Search (22 results, page 1 of 2)

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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.

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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Abstract

Description of an experimental trend-predicting exercise covering a time period as far as 50 years into the future. The Delphi technique is used in soliciting the opinions of experts in six areas: scientific breakthroughs, population growth, automation, space progress, probability and prevention of war, and future weapon systems. Possible objections to the approach are also discussed.

Date

22. 6.2018 13:24:08
22. 6.2018 13:54:52

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Abstract

The past decade has seen the emergence of a new paradigm in the corporate world where organisations emphasised connectivity as a means of exposing decision-makers to wider resources of information within and outside the organisation. Many organisations followed the initiatives of enhancing infrastructures, manipulating cultural shifts and emphasising managerial commitment for creating pools and networks of knowledge. However, the concept of connectivity is not merely presenting people with the data, but more importantly, to create environments where people can seek information efficiently. This paradigm has therefore caused a shift in the function of information systems in organisations. They have to be now assessed in relation to how they underpin people's information-seeking activities within the context of their organisational environment. This research project used interpretative research methods to investigate the nature of people's information-seeking activities at two culturally contrasting organisations. Outcomes of this research project provide insights into phenomena associated with people's information-seeking function, and show how they depend on the organisational context that is defined partly by information systems. It suggests that information-seeking is not just searching for data. The inefficiencies inherent in both people and their environments can bring opaqueness into people's data, which they need to avoid or eliminate as part of seeking information. This seems to have made information-seeking a two-tier process consisting of a primary process of searching and interpreting data and auxiliary process of avoiding and eliminating opaqueness in data. Based on this view, this research suggests that organisational information systems operate naturally as implicit dual-mechanisms to underpin the above two-tier process, and that improvements to information systems should concern maintaining the balance in these dual-mechanisms.

Date

22. 7.2022 12:16:58

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Content

PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.

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Abstract

This dissertation investigates the social and technological factors of collaboratively organizing information in commons-based peer production systems. To do so, it analyzes the diverse strategies that members of Wikimedia Foundation (WMF) project communities use to organize information. Key findings from this dissertation show that conceptual structures of information organization are encoded into the infrastructure of WMF projects. The fact that WMF projects are commons-based peer production systems means that we can inspect the code that enables these systems, but a specific type of technical literacy is required to do so. I use three methods in this dissertation. I conduct a qualitative content analysis of the discussions surrounding the design, implementation and evaluation of the category system; a quantitative analysis using descriptive statistics of patterns of editing among editors who contributed to the code of templates for information boxes; and a close reading of the infrastructure used to create the category system, the infobox templates, and the knowledge base of structured data.

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Abstract

Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.

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Abstract

In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.

Source

Information Systems. 37(2012) no. 4, S.294-305

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Date

10. 1.2013 19:22:47

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Abstract

The focus of this thesis is information use. Although a key concept in information behaviour, information use has received little attention from information science researchers. Studies of other key concepts such as information need and information seeking are dominant in information behaviour research. Information use is an area of interest to information professionals who rely on research outcomes to shape their practice. There are few empirical studies of how people actually use information that might guide and refine the development of information systems, products and services.

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Abstract

In the Internet information space, advanced information retrieval (IR) methods and automatic text processing are used in conjunction with traditional knowledge organization systems (KOS). New information technology provides a platform for better KOS publishing, exploitation and sharing both for human and machine use. Networked KOS services are now being planned and developed as powerful tools for resource discovery. They will enable automatic contextualisation, interpretation and query matching to different indexing languages. The Semantic Web promises to be an environment in which the quality of semantic relationships in bibliographic classification systems can be fully exploited. Their use in the networked environment is, however, limited by the fact that they are not prepared or made available for advanced machine processing. The UDC was chosen for this research because of its widespread use and its long-term presence in online information retrieval systems. It was also the first system to be used for the automatic classification of Internet resources, and the first to be made available as a classification tool on the Web. The objective of this research is to establish the advantages of using UDC for information retrieval in a networked environment, to highlight the problems of automation and classification exchange, and to offer possible solutions. The first research question was is there enough evidence of the use of classification on the Internet to justify further development with this particular environment in mind? The second question is what are the automation requirements for the full exploitation of UDC and its exchange? The third question is which areas are in need of improvement and what specific recommendations can be made for implementing the UDC in a networked environment? A summary of changes required in the management and development of the UDC to facilitate its full adaptation for future use is drawn from this analysis.

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Date

3. 2.2013 18:00:22

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Abstract

Viele auch kleinere Unternehmen, Verwaltungen und Organisationen sind angesichts eines wachsenden Berges von digitalen Informationen mit dem Ordnen und Strukturieren ihrer Ablagen beschäftigt. In den meisten Organisationen besteht ein Konzept der Dokumentenlenkung. Records Management verfolgt vor allem in zwei Punkten einen weiterführenden Ansatz. Zum einen stellt es über den Geschäftsalltag hinaus den Kontext und den Entstehungszusammenhang ins Zentrum und zum anderen gibt es Regeln vor, wie mit ungenutzten oder inaktiven Dokumenten zu verfahren ist. Mit den «Model Requirements for the Management of Electronic Records» - MoReq - wurde von der europäischen Kommission ein Standard geschaffen, der alle Kernbereiche des Records Managements und damit den gesamten Entstehungs-, Nutzungs-, Archivierungsund Aussonderungsbereich von Dokumenten abdeckt. In der «Anleitung zur Umsetzung» wird die umfangreiche Anforderungsliste von MoReq2 (August 2008) zusammengefasst und durch erklärende Abschnitte ergänzt, mit dem Ziel, als griffiges Instrument bei der Einführung eines Record Management Systems zu dienen.

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Abstract

While being theoretically so widely available, information can be restricted from a more general use by linguistic barriers. The linguistic aspects of the information languages and particularly the chances of an enhanced access to information by means of multilingual access facilities will make the substance of this thesis. The main problem of this research is thus to demonstrate that information retrieval can be improved by using multilingual thesaurus terms based on an intermediate or switching language to search with. Universal classification systems in general can play the role of switching languages for reasons dealt with in the forthcoming pages. The Universal Decimal Classification (UDC) in particular is the classification system used as example of a switching language for our objectives. The question may arise: why a universal classification system and not another thesaurus? Because the UDC like most of the classification systems uses symbols. Therefore, it is language independent and the problems of compatibility between such a thesaurus and different other thesauri in different languages are avoided. Another question may still arise? Why not then, assign running numbers to the descriptors in a thesaurus and make a switching language out of the resulting enumerative system? Because of some other characteristics of the UDC: hierarchical structure and terminological richness, consistency and control. One big problem to find an answer to is: can a thesaurus be made having as a basis a classification system in any and all its parts? To what extent this question can be given an affirmative answer? This depends much on the attributes of the universal classification system which can be favourably used to this purpose. Examples of different situations will be given and discussed upon beginning with those classes of UDC which are best fitted for building a thesaurus structure out of them (classes which are both hierarchical and faceted)...

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Abstract

Industries have adopted a standardized set of practices for developing their products. In the automotive domain, the provision of safety-compliant systems is guided by ISO 26262, a standard that specifies a set of requirements and recommendations for developing automotive safety-critical systems. For being in compliance with ISO 26262, the safety lifecycle proposed by the standard must be included in the development process of a vehicle. Besides, a safety case that shows that the system is acceptably safe has to be provided. The provision of a safety case implies the execution of a precise documentation process. This process makes sure that the work products are available and traceable. Further, the documentation management is defined in the standard as a mandatory activity and guidelines are proposed/imposed for its elaboration. It would be appropriate to point out that a well-documented safety lifecycle will provide the necessary inputs for the generation of an ISO 26262-compliant safety case. The OSLC (Open Services for Lifecycle Collaboration) standard and the maturing stack of semantic web technologies represent a promising integration platform for enabling semantic interoperability between the tools involved in the safety lifecycle. Tools for requirements, architecture, development management, among others, are expected to interact and shared data with the help of domains specifications created in OSLC. This thesis proposes the creation of an OSLC tool-chain infrastructure for sharing safety-related information, where fragments of safety information can be generated. The steps carried out during the elaboration of this master thesis consist in the identification, representation, and shaping of the RDF resources needed for the creation of a safety case. The focus of the thesis is limited to a tiny portion of the ISO 26262 left-hand side of the V-model, more exactly part 6 clause 8 of the standard: Software unit design and implementation. Regardless of the use of a restricted portion of the standard during the execution of this thesis, the findings can be extended to other parts, and the conclusions can be generalize. This master thesis is considered one of the first steps towards the provision of an OSLC-based and ISO 26262-compliant methodological approach for representing and shaping the work products resulting from the execution of the safety lifecycle, documentation required in the conformation of an ISO-compliant safety case.

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Date

20. 1.2015 18:30:22

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Abstract

This dissertation studies ontology mapping: the problem of finding semantic correspondences between similar elements of different ontologies. In the dissertation, elements denote classes or properties of ontologies. The goal of this research is to use ontology mapping to make heterogeneous information more accessible. The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semiautomated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests.

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Abstract

The amount and complexity of information available in a digital form is already huge and new information is being produced every day. Retrieving information relevant to address a particular need becomes a significant issue. This work utilizes knowledge organization systems (KOS), such as thesauri and ontologies and applies information extraction (IE) and computational linguistics (CL) techniques to organize, manage and retrieve information stored in digital collections in the agricultural domain. Two real world applications of the approach have been developed and are available and actively used by the public. An ontology is used to manage the Water Conservation Digital Library holding a dynamic collection of various types of digital resources in the domain of urban water conservation in Florida, USA. The ontology based back-end powers a fully operational web interface, available at http://library.conservefloridawater.org. The system has demonstrated numerous benefits of the ontology application, including accurate retrieval of resources, information sharing and reuse, and has proved to effectively facilitate information management. The major difficulty encountered with the approach is that large and dynamic number of concepts makes it difficult to keep the ontology consistent and to accurately catalog resources manually. To address the aforementioned issues, a combination of IE and CL techniques, such as Vector Space Model and probabilistic parsing, with the use of Agricultural Thesaurus were adapted to automatically extract concepts important for each of the texts in the Best Management Practices (BMP) Publication Library--a collection of documents in the domain of agricultural BMPs in Florida available at http://lyra.ifas.ufl.edu/LIB. A new approach of domain-specific concept discovery with the use of Internet search engine was developed. Initial evaluation of the results indicates significant improvement in precision of information extraction. The approach presented in this work focuses on problems unique to agriculture and natural resources domain, such as domain specific concepts and vocabularies, but should be applicable to any collection of texts in digital format. It may be of potential interest for anyone who needs to effectively manage a collection of digital resources.

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Abstract

In this thesis, we will present methods for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently found in information retrieval. This utilization of ontologies has a number of challenges. Our focus is on the use of similarity measures derived from the knowledge about relations between concepts in ontologies, the recognition of semantic information in texts and the mapping of this knowledge into the ontologies in use, as well as how to fuse together the ideas of ontological similarity and ontological indexing into a realistic information retrieval scenario. To achieve the recognition of semantic knowledge in a text, shallow natural language processing is used during indexing that reveals knowledge to the level of noun phrases. Furthermore, we briefly cover the identification of semantic relations inside and between noun phrases, as well as discuss which kind of problems are caused by an increase in compoundness with respect to the structure of concepts in the evaluation of queries. Measuring similarity between concepts based on distances in the structure of the ontology is discussed. In addition, a shared nodes measure is introduced and, based on a set of intuitive similarity properties, compared to a number of different measures. In this comparison the shared nodes measure appears to be superior, though more computationally complex. Some of the major problems of shared nodes which relate to the way relations differ with respect to the degree they bring the concepts they connect closer are discussed. A generalized measure called weighted shared nodes is introduced to deal with these problems. Finally, the utilization of concept similarity in query evaluation is discussed. A semantic expansion approach that incorporates concept similarity is introduced and a generalized fuzzy set retrieval model that applies expansion during query evaluation is presented. While not commonly used in present information retrieval systems, it appears that the fuzzy set model comprises the flexibility needed when generalizing to an ontology-based retrieval model and, with the introduction of a hierarchical fuzzy aggregation principle, compound concepts can be handled in a straightforward and natural manner.

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Abstract

As the Web continues to grow and encompass broader and more diverse sources of information, providing effective search facilities to users becomes an increasingly challenging problem. To help users deal with the deluge of Web-accessible information, we propose a search system which makes use of context to improve search results in a scalable way. By context, we mean any sources of information, in addition to any search query, that provide clues about the user's true information need. For instance, a user's bookmarks and search history can be considered a part of the search context. We consider two types of context-based search. The first type of functionality we consider is "similarity search." In this case, as the user is browsing Web pages, URLs for pages similar to the current page are retrieved and displayed in a side panel. No query is explicitly issued; context alone (i.e., the page currently being viewed) is used to provide the user with useful related information. The second type of functionality involves taking search context into account when ranking results to standard search queries. Web search differs from traditional information retrieval tasks in several major ways, making effective context-sensitive Web search challenging. First, scalability is of critical importance. With billions of publicly accessible documents, the Web is much larger than traditional datasets. Similarly, with millions of search queries issued each day, the query load is much higher than for traditional information retrieval systems. Second, there are no guarantees on the quality ofWeb pages, with Web-authors taking an adversarial, rather than cooperative, approach in attempts to inflate the rankings of their pages. Third, there is a significant amount of metadata embodied in the link structure corresponding to the hyperlinks between Web pages that can be exploitedduring the retrieval process. In this thesis, we design a search system, using the Stanford WebBase platform, that exploits the link structure of the Web to provide scalable, context-sensitive search.