1,721,226 research outputs found
Reasoning support for mapping revision
No full textFinding correct semantic correspondences between heterogeneous ontologies is one of the most challenging problems in the area of semantic web technologies. As manually constructing such mappings is not feasible in realistic scenarios, a number of automatic matching tools have been developed that propose mappings based on general heuristics. As these heuristics often produce incorrect results, a manual revision is inevitable in order to guarantee the quality of generated mappings. Experiences with benchmarking matching systems revealed that the manual revision of mappings is still a very difficult problem because it has to take the semantics of the ontologies as well as interactions between mappings into account. In this article, we propose methods for supporting human experts in the task of revising automatically created mappings. In particular, we present non-standard reasoning methods for detecting and propagating implications of expert decisions on the correctness of a mapping
Comparing human and automatic thesaurus mapping approaches in the agricultural domain
Full text linkKnowledge organization systems (KOS), like thesauri and other controlled vocabularies, are used to provide subject access to information systems across the web. Due to the heterogeneity of these systems, mapping between vocabularies becomes crucial for retrieving relevant information. However, mapping thesauri is a laborious task, and thus big efforts are being made to automate the mapping process. This paper examines two mapping approaches involving the agricultural thesaurus AGROVOC, one machine-created and one human created. We are addressing the basic question "What are the pros and cons of human and automatic mapping and how can they complement each other?" By pointing out the difficulties in specific cases or groups of cases and grouping the sample into simple and difficult types of mappings, we show the limitations of current automatic methods and come up with some basic recommendations on what approach to use when
Repairing Ontology Mappings
No full textAutomatically discovering semantic relations between on-tologies is an important task with respect to overcoming se-mantic heterogeneity on the semantic web. Existing ontology matching systems, however, often produce erroneous map-pings. In this paper, we address the problem of errors in mappings by proposing a completely automatic debugging method for ontology mappings. The method uses logical rea-soning to discover and repair logical inconsistencies caused by erroneous mappings. We describe the debugging method and report experiments on mappings submitted to the ontol-ogy alignment evaluation challenge that show that the pro-posed method actually improves mappings created by differ-ent matching systems without any human intervention
Supporting Manual Mapping Revision using Logical Reasoning
No full textFinding correct semantic correspondences between ontolo-gies is one of the most challenging problems in the area of semantic web technologies. Experiences with benchmarking matching systems revealed that even the manual revision of automatically generated mappings is a very difficult problem because it has to take the semantics of the ontologies as well as interactions between correspondences into account. In this paper, we propose methods for supporting human experts in the task of revising automatically created mappings. In par-ticular, we present non-standard reasoning methods for de-tecting and propagating implications of expert decisions on the correctness of a mapping. We show that the use of these reasoning methods significantly reduces the effort of mapping revision in terms of the number of decisions that have to be made by the expert
Marrying uncertainty and time in knowledge graphs
No full textThe management of uncertainty is crucial when harvest-ing structured content from unstructured and noisy sources.Knowledge Graphs (KGs) are a prominent example.KGsmaintain both numerical and non-numerical facts, with thesupport of an underlying schema. These facts are usually ac-companied by a confidence score that witnesses how likelyis for them to hold. Despite their popularity, most of exist-ingKGsfocus on static data thus impeding the availabilityof timewise knowledge. What is missing is a comprehensivesolution for the management of uncertain and temporal datainKGs. The goal of this paper is to fill this gap. We rely ontwo main ingredients. The first is a numerical extension ofMarkov Logic Networks (MLNs) that provide the necessaryunderpinning to formalize the syntax and semantics of un-certain temporalKGs. The second is a set of Datalog con-straints with inequalities that extend the underlying schemaof theKGsand help to detect inconsistencies. From a theoret-ical point of view, we discuss the complexity of two impor-tant classes of queries for uncertain temporalKGs:maximuma-posterioriandconditional probability inference. Due to thehardness of these problems and the fact that MLN solversdo not scale well, we also explore the usage of ProbabilisticSoft Logics (PSL) as a practical tool to support our reasoningtasks. We report on an experimental evaluation comparing theMLN and PSL approaches
Spatial Reasoning for the SemanticWeb - Use Cases and Technological Challenges
Full text linkThe goal of semantic web research is to turn the World-Wide Web into a Web of Data that can be processed automatically to a much larger extend than possible with traditional web technology. Important features of the solution currently being developed is the ability to link data from from different sources and to provide formal definitions of the intended meaning of the terminology used in different sources as a basis for deriving implicit information and for conflict detection. Both requires the ability to reason about the definition of terms. With the development of OWL as the standard language for representing terminological knowledge, reasoning in description logics has been determined as the major technique for performing this reasoning cite{OWLreasoning}. More recently, rule languages have gained more importance as well as they have been shown to be more suited for efficient reasoning about terminology and data at the same time.
So far little attention has been paid to the problem of representing and reasoning about space and time on the semantic web. In particular, existing semantic web languages are not well suited for representing these aspects as they require to operate over metric spaces that behave fundamentally different from the abstract interpretation domains description logics are based on. Nevertheless, there is a strong need to integrate reasoning about space and time into existing semantic web technologies especially because more and more data available on the web has a references to space and time. Images taken by digital cameras are a good example of such data as they come with a time stamp and geographic coordinates.
In this paper, we concentrate on spatial aspects and discuss different use case for reasoning about spatial aspects on the (semantic) web and possible technological solutions for these use cases. Based on these discussions we conclude that the actual open problem is not existing technologies for terminological or spatial reasoning, but the lack of an established mechanism for combining the two
Compiling Complex Terminologies for Query Processing
No full textIt is widely accepted that the Semantic Web will be based on machine-readable metadata describing the content of resources. These descriptions are designed to enable intelligent agents to locate and filter relevant information with a higher level of accuracy. The Resource Description Framework (RDF) has been developed as universal language for encoding content-related metadata and recently a number of query languages have been proposed to extract information from metadata models. Current applications of RDF and RDF query languages only use very simple metadata like simple concept hierarchies (The Open Directory) or pre-defined attribute value pairs (Dublin Core). In this paper we address the problem of encoding and querying complex metada using RDF models and queries. In our approach we consider ontologies with complex concept definitions in the spirit of DAML+OIL and propose a pre-processing method that enables us to access these models using RDF query languages without losing information
Ontology Alignment: An annotated Bibliography
Full text linkOntology mapping, alignment, and translation has been an active research component of the general research on semantic integration and interoperability. In our talk, we gave our own classification of different topics in this research. We talked about types of heterogeneity between ontologies, various mapping representations, classified methods for discovering methods both between ontology concepts and data, and talked about various tasks where mappings are used. In this extended abstract of our talk, we provide an annotated bibliography for this area of research, giving readers brief pointers on representative papers in each of the topics mentioned above. We did not attempt to compile a comprehensive bibliography and hence the list in this abstract is necessarily incomplete. Rather, we tried to sketch a map of the field, with some specific reference to help interested readers in their exploration of the work to-date
Toward Building a Content-Based Video Recommendation System Based on Low-Level Features
Full text linkOne of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, every-day, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations. In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features
Cartographic and semantic aspects on web services
Full text linkSeveral countries are currently working on setting up geoportals as part of their national spatial data infrastructure (SDI) (and this is also a requirement of the Inspire initiative). A key ability of these geoportals is that the user should be able to view (and download) data from several sources from one access point. This will certainly make the access to geospatial data easier. However, there are also cartographic and semantic challenges that have to be solved. In this discussion group we discussed some topics concerning both download services and view services and some possible solutions
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