2,291 research outputs found
Context and Keyword Extraction in Plain Text Using a Graph Representation
Document indexation is an essential task achieved by archivists or automatic
indexing tools. To retrieve relevant documents to a query, keywords describing
this document have to be carefully chosen. Archivists have to find out the
right topic of a document before starting to extract the keywords. For an
archivist indexing specialized documents, experience plays an important role.
But indexing documents on different topics is much harder. This article
proposes an innovative method for an indexing support system. This system takes
as input an ontology and a plain text document and provides as output
contextualized keywords of the document. The method has been evaluated by
exploiting Wikipedia's category links as a termino-ontological resources
Knowledge Propagation in Contextualized Knowledge Repositories: an Experimental Evaluation
As the interest in the representation of context dependent knowledge in the
Semantic Web has been recognized, a number of logic based solutions have been
proposed in this regard. In our recent works, in response to this need, we
presented the description logic-based Contextualized Knowledge Repository (CKR)
framework. CKR is not only a theoretical framework, but it has been effectively
implemented over state-of-the-art tools for the management of Semantic Web
data: inference inside and across contexts has been realized in the form of
forward SPARQL-based rules over different RDF named graphs. In this paper we
present the first evaluation results for such CKR implementation. In
particular, in first experiment we study its scalability with respect to
different reasoning regimes. In a second experiment we analyze the effects of
knowledge propagation on the computation of inferences.Comment: ARCOE-Logic 2014 Workshop Notes, pp. 13-2
Personalized content retrieval in context using ontological knowledge
Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context
Reasoning on Multi-Relational Contextual Hierarchies via Answer Set Programming with Algebraic Measures (Extended Abstract)
This extended abstract summarizes our previous work on a defeasible extension of Description Logic (DL) for contextual reasoning. Here, we considered on the one hand the addition of multiple dimensions of defeasibility, allowing us to express for example that a rule has to be satisfied no matter the geographical context but that the rule can change in the next years. On the other hand, we showed that Answer Set Programming (ASP) especially when enhanced with algebraic measures provide a powerful tool to implement our framework and open up perspectives for the future
Guidelines for the Specification and Design of Large-Scale Semantic Applications
This paper presents a set of guidelines to help software engineers with the specification and design of large-scale semantic applications by defining new processes for Requirements Engineering and Design for semantic applications. To facilitate its use to software engineers not experts in semantic technologies, several techniques are provided, namely, a characterization of large-scale semantic applications, common use cases that appear when developing this type of application, and a set of architectural patterns that can be used for modelling the architecture of semantic applications. The paper also presents an example of how these guidelines can be used and an evaluation of our contributions using the W3C Semantic Web use cases
Integrating data warehouses with web data : a survey
This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML
technologies that are currently being used to integrate, store, query, and retrieve Web data and their application to DWs. The paper
reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces
the problem of dealing with semistructured data in a DW. It studies Web data repositories, the design of multidimensional databases for
XML data sources, and the XML extensions of OnLine Analytical Processing techniques. The paper addresses the application of
information retrieval technology in a DW to exploit text-rich document collections. The authors hope that the paper will help to discover
the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as to identify open research
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