Antonia M. Chávez-González

Foundational challenges in automated semantic Web data and ontology cleaning

Nowadays, Web-based data management needs tools to ensure secure, trustworthy performance. The Utopian future shows a semantic Web providing dependable framework that can solve many of todays data problems. However, the realistic immediate future raises several challenges, including foundational semantic Web issues, the abstract definition of data, and incomplete, evolving ontologies. In either case, the marriage of data and ontologies is indissoluble and represents the knowledge database (KDB), a basic ingredient of the semantic Web. In this article, we look closely at problems in data analysis, the first phase of data cleaning. Applying automated reasoning systems to semantic Web data cleaning and to cleaning-agent design raises many challenges. We can build trust in semantic Web logic only if its based on certified reasoning.

Visual ontology cleaning: cognitive principles and applicability

In this paper we connect two research areas, the Qualitative Spatial Reasoning and visual reasoning on ontologies. We discuss the logical limitations of the mereotopological approach to the visual ontology cleaning, from the point of view of its formal support. The analysis is based on three different spatial interpretations wich are based in turn on three different spatial interpretations of the concepts of an ontology.

Towards a practical argumentative reasoning with qualitative spatial databases

Classical database management can be flawed if the Knowledge database is built within a complex Knowledge Domain. We must then deal with inconsistencies and, in general, with anomalies of several types. In this paper we study computational and cognitive problems in dealing qualitative spatial databases.

A methodology for the computer-aided cleaning of complex knowledge databases

In environments with complex cognitive structure (such as semantic web or sophisticated spatial databases for geographical information systems), classical methods for detecting anomalies can be inadequate. In this paper the use of an automated theorem prover to detect anomalies in knowledge bases within a complex ontology is proposed. The authors argue that it will need to integrate such systems in some intelligent agents. The loss of real-time execution in some cases. is discussed with examples.

Semantic Geodemography and Urban Interoperability

Nowadays there exists an increasing interest on the use of the information collected by cities coming from different resources as data with dynamic nature like the one provided by sensor networks, as static data associated to the socio-technical system that the city performs. As well as the Semantic Sensor Web allows the standardization of data, it is essential to give an appropriate dealing to geo-demographic data. In this paper, an approach to the semantization of the geo-demographic information is presented, with the aim of achieving interoperability within other systems of the geospatial cyberinfrastructure. Furthermore, fundamental aspects of the creation of ontologies by starting from socio-demographical systems are discussed and the process is illustrated with a case study.

Ontology cleaning by mereotopological reasoning

A mereotopological semantics to manage ontologies is presented. The aim is to provide a formal basis for ontology cleaning. It allows us to arrange, in a consistent manner, the concepts in early steps of the building of an ontology as well as to repair anomalies. The semantics supports cleaning cycle that combines several AI techniques as closed world assumption, default reasoning on taxonomies and knowledge acquisition.

Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies

A logical formalism to support the insertion of uncertain concepts in formal ontologies is presented. It is based on the search of extensions by means of two automated reasoning systems (ARS), and it is driven by what we call cognitive entropy .

A formal foundation for knowledge integration of defficent information in the semantic web

Maintenance of logical robustness in Information Integration represents a major challenge in the envisioned Semantic Web. In this framework, it is previsible unprecise information (with respect to an ontology) is retrieved from some resources. The sound integration of such information is crucial to achieve logical soundness. We present a data-driven approach to classify that knowledge by means of the cognitive entropy of the possible robust ontology extensions and data.

Logic Databases and Inconsistency Handling

Nowadays, data management on the World Wide Web needs to consider very large knowledge databases (KDB). The larger is a KDB, the smaller the possibility of being consistent. Consistency in checking algorithms and systems fails to analyse very large KDBs, and so many have to work every day with inconsistent information. Database revision—transformation of the KDB into another, consistent database—is a solution to this inconsistency, but the task is computationally untractable. Paraconsistent logics are also a useful option to work with inconsistent databases. These logics work on inconsistent KDBs but prohibit nondesired inferences. From a philosophical (logical) point of view, the paraconsistent reasoning is a need that the self human discourse practises. From a computational, logical point of view, we need to design logical formalisms that allow us to extract useful information from an inconsistent database, taking into account diverse aspects of the semantics that are “attached” to deductive databases reasoning (see Table 1). The arrival of the semantic web (SW) will force the database users to work with a KDB that is expressed by logic formulas with higher syntactic complexity than are classic logic databases.

Specifying and Verifying Meta-Security by Means of Semantic Web Methods

In order to achieve a systematic treatment of security protocols, organizations release a number of technical briefings for describing how security incidents have to be managed. These documents can suffer semantic deficiencies, mainly due to ambiguity or different granularity levels of description and analysis. Ontological Engineering (OE) is a powerful instrument that can be applied for both, cleaning methods and knowledge in incident protocols, and specifying (meta)security requirements on protocols for solving security incidents. We also show how the ontology built from security reports can be used as the knowledge core for semantic systems in order to work with resolution incidents in a safe way. The method has been illustrated with a case study.