Catálogo de publicaciones - libros

The main purpose of an enterprise ontology is to promote the common understanding between people across enterprises, as well as to serve as a communication medium between people and applications, and between different applications. This paper outlines a top-level ontology, called the context-based enterprise ontology, which aims to advance the understanding of the nature, purposes and meanings of things in enterprises with providing basic concepts for conceiving, structuring and representing things within contexts and/or as contexts. The ontology is based on the contextual approach according to which a context involves seven domains: purpose, actor, action, object, facility, location, and time. The concepts in the ontology are defined in English and presented in meta models in a UML-based ontology engineering language.

This paper presents , an event extraction system, developed at the Joint Research Center of the European Commission utilized for populating violent incident knowledge bases. It automatically extracts security-related facts from on-line news articles. In particular, the paper focuses on a novel bootstrapping algorithm for weakly supervised acquisition of extraction patterns from clustered news, cluster-level information fusion and pattern specification language. Finally, a preliminary evaluation of on real-world data is given which revealed acceptable precision and a strong application potential.

In this paper we introduce a prototype job portal which uses semantically annotated job offers and applicants. In our opinion, using Semantic Web technologies substantially increase market transparency, lower transaction costs and speed up the procurement process. However adding semantics is not a panacea for everything. We identify some outstanding problems in job search using the system and outline how the technique of query approximation can be the basis for a solution. Through an Industry-Research co-operation we are extending the prototype with these semantic techniques to demonstrate a more accurate job search.

Profiles are the basis for individual communication, because they provide information about website users. Ontologies represent a possibility for modeling user profiles. The ontology development within the paper is based on a concept which shows firstly criteria of segmentation and secondly product programs of a retailer. A meta-ontology is built to enforce a mapping between the ontologies. Due to ontology-based recommendations it is obvious that they imply an additive character regarding conventional recommender systems. Therefore, the possibility arises to increase the turnover and to achieve customer satisfaction. But, the usage of ontology-based profiles is currently disputable relating to economic efficiency.

This article mainly proposes a bottom-up method to index XML document. Firstly we discuss the underlying properties of the method, architecture, creation algorithm and query algorithm, then conduct a set of experiments referring to the Timber and XIndice system. The demo system convinces that, this method can maintain excellent indexing and querying performance under given queries with normal PC on the DBLP XML test set of which the size is 315M, so it can be regarded as a prospective application with good performance. XML, indexing, Inter-relevant Successive Trees.

The problem of spatial configuration information retrieval is a Constraint Satisfaction Problem (CSP), which can be solved using traditional CSP algorithms. But the spatial data can be reorganized using index techniques like R-tree and the spatial data are approximated by their Minimum Bounding Rectangles (MBRs), so the spatial configuration information retrieval is actually based on the MBRs and some special techniques can be studied. This paper studies the mapping relationships among the spatial relations for real spatial objects, the corresponding spatial relations for their MBRs and the corresponding spatial relations between the intermediate nodes and the MBRs in R-tree. Three algorithms are designed and studied, and their performances are compared.

String distance metrics have been widely used in various applications concerning processing of textual data. This paper reports on the exploration of their usability for tackling the reference matching task and for the automatic correction of misspelled search engine queries, in the context of highly inflective languages, in particular focusing on Polish. The results of numerous experiments in different scenarios are presented and they revealed some preferred metrics. Surprisingly good results were observed for correcting misspelled search engine queries. Nevertheless, a more in-depth analysis is necessary to achieve improvements. The work reported here constitutes a good point of departure for further research on this topic.

Business intelligence requires the collecting and merging of information from many different sources, both structured and unstructured, in order to analyse for example financial risk, operational risk factors, follow trends and perform credit risk management. While traditional data mining tools make use of numerical data and cannot easily be applied to knowledge extracted from free text, traditional information extraction is either not adapted for the financial domain, or does not address the issue of information integration: the merging of information from different kinds of sources. We describe here the development of a system for content mining using , which enables the extraction of relevant information to be fed into models for analysis of and other business intelligence applications such as , by means of the XBRL standard. The results so far are of extremely high quality, due to the implementation of primarily high-precision rules.

A word-to-word similarity function automatically extracted from a corpus of texts can be a very helpful tool in automatic extraction of lexical semantic relations. There are many approaches for English, but only a few for inflective languages with almost free word order. In the paper a method for the construction of a similarity function for Polish nouns is proposed. The method uses only simple tools for language processing (e.g. it does need the application of a parser). The core is the construction of a matrix of co-occurrences of nouns and adjectives on the basis of application of morpho-syntactic constraints testing agreement between an adjective and a noun. Several methods of transformation of the matrix and calculation of the similarity function are presented. The achieved accuracy of 81.15% in WordNet-based Synonymy Test (for 4 611 Polish nouns, using the current version of Polish WordNet) seems to be comparable with the best results reported for English (e.g. 75.8% [5]).

A great amount of information is still being stored in loosely structured documents in several widely used formats. Due to the lack of data description in these documents, their integration to the existing information systems requires sophisticated pre-processing techniques to be developed. To the document reader, the content structure is mostly presented by visual means. Therefore, we propose a technique for the discovery of the logical document structure based on the analysis of various visual properties of the document such as the page layout or text properties. This technique is currently being tested and some promising preliminary results are available.