Knowledge-Based Matching of nn-ary Tuples

An increasing number of data and knowledge sources are accessible by human and software agents in the expanding Semantic Web. Sources may differ in granularity or completeness, and thus be complementary. Consequently, they should be reconciled in order to unlock the full potential of their conjoint knowledge. In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar. This task is challenging since knowledge units can be heterogeneously represented in sources (e.g., in terms of vocabularies). In this paper, we focus on matching n-ary tuples in a knowledge base with a rule-based methodology. To alleviate heterogeneity issues, we rely on domain knowledge expressed by ontologies. We tested our method on the biomedical domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples from four different real-world sources. Results highlight noteworthy agreements and particularities within and across sources

Integrating e-commerce standards and initiatives in a multi-layered ontology

The proliferation of different standards and joint initiatives for the classification of products and services (UNSPSC, e-cl@ss, RosettaNet, NAICS, SCTG, etc.) reveals that B2B markets have not reached a consensus on the coding systems, on the level of detail of their descriptions, on their granularity, etc. This paper shows how these standards and initiatives, which are built to cover different needs and functionalities, can be integrated in an ontology using a common multi-layered knowledge architecture. This multi-layered ontology will provide a shared understanding of the domain for applications of e-commerce, allowing the information sharing between heterogeneous systems. We will present a method for designing ontologies from these information sources by automatically transforming, integrating and enriching the existing vocabularies with the WebODE platform. As an illustration, we show an example on the computer domain, presenting the relationships between UNSPSC, e-cl@ss, RosettaNet and an electronic catalogue from an e-commerce platform

Time indeterminacy and spatio-temporal building transformations: an approach for architectural heritage understanding

Nowadays most digital reconstructions in architecture and archeology describe buildings heritage as awhole of static and unchangeable entities. However, historical sites can have a rich and complex history, sometimes full of evolutions, sometimes only partially known by means of documentary sources. Various aspects condition the analysis and the interpretation of cultural heritage. First of all, buildings are not inexorably constant in time: creation, destruction, union, division, annexation, partial demolition and change of function are the transformations that buildings can undergo over time. Moreover, other factors sometimes contradictory can condition the knowledge about an historical site, such as historical sources and uncertainty. On one hand, historical documentation concerning past states can be heterogeneous, dubious, incomplete and even contradictory. On the other hand, uncertainty is prevalent in cultural heritage in various forms: sometimes it is impossible to define the dating period, sometimes the building original shape or yet its spatial position. This paper proposes amodeling approach of the geometrical representation of buildings, taking into account the kind of transformations and the notion of temporal indetermination

From Data Fusion to Knowledge Fusion

The task of {\em data fusion} is to identify the true values of data items (eg, the true date of birth for {\em Tom Cruise}) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent survey\cite{LDL+12} has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: {\em knowledge fusion}. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.Comment: VLDB'201

An evaluation of the performance of three semantic background knowledge sources in comparative anatomy

In this paper we evaluate the performance and usefulness of three semantic background knowledge sources for predicting synonymous anatomical terms across species boundaries. The reference sources under evaluation are UMLS, FMA-OBO and WordNet, which are applied to the anatomical ontologies of mouse and zebrafish. Our results show that the use of specialized knowledge sources leads to highly accurate predictions, verified through complete manual curation, which can be further improved by combining multiple of said sources. We argue that these three references complement each other in terms of granularity and specificity. From our results we conclude that these references can be used to create reliable ontology mappings with minimal human supervision