Search (183 results, page 1 of 10)

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Theme

Klassifikationssysteme im Online-Retrieval

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Theme

Klassifikationssysteme im Online-Retrieval

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Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

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Abstract

The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

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Footnote

Rez. in: iwp 62(2011) H.4, S.205-206 (C. Carstens): "Welche Arten der Wissensrepräsentation existieren im Web, wie ausgeprägt sind semantische Strukturen in diesem Kontext, und wie können soziale Aktivitäten im Sinne des Web 2.0 zur Strukturierung von Wissen im Web beitragen? Diesen Fragen widmet sich Wellers Buch mit dem Titel Knowledge Representation in the Social Semantic Web. Der Begriff Social Semantic Web spielt einerseits auf die semantische Strukturierung von Daten im Sinne des Semantic Web an und deutet andererseits auf die zunehmend kollaborative Inhaltserstellung im Social Web hin. Weller greift die Entwicklungen in diesen beiden Bereichen auf und beleuchtet die Möglichkeiten und Herausforderungen, die aus der Kombination der Aktivitäten im Semantic Web und im Social Web entstehen. Der Fokus des Buches liegt dabei primär auf den konzeptuellen Herausforderungen, die sich in diesem Kontext ergeben. So strebt die originäre Vision des Semantic Web die Annotation aller Webinhalte mit ausdrucksstarken, hochformalisierten Ontologien an. Im Social Web hingegen werden große Mengen an Daten von Nutzern erstellt, die häufig mithilfe von unkontrollierten Tags in Folksonomies annotiert werden. Weller sieht in derartigen kollaborativ erstellten Inhalten und Annotationen großes Potenzial für die semantische Indexierung, eine wichtige Voraussetzung für das Retrieval im Web. Das Hauptinteresse des Buches besteht daher darin, eine Brücke zwischen den Wissensrepräsentations-Methoden im Social Web und im Semantic Web zu schlagen. Um dieser Fragestellung nachzugehen, gliedert sich das Buch in drei Teile. . . .
Insgesamt besticht das Buch insbesondere durch seine breite Sichtweise, die Aktualität und die Fülle an Referenzen. Es ist somit sowohl als Überblickswerk geeignet, das umfassend über aktuelle Entwicklungen und Trends der Wissensrepräsentation im Semantic und Social Web informiert, als auch als Lektüre für Experten, für die es vor allem als kontextualisierte und sehr aktuelle Sammlung von Referenzen eine wertvolle Ressource darstellt." Weitere Rez. in: Journal of Documentation. 67(2011), no.5, S.896-899 (P. Rafferty)

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Abstract

Das wachsende Informationsvolumen im WWW führt paradoxerweise zu einer immer schwierigeren Nutzung, das Finden und Verknüpfen von Informationen in einem unstrukturierten Umfeld wird zur Sisyphosarbeit. Hier versprechen Semantic-Web-Ansätze Abhilfe. Die Autoren beschreiben Technologien, wie eine semantische Integration verteilter Daten durch verteilte Ontologien erreicht werden kann. Diese Techniken sind sowohl für Forscher als auch für Professionals interessant, die z.B. die Integration von Produktdaten aus verteilten Datenbanken im WWW oder von lose miteinander verbunden Anwendungen in verteilten Organisationen implementieren sollen.

LCSH

Ontologies (Information retrieval)

RSWK

Semantic Web / Ontologie <Wissensverarbeitung> / Information Retrieval / Verteilung / Metadaten / Datenintegration

Subject

Semantic Web / Ontologie <Wissensverarbeitung> / Information Retrieval / Verteilung / Metadaten / Datenintegration
Ontologies (Information retrieval)

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Abstract

One vision of the Semantic Web is that it will be much like the Web we know today, except that documents will be enriched by annotations in machine understandable markup. These annotations will provide metadata about the documents as well as machine interpretable statements capturing some of the meaning of document content. We discuss how the information retrieval paradigm might be recast in such an environment. We suggest that retrieval can be tightly bound to inference. Doing so makes today's Web search engines useful to Semantic Web inference engines, and causes improvements in either retrieval or inference to lead directly to improvements in the other.

Date

12. 2.2011 17:35:22

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Content

Introduction: envisioning semantic information spacesIndexing and knowledge organization -- Semantic technologies for knowledge representation -- Information retrieval and knowledge exploration -- Approaches to handle heterogeneity -- Problems with establishing semantic interoperability -- Formalization in indexing languages -- Typification of semantic relations -- Inferences in retrieval processes -- Semantic interoperability and inferences -- Remaining research questions.

Date

23. 7.2017 13:49:22

LCSH

Information retrieval

RSWK

Information Retrieval

Subject

Information retrieval
Information Retrieval

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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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Abstract

This paper describes an approach to path-finding in the intelligent graphs, with vertices being intelligent agents. A possible implementation of this approach is described, based on logical inference in distributed frame hierarchy. Presented approach can be used for implementing distributed intelligent information systems that include automatic navigation and path generation in hypertext, which can be used, for example in distance education, as well as for organizing intelligent web catalogues with flexible ontology-based information retrieval.

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Abstract

Ontologies improve IR systems regarding its retrieval and presentation of information, which make the task of finding information more effective, efficient, and interactive. In this paper we argue that ontologies also greatly improve the engineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We developed a prototype that shows how a domain specialist without knowledge in the IR field can build an IR system with interactive components. The resulting system provides support for users not only to find their information needs but also to extend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineering aspect described here and also the usability aspect described elsewhere.

Date

28.11.2016 12:43:22

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Abstract

Contemporary retrieval systems, which search across collections, usually ignore collection-level metadata. Alternative approaches, exploiting collection-level information, will require an understanding of the various kinds of relationships that can obtain between collection-level and item-level metadata. This paper outlines the problem and describes a project that is developing a logic-based framework for classifying collection/item metadata relationships. This framework will support (i) metadata specification developers defining metadata elements, (ii) metadata creators describing objects, and (iii) system designers implementing systems that take advantage of collection-level metadata. We present three examples of collection/item metadata relationship categories, attribute/value-propagation, value-propagation, and value-constraint and show that even in these simple cases a precise formulation requires modal notions in addition to first-order logic. These formulations are related to recent work in information retrieval and ontology evaluation.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Source

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)

Date

22. 1.2016 17:47:06

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Abstract

Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.

Date

22. 1.2016 17:30:31

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Abstract

Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized cus-tomizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-basedretrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the LinkedOpen Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches.They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based querylanguage that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensivelyevaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimediaretrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of rec-ommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not providehelpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semanticsearch engines that can consume LOD resources. (PDF) Similarity-based knowledge graph queries for recommendation retrieval. Available from: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval [accessed May 21 2020].

Content

Vgl.: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval. Vgl. auch: http://semantic-web-journal.net/content/similarity-based-knowledge-graph-queries-recommendation-retrieval-1.

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Abstract

In the process of searching for a particular course on e-learning platforms, it is required to browse through different platforms, and it becomes a time-consuming process. To resolve the issue, an ontology has been developed that can provide single-point access to all the e-learning platforms. The modelled ONline Course Ontology (ONCO) is based on YAMO, METHONTOLOGY and IDEF5 and built on the Protégé ontology editing tool. ONCO is integrated with sample data and later evaluated using pre-defined competency questions. Complex SPARQL queries are executed to identify the effectiveness of the constructed ontology. The modelled ontology is able to retrieve all the sampled queries. The ONCO has been developed for the efficient retrieval of similar courses from massive open online course (MOOC) platforms.

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Abstract

Full-text documents are a vital and rapidly growing part of online biomedical information. A single large document can contain as much information as a small database, but normally lacks the tight structure and consistent indexing of a database. Retrieval systems will often miss highly relevant parts of a document if the document as a whole appears irrelevant. Access to full-text information is further complicated by the need to search separately many disparate information resources. This research explores how these problems can be addressed by the combined use of 2 techniques: 1) natural language processing for automatic concept-based indexing of full text, and 2) methods for exploiting the structure and hierarchy of full-text documents. We describe methods for applying these techniques to a large collection of full-text documents drawn from the Health Services / Technology Assessment Text (HSTAT) database at the NLM and examine how this hierarchical concept indexing can assist both document- and source-level retrieval in the context of NLM's Information Source Map project

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Abstract

Indexing plays a vital role in Information Retrieval. With the availability of huge volume of information, it has become necessary to index the information in such a way to make easier for the end users to find the information they want efficiently and accurately. Keyword-based indexing uses words as indexing terms. It is not capable of capturing the implicit relation among terms or the semantics of the words in the document. To eliminate this limitation, ontology-based indexing came into existence, which allows semantic based indexing to solve complex and indirect user queries. Ontologies are used for document indexing which allows semantic based information retrieval. Existing ontologies or the ones constructed from scratch are used presently for indexing. Constructing ontologies from scratch is a labor-intensive task and requires extensive domain knowledge whereas use of an existing ontology may leave some important concepts in documents un-annotated. Using multiple ontologies can overcome the problem of missing out concepts to a great extent, but it is difficult to manage (changes in ontologies over time by their developers) multiple ontologies and ontology heterogeneity also arises due to ontologies constructed by different ontology developers. One possible solution to managing multiple ontologies and build from scratch is to use modular ontologies for indexing.
Modular ontologies are built in modular manner by combining modules from multiple relevant ontologies. Ontology heterogeneity also arises during modular ontology construction because multiple ontologies are being dealt with, during this process. Ontologies need to be aligned before using them for modular ontology construction. The existing approaches for ontology alignment compare all the concepts of each ontology to be aligned, hence not optimized in terms of time and search space utilization. A new indexing technique is proposed based on modular ontology. An efficient ontology alignment technique is proposed to solve the heterogeneity problem during the construction of modular ontology. Results are satisfactory as Precision and Recall are improved by (8%) and (10%) respectively. The value of Pearsons Correlation Coefficient for degree of similarity, time, search space requirement, precision and recall are close to 1 which shows that the results are significant. Further research can be carried out for using modular ontology based indexing technique for Multimedia Information Retrieval and Bio-Medical information retrieval.

Date

20. 1.2015 18:30:22

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Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31