirelational machine learning, have been integrated in various tools for knowledge engineers to carry out several tasks related to the construction, completion and maintenance of the knowledge bases: essentially they are used...

The two volume set LNCS 12506 and 12507 constitutes the proceedings of the 19th International Semantic Web Conference, ISWC 2020, which was planned to take place in Athens, Greece, during November 2-6, 2020. The conference changed to a virtual format due to the COVID-19 pandemic. The papers included in this volume deal with the latest advances in f...

The two volume set LNCS 12506 and 12507 constitutes the proceedings of the 19th International Semantic Web Conference, ISWC 2020, which was planned to take place in Athens, Greece, during November 2-6, 2020. The conference changed to a virtual format due to the COVID-19 pandemic. The papers included in this volume deal with the latest advances in f...

Slides for the talk at ESWC 2019

We present a method for boosting relational classifiers of individual resources in the context of the Web of Data. We show how weak classifiers induced by simple concept learners can be enhanced producing strong classification models from training datasets. Even more so the comprehensibility of the model is to some extent preserved as it can be reg...

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backb...

slides presented at EKAW 2018

Slides presented at EKAW 2018

The paper presents the ultimate version of a concept learning system which can support typical ontology construction / evolution tasks through the induction of class expressions from groups of individual resources labeled by a domain expert. Stating the target task as a search problem, a Foil-like algorithm was devised based on the employment of r...

A prominent class of supervised methods for the representations adopted in the context of the Web of Data are designed to solve concept learning problems. Such methods aim at approximating an intensional definition for a target concept from a set of individuals of a target knowledge base. In this scenario, most of the well-known solutions exploit a...

We present a method for boosting relational classifiers of individual resources in the context of the Web of Data. We show how weak classifiers induced by simple concept learners can be enhanced producing strong classification models from training datasets. Even more so the comprehensibility of the model is to some extent preserved as it can be re...

It is our great pleasure to welcome you to the first edition of WWW 2018 Journal Track. The track is new track within WWW conference series and it is intended as a forum for presentations of significant Web-related research results that have been published recently in well-known and established journals, and have never been presented at any Web-rel...

In the context of the Semantic Web, assigning individuals to their respective classes is a fundamental reasoning service. It has been shown that, when purely deductive reasoning falls short, this problem can be solved as a prediction task to be accomplished through inductive classification models built upon the statistical evidence elicited from on...

The Web of Data, which is one of the dimensions of the Semantic Web (SW), represents a tremendous source of information, which motivates the increasing attention to the formalization and application of machine learning methods for solving tasks such as concept learning, link prediction, inductive instance retrieval in this context. However, the Web...

We focus on the problem of predicting missing assertions in Web ontologies. We start from the assumption that individual resources that are similar in some aspects are more likely to be linked by specific relations: this phenomenon is also referred to as homophily and emerges in a variety of relational domains. In this article, we propose a method...

In the Semantic Web context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by RDF data referring to them. Because of its open, distributed, and collaborative nature, such knowledge can be incomplete, noisy, and sometimes inconsistent. By exploiting the evidence coming fr...

Despite the benefits deriving from explicitly stating concepts as dis-joint to model high-quality ontologies, the number of disjointness axioms in on-tologies adopted as vocabularies for the Web of Data is limited. As a result, while the limited expressiveness fosters their use, these vocabularies fail to specify important constraints. Therefore, d...

Despite the benefits deriving from explicitly modeling concept disjointness to increase the quality of the ontologies, the number of disjointness axioms in vocabularies for the Web of Data is still limited, thus risking to leave important constraints underspecified. Automated methods for discovering these axioms may represent a powerful modeling to...

The two-volume set LNCS 10587 + 10588 constitutes the refereed proceedings of the 16th International Semantic Web Conference, ISWC 2017, held in Vienna, Austria, in October 2017. ISWC 2017 is the premier international forum, for the Semantic Web / Linked Data Community. The total of 55 full and 21 short papers presented in this volume were carefull...

The two-volume set LNCS 10587 + 10588 constitutes the refereed proceedings of the 16th International Semantic Web Conference, ISWC 2017, held in Vienna, Austria, in October 2017. ISWC 2017 is the premier international forum, for the Semantic Web / Linked Data Community. The total of 55 full and 21 short papers presented in this volume were carefull...

We focus on the problem of predicting missing class memberships and property assertions in Web Ontologies. We start from the assumption that related entities influence each other, and they may be either similar or dissimilar with respect to a given set of properties: the former case is referred to as homophily, and the latter as heterophily. We pre...

In the Semantic Web, OWL ontologies play the key role of domain conceptualizations, while the corresponding assertional knowledge is given by the heterogeneous Web resources referring to them. However, being strongly decoupled, ontologies and assertional knowledge can be out of sync. In particular, an ontology may be incomplete, noisy, and sometime...

A tree structure for efficient service matchmaking is created by using a clustering algorithm. Tree nodes represent a superset of all service descriptions in the leaves below. During query processing matchmaking can be restricted to the branches of the tree where tree nodes indicate overlapping between user requests and service descriptions. Good c...

In the context of the Web of Data, plenty of properties may be used for linking resources to other resources but also to literals that specify their attributes. However the scale and inherent nature of the setting is also characterized by a large amount of missing and incorrect information. To tackle these problems, learning models and rules for pr...

In this paper, we tackle the problem of clustering individual resources in the context of the Web of Data, that is characterized by a huge amount of data published in a standard data model with a well-defined semantics based on Web ontologies. In fact, clustering methods offer an effective solution to support a lot of complex related activities, su...

We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a l...

In this work, we tackle the problem of predicting unknown values of numeric features expressed as datatype properties. The task can be cast as a regression problem for which suitable solutions have been devised, for instance, in the related context of RDBs. However, solving such problems singularly does not allow to exploit likely correlations exis...

We focus on the problem of predicting missing links in large Knowledge Graphs (KGs), so to discover new facts. Over the last years, latent factor models for link prediction have been receiving an increasing interest: they achieve state of-the-art accuracy in link prediction tasks, while scaling to very large KGs. However, KGs are often endowed with...

In the Semantic Web context, OWL ontologies represent the con-ceptualization of domains of interest while the corresponding as-sertional knowledge is given by the heterogeneous Web resources referring to them. Being strongly decoupled, ontologies and assertion can be out-of-sync. An ontology can be incomplete, noisy and sometimes inconsistent with...

Knowledge Graphs (KGs) are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of link prediction, i.e. predicting missing links in large knowledge graphs, so to discover new facts about the world. Representation learning models that embed entities and relation types in continuous vector spaces recently we...

Concept learning methods for Web ontologies inspired by Inductive Logic Programming and the derived inductive models for classmembership prediction have been shown to offer viable solutions to concept approximation, query answering and ontology completion problems. They generally produce human-comprehensible logic-based models (e.g. terminological...

n the context of Semantic Web, one of the most important issues related to the class-membership prediction task through inductive models on ontological knowledge bases concerns the imbalance of the training examples distribution, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach has...

In the context of Semantic Web, one of the most important issues related to the class-membership prediction task (through inductive models) on ontological knowledge bases concerns the imbalance of the training examples distribution, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach h...

In the context of Semantic Web, one of the most important issues related to the class-membership prediction task (through inductive models) on ontological knowledge bases concerns the imbalance of the training examples distribution, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach h...

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection...

In the context of Semantic Web, one of the most important issues related to the class-membership prediction task (through inductive models) on ontological knowledge bases concerns the imbalance of the training examples distribution, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach h...

This book constitutes the refereed proceedings of the 12th Extended Semantic Web Conference, ESWC 2014, held in Anissaras, Portoroz, Slovenia, in May/June 2015. The 43 revised full papers presented together with three invited talks were carefully reviewed and selected from 164 submissions. This program was completed by a demonstration and poster se...

We consider the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We first identify which relations tend to link similar individuals by means of a finite-set Gaussian Process regression model, and then efficiently propagate knowledge about individuals across their relations. Our experimen...

The increasing availability of structured machine-processable knowledge in the Web of Data calls for machine learning methods to support standard reasoning based services (such as query-answering and logic inference). Statistical regularities can be efficiently exploited to overcome the limitations of the inherently incomplete knowledge bases distr...

We propose a method that combines terminological decision trees and the Dempster-Shafer Theory, to support tasks like ontology completion. The goal is to build a predictive model that can cope with the epistemological uncertainty due to the Open World Assumption when reasoning with Web ontologies. With such models not only one can predict new (non...

Real-world knowledge often involves various degrees of uncertainty. For such a reason, in the Semantic Web context, difficulties arise when modeling real-world domains using only purely logical formalisms. Alternative approaches almost always assume the availability of probabilistically-enriched knowledge, while this is hardly known in advance. In...

We consider the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We first identify which relations tend to link similar individuals by means of a finite-set Gaussian Process regression model, and then efficiently propagate knowledge about individuals across their relations. Our experimen...

Available domain ontologies are increasing over the time. However there is still a huge amount of data stored and managed with RDBMS. This complementarity could be exploited both for discovering knowledge patterns that are not formalized within the ontology but that are learnable from the data, and for enhancing reasoning on ontologies by relying o...