Álvaro Cortés-Calabuig

Towards a logical reconstruction of a theory for locally closed databases

The Closed World Assumption (CWA) on databases expresses the assumption that an atom not in the database is false. This assumption is applicable only in cases where the database has complete knowledge about the domain of discourse. In this article, we investigate locally closed databases, that is: databases that are sound but partially incomplete about their domain. Such databases consist of a standard database instance, augmented with a collection of Local Closed World Assumptions (LCWAs). A LCWA is a “local” form of the CWA, expressing that a database relation is complete in a certain area, called a window of expertise. In this work, we study locally closed databases both from a knowledge representation and from a computational perspective. At the representation level, the approach taken in this article distinguishes between the data that is conveyed by a database and the metaknowledge about the area in which the data is complete. We study the semantics of the LCWAs and relate it to several knowledge representation formalisms. At the reasoning level, we study the complexity of, and algorithms for two basic reasoning tasks: computing certain and possible answers to queries and determining whether a database has complete knowledge on a query. As the complexity of these tasks is unacceptably high, we develop efficient approximate methods for query answering. We also prove that for useful classes of queries and locally closed databases, these methods are optimal, and thus they solve the original query in a tractable way. As a result, we obtain classes of queries and locally closed databases for which query answering is tractable.

Representation of partial knowledge and query answering in locally complete databases

The Local Closed-World Assumption (LCWA) is a generalization of Reiters Closed-World Assumption (CWA) for relational databases that may be incomplete. Two basic questions that are related to this assumption are: (1) how to represent the fact that only part of the information is known to be complete, and (2) how to properly reason with this information, that is: how to determine whether an answer to a database query is complete even though the database information is incomplete. In this paper we concentrate on the second issue based on a treatment of the first issue developed in earlier work of the authors. For this we consider a fixpoint semantics for declarative theories that represent locally complete databases. This semantics is based on 3-valued interpretations that allow to distinguish between the certain and possible consequences of the databases theory.

On the local closed-world assumption of data-sources

The Closed-World Assumption (CWA) on a database expresses that an atom not in the database is false. The CWA is only applicable in domains where the database has complete knowledge. In many cases, for example in the context of distributed databases, a data source has only complete knowledge about part of the domain of discourse. In this paper, we introduce an expressive and intuitively appealing method of representing a local closed-world assumption (LCWA) of autonomous data-sources. This approach distinguishes between the data that is conveyed by a data-source and the meta-knowledge about the area in which these data is complete. The data is stored in a relational database that can be queried in the standard way, whereas the meta-knowledge about its completeness is expressed by a first order theory that can be processed by an independent reasoning system (for example a mediator). We consider different ways of representing our approach, relate it to other methods of representing local closed-word assumptions of data-sources, and show some useful properties of our framework which facilitate its application in real-life systems.

Data Integration Using ID -Logic

ID-Logic is a knowledge representation language that extends first-order logic with non-monotone inductive definitions. This paper introduces an ID-Logic based framework for database schema integration. It allows us to to uniformly represent and reason with independent source databases that contain information about a common domain, but may have different schemas. The ID-Logic theories that are obtained are called mediator-based systems. We show that these theories properly capture the common methods for data integration (i.e., global-as view and local-as-view with either exact or partial definitions), and apply on them a robust abductive inference technique for query answering.

MicroRNAs promote skeletal muscle differentiation of mesodermal iPSC-derived progenitors

Muscular dystrophies (MDs) are often characterized by impairment of both skeletal and cardiac muscle. Regenerative strategies for both compartments therefore constitute a therapeutic avenue. Mesodermal iPSC-derived progenitors (MiPs) can regenerate both striated muscle types simultaneously in mice. Importantly, MiP myogenic propensity is influenced by somatic lineage retention. However, it is still unknown whether human MiPs have in vivo potential. Furthermore, methods to enhance the intrinsic myogenic properties of MiPs are likely needed, given the scope and need to correct large amounts of muscle in the MDs. Here, we document that human MiPs can successfully engraft into the skeletal muscle and hearts of dystrophic mice. Utilizing non-invasive live imaging and selectively induced apoptosis, we report evidence of striated muscle regeneration in vivo in mice by human MiPs. Finally, combining RNA-seq and miRNA-seq data, we define miRNA cocktails that promote the myogenic potential of human MiPs.Mesodermal iPSC-derived progenitors (MiPs) can regenerate both skeletal and cardiac muscle. Here, the authors show that a microRNA cocktail stimulates skeletal muscle differentiation and that human MiPs can engraft into striated muscle in mice.

An ID-logic formalization of the composition of autonomous databases

We introduce a declarative approach for a coherent composition of autonomous databases. For this we use ID-logic, a formalism that extends classical logic with inductive definitions. We consider ID-logic theories that express, at the same time, the two basic challenges in database composition problems: relating different schemas of the local databases to one global schema (schema integration) and amalgamating the distributed and possibly contradictory data to one consistent database (data integration). We show that our framework supports different methods for schema integration (as well as their combinations) and that it provides a straightforward way of dealing with inconsistent data. Moreover, this framework facilitates the implementation of database repair and consistent query answering by means of a variety of reasoning systems.