The talk describes some of the research experiences I have carried out in the last 5 years in the area of mixing logical and statistical reasoning. In particular I will describe the usage of a misture of statistical-logical techniques we have applied for the resolution of problems in different research areas such as co-reference resolution for natural language understanding, semantic image interpretation, and data mining in presence of logicWl constraints. The talk will end with the proposal of a theoretical framework in which it is possible to formally define a task called learning-reasoning, which at the same time learns from observations by using logical reasoning, and uses observations to guide reasoning. We will explain the idea behind this formalism with a toy example.

Affiliations: Fondazione Bruno Kessler

Bio: He is a researcher at FBK and head of the Data and Knowledge Management (DKM) research unit.

OWL ontologies have gained great popularity as a context modelling tool for intelligent environments due to their expressivity. However, they present some disadvantages when it is necessary to deal with uncertainty, which is common in our daily life and aects the decisions that we take. To overcome this drawback, we have developed a novel framework to compute fact probabilities from the axioms in an OWL ontology. This proposal comprises the denition and description of our probabilistic ontology. Our probabilistic ontology extends OWL 2 DL with a new layer to model uncertainty. With this work we aim to overcome OWL limitations to reason with uncertainty, developing a novel framework that can be used in intelligent environments.

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 knowledge bases distributed across the Web. This paper focuses on the problem of predicting missing class-memberships and property values of individual resources in Web ontologies. We propose a transductive inference method for inferring missing properties about individuals: given a class-membership/property value prediction problem, we address the task of identifying relations encoding similarities between individuals, and efficiently propagating knowledge across their relations.

Cultural heritage institutions are employing crowdsourcing techniques to enrich their collection. However, assessing the quality of crowdsourced annotations is a challenge for these institutions and manually evaluating all annotations is not feasible. We employ Support Vector Machines and feature set selectors to understand which annotator and annotation properties are relevant to the annotation quality. In addition we propose a trust model to build an annotator reputation using subjective logic and assess the relevance of both annotator and annotation properties on the reputation. We applied our models to the Steve.museum dataset and found that a subset of annotation properties can identify useful annotations with a precision of 90%. However, our studied annotator properties were less predictive.

We examine the use of semantic web resources in robot navigation; more specifically, in qualitative navigation where uncertain reasoning plays a signicant role. We propose a framework for robot navigation that connects existing semantic web resources based on probabilistic description logics, with probabilistic relational learning and planning. We show the benets of this framework in a real robot, presenting a case study on how semantic web resources can be used to face sensor and mapping uncertainty in a practical problem.

The capacity of distributional semantic models (DSMs) to discover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.

The ability to calculate hash values is fundamental for using cryptographic tools, such as digital signatures, with RDF data. Without hashing it is dicult to implement tamper-resistant attribution or provenance tracking, both important for establishing trust with open data. We propose a novel hash function for RDF graphs, which does not require altering the contents of the graph, does not need to record additional information, and does not depend on a concrete RDF syntax. We are also presenting a solution to the deterministic blank node labeling problem.

Research is increasingly becoming a data-intensive science, however proper data integration and sharing is more than storing the datasets in a public repository, it requires the data to be organized, characterized and updated continuously. This article assumes that by rewarding and recognizing metadata sharing and integration on the semantic web using ontologies, we are promoting and intensifying the trust and quality in data sharing and integration. So, the proposed approach aims at measuring the knowledge rating of a dataset according to the specificity and distinctiveness of its mappings to ontology concepts. The knowledge ratings will then be used as the basis of a novel reward and recognition mechanism that will rely on a virtual currency, dubbed KnowledgeCoin (KC). Its implementation could explore some of the solutions provided by current cryptocurrencies, but KC will not be a cryptocurrency since it will not rely on a cryptographic proof but on a central authority whose trust depends on the knowledge rating measures proposed by this article. The idea is that every time a scientific article is published, KCs are distributed according to the knowledge rating of the datasets supporting the article.

This paper introduces an ontology for representing trust that extends existing ones by integrating them with recent trust theories. Then, we propose an extension of such an ontology, tailored for representing trust assessments of data, and we outline its specificity and its relevance.

TBD.