Catálogo de publicaciones - libros

Building domain ontology is time consuming and tedious since it is usually done by domain experts and knowledge engineers manually. This paper proposes a two-stage clustering approach for semi-automatically building ontologies from the Chinese-document corpus ba-sed on SOM neural network and agglomerative hierarchical clustering and automatically checking the ontology consistency. Chinese lexical analysis and XML Path Language(XPath) are used in the process of extracting resources from Web documents. In our experiment, this two-stage clustering approach is used for building an automobile ontology. Experimental results and the comparison with the more conventional ontology-generation method are presented and discussed, indicating the high performance of our approach. A Racer-based consistency-checking method of reasoning is presented in this paper. An ontology evolution method and performance evaluation are also given.

A unifying logic is built on top of ontologies and rules for the revised Semantic Web Architecture. This paper proposes , which integrates a description logic (DL) that makes a nique names assumption with general rules that have the form of Datalog ℙrograms permitting default negation in the body. An knowledge base (KB) consists of a TBox of subsumptions, an ABox of assertions, and a novel PBox ℙ of general rules that share predicates with DL concepts and DL roles. To model open answer set semantics, extended Herbrand structures are used for interpreting DL concepts and DL roles, while open answer sets hold for general rules. To retain decidability, a well-known weak safeness condition is employed. We develop DL tableaux-based algorithms for decision procedures of the KB satisfiability and the query entailment problems.

Modern semantic technology is one of the necessary supports for the infrastructure of next generation information systems. In particular, large international organizations, which usually have branches around the globe, need to manage web-based, complex, dynamically changing and geographically distributed information. Formalisms for modular ontologies offer the necessary mechanism that is needed to handle ontology-based distributed information systems in the aforementioned scenario. In this paper, we investigate state-of-the-art technologies in the area of modular ontologies and corresponding logical formalisms. We compare different formalisms for modular ontologies in their ability to support networked, dynamic and distributed ontologies, as well as the reasoning capability over these ontologies. The comparison results show the strength and limitation of existing formalisms against the needs of modular ontologies in the given setting, and possible future extensions to overcome those limitations.

Description Logics (DLs) have been widely used in the last years as formal language for specifying ontologies over the web. Due to the dynamic nature of this setting, it may frequently happen that data retrieved from the web contradict the intensional knowledge provided by the ontology through which they are collected, which therefore may result inconsistent. In this paper, we analyze the problem of consistent query answering over DL ontologies, i.e., the problem of providing meaningful answers to queries posed over inconsistent ontologies. We provide inconsistency tolerant semantics for DLs, and study the computational complexity of consistent query answering over ontologies specified in , a family of DLs specifically tailored to deal with large amounts of data. We show that the above problem is coNP-complete w.r.t. data complexity, i.e., the complexity measured w.r.t. the size of the data only. Towards identification of tractable cases of consistent query answering over ontologies, we then study the problem of consistent instance checking, i.e., the instance checking problem considered under our inconsistency-tolerant semantics. We provide an algorithm for it which runs in time polynomial in the size of the data, thus showing that the problem is in w.r.t. data complexity.

In this paper we present a context-based architecture and implementation for supporting the construction and management of contextualized RDF knowledge bases. The goal of this work is to take explicitly into account any possible contextual dependency of a collection of RDF models, without losing sight of performance and scalability issues. We are illustrating motivations, as well as theoretical background, implementation details and test-results of our latest works.

This paper describes an hybrid method combining symbolic and numerical techniques for annotating brain Magnetic Resonance images. Existing automatic labelling methods are mostly statistical in nature and do not work very well in certain situations such as the presence of lesions. The goal is to assist them by a knowledge-based method. The system uses statistical method for generating a sufficient set of initial facts for fruitful reasoning. Then, the reasoning is supported by an OWL DL ontology enriched by SWRL rules. The experiments described were achieved using the KAON2 reasoner for inferring the annotations.

Rule based systems and agents are important applications of the Semantic Web constructs such as RDF, OWL, and SWRL. While there are plenty of utilities that support ontology generation and utilization, rule acquisition is still a bottleneck as an obstacle to wide propagation of rule based systems. To automatically acquire rules from unstructured texts, we develop a rule acquisition framework that uses a rule ontology. The ontology can be acquired from the rule base of a similar site, and then is used for rule acquisition in the other sites of the same domain. The procedure of ontology-based rule acquisition consists of rule component identification and rule composition. The former uses stemming and semantic similarity to extract variables and values from the Web page and the latter uses the best-first search method in composing the variables and values into rules.

In this paper, we present the rewriting-based, Web verification service WebVerdi-M, which is able to recognize forbidden/incorrect patterns and incomplete/missing Web pages. WebVerdi-M relies on a powerful Web verification engine that is written in Maude, which automatically derives the error symptoms. Thanks to the AC pattern matching supported by Maude and its metalevel facilities, WebVerdi-M enjoys much better performance and usability than a previous implementation of the verification framework. By using the XML Benchmarking tool xmlgen, we develop some scalable experiments which demonstrate the usefulness of our approach.

Trust is a vital feature for the Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension.

We investigate the use of domain ontologies that also include actions and events of that domain. Such ontologies do not only cover the static aspects of an ontology, but also activities and behavior in the given domain. We analyze what information has to be contained in such an ontology and show that large parts of the behavior can be expressed preferably by rules. We show how the tasks can be integrated and handled by a service infrastructure in the Semantic Web.