Nicola Fanizzi

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Verified

Nicola verified their affiliation via an institutional email.

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• PhD
• Professor (Associate) at University of Bari Aldo Moro

Since Knowledge Graphs are often incomplete, link prediction methods are adopted for predicting missing facts. Scalable embedding based solutions are mostly adopted for this purpose, however, they lack comprehensibility, which may be crucial in several domains. Explanation methods tackle this issue by identifying supporting knowledge explaining the...

Legal decision-making process requires the availability of comprehensive and detailed legislative background knowledge and up-to-date information on legal cases and related sentences/decisions. Legal Knowledge Graphs (KGs) would be a valuable tool to facilitate access to legal information, to be queried and exploited for the purpose, and to enable...

Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs. Embedding models are trained relying on both positive and negative samples of triples. However, in the absence of negative assertions, these must be usually artificially generated using various negative sampling...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

The problem of predicting the membership w.r.t. a target concept for individuals of Semantic Web knowledge bases can be cast as a concept learning problem, whose goal is to induce intensional definitions describing the available examples. However, the models obtained through the methods borrowed from Inductive Logic Programming e.g. Terminological...

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...

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...

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...

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...

In the Semantic Web context, procedures for deciding the class-membership of an individual to a target concept in a knowledge base are generally based on automated reasoning. However, frequent cases of incompleteness/inconsistency due to distributed, heterogeneous nature and the Web-scale dimension of the knowledge bases. It has been shown that res...

Considering the increasing availability of structured machine processable knowledge in the context of the Semantic Web, only relying on purely deductive inference may be limiting. This work proposes a new method for similarity-based class-membership prediction in Description Logic knowledge bases. The underlying idea is based on the concept of prop...

Concept learning methods for Web ontologies inspired by Inductive Logic Programming and the derived inductive models for class-membership 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...

The increasing availability of structured machine-processable knowledge in the WEB OF DATA calls for machine learning methods to support standard pattern matching and reasoning based services (such as query-answering and inference). Statistical regularities can be efficiently exploited to overcome the limitations of the inherently incomplete knowle...

One of the bottlenecks of the ontology construction process is the amount of work required with various figures playing a role in it: domain experts contribute their knowledge that has to be formalized by knowledge engineers so that it can be mechanized. As the gap between these roles likely makes the process slow and burdensome, this problem may b...

With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research.

We investigate on modeling uncertain concepts via rough description logics, which are an extension of traditional description logics by a simple mechanism to handle approximate concept definitions through lower and upper approximations of concepts based on a rough-set semantics. This allows to apply rough description logics for modeling uncertain k...

The increasing availability of structured machine-processable knowledge in the context of the Semantic Web, allows for inductive methods to back and complement purely deductive reasoning in tasks where the latter may fall short. This work proposes a new method for similarity-based class-membership prediction in this context. The underlying idea is...

In the context of semantic knowledge bases, we tackle the problem of ranking resources w.r.t. some criterion. The proposed solution is a method for learning functions that can approximately predict the correct ranking. Differently from other related methods proposed, that assume the ranking criteria to be explicitly expressed (e.g. as a query or a...

Extensive research activities are recently directed towards the Semantic Web as a future form of the Web. Consequently, Web search as the key technology of the Web is evolving towards some novel form of Semantic Web search. A very promising recent such approach is based on combining standard Web pages and search queries with ontological background...

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Fede...

Following previous works on inductive methods for ABox reasoning, we propose an alternative method for predicting assertions based on the available evidence and the analogical criterion. Once neighbors of a test individual are selected through some distance measures, a combination rule descending from the Dempster-Shafer theory can join together th...

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm i...

In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the definition of new features to be derived from the data model. These problems can be cast as regression...

In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the definition of new features to be derived from the data model. These problems can be cast as regression...

The paper tackles the problem of mining linked open data. The inherent lack of knowledge caused by the open-world assumption made on the semantic of the data model determines an abundance of data of uncertain classification. We present a semi-supervised machine learning approach. Specifically a self-training strategy is adopted which iteratively us...

Semantic Web search is currently one of the hottest research topics in both Web search and the Semantic Web. In previous work, we have presented a novel approach to Semantic Web search, which allows for evaluating ontology-based complex queries that involve reasoning over the Web relative to an underlying background ontology. We have developed the...

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can b...

Knowledge available through Semantic Web standards can be missing, generally because of the adoption of the Open World Assumption. We present a Statistical Relational Learning system for learning terminological naïve Bayesian classifiers, which estimate the probability that an individual belongs to a target concept given its membership to a set of...

The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective statistical learning algorithms. Kernel functions encode a notion of similarity among elements of some i...

In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the de¯nition of new features to be derived from the data model. These problems can be cast as regression p...

Knowledge available through Semantic Web representation formalisms can be missing, i.e. it is not always possible to infer the truth value of an assertion (due to the Open World Assumption). We propose a method for incrementally inducing terminological (tree-augmented) naïve Bayesian classifiers, which aim at estimating the probability that an indi...

Considering the increasing availability of structured machine processable knowledge in the context of the Semantic Web, only relying on purely deductive inference may be limiting. This work proposes a new method for similarity-based class-membership prediction in Description Logic knowledge bases. The underlying idea is based on the concept of prop...

Many applicative domains require complex multi-relational representations. We propose a family of kernels for relational representations to produce statistical classifiers that can be effectively employed in a variety of such tasks. The kernel functions are defined over the set of objects in a knowledge base parameterized on a notion of context, re...

In the line of our investigation of inductive methods for Semantic Web reasoning, we propose an alternative way for approximate ABox reasoning based on the evidence and the analogical principle of the nearest-neighbors. Once neighbors of a test individual are selected through some distance measures, a combination rule descending from the Dempster-S...

This paper presents an approach to ontology construction pursued through the induction of concept descriptions expressed in Description Logics. The author surveys the theoretical foundations of the standard representations for formal ontologies in the Semantic Web. After stating the learning problem in this peculiar context, a FOIL-like algorithm i...

An automated ontology matching methodology is presented, supported by various machine learning techniques, as implemented in the system MoTo. The methodology is two-tiered. On the first stage it uses a meta-learner to elicit certain mappings from those predicted by single matchers induced by a specific base-learner. Then, uncertain mappings are rec...

Knowledge available through Semantic Web standards can easily be missing, generally because of the adoption of the Open World Assumption (i.e. the truth value of an assertion is not necessarily known). However, the rich relational structure that characterizes ontologies can be exploited for handling such missing knowledge in an explicit way. We pre...

Efficient resource retrieval is a crucial issue, particularly when semantic resource descriptions are considered which enable the exploitation of reasoning services during the retrieval process. In this context, resources are commonly retrieved by checking if each available resource description satisfies the given query. This approach becomes ineff...

Using a variant of Lehmann's Default Logics and Probabilistic Description Logics we recently presented a framework that invalidates those unwanted inferences that cause concept unsatisfiability without the need to remove explicitly stated axioms. The solutions of this methods were shown to outperform classical ontology repair w.r.t. the number of i...

In the beginning of the Semantic Web, ontologies were usually constructed once by a single knowledge engineer and then used as a static conceptualization of some domain. Nowadays, knowledge bases are increasingly dynamically evolving and incorporate new knowledge from different heterogeneous domains -- some of which is even contributed by casual us...

Efficient resource retrieval is a crucial issue, particularly in the context of Semantic Web, since forms of reasoning are used for answering requests. Resources are retrieved by performing a match test between each resource description and the query. This approach becomes inefficient with the increase of available resources. We propose a method fo...

We describe a method for learning functions that can predict the ranking of resources in knowledge bases expressed in Description Logics. The method relies on a kernelized version of the Perceptron Ranking algorithm which is suitable for batch but also online problems settings. The usage of specific kernel functions that encode the similarity betwe...

A new framework for the induction of logical decision trees is presented. Differently from the original setting, tests at the tree nodes are expressed with Description Logic concepts. This has a number of advantages: expressive terminological languages are endowed with full negation, thus allowing for a more natural division of the individuals at e...

With the introduction of the Semantic Web as a future substitute of the Web, the key task for the Web, namely, Web Search, is evolving towards some novel form of Semantic Web search. Avery promising recent approach to Semantic Web search is based on combining standard Web pages and search queries with ontological background knowledge, and using sta...

The methods proposed for aggregating results of structured queries are typically grounded on syntactic approaches. This may be inconvenient for an exploratory data retrieval, with often overwhelming number of the returned answers, requiring their further analysis and categorization. For example, if the values instantiating a grouping criterion are...