Search (11 results, page 1 of 1)

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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

Since its appearance in the early 90's, the World Wide Web (WWW or Web) has provided universal access to knowledge and the world of information has been primarily witness to a great revolution (the digital revolution). It quickly became very popular, making it the largest and most comprehensive database and knowledge base thanks to the amount and diversity of data it contains. However, the considerable increase and evolution of these data raises important problems for users, in particular for accessing the documents most relevant to their search queries. In order to cope with this exponential explosion of data volume and facilitate their access by users, various models are offered by information retrieval systems (IRS) for the representation and retrieval of web documents. Traditional SRIs use simple keywords that are not semantically linked to index and retrieve these documents. This creates limitations in terms of the relevance and ease of exploration of results. To overcome these limitations, existing techniques enrich documents by integrating external keywords from different sources. However, these systems still suffer from limitations that are related to the exploitation techniques of these sources of enrichment. When the different sources are used so that they cannot be distinguished by the system, this limits the flexibility of the exploration models that can be applied to the results returned by this system. Users then feel lost to these results, and find themselves forced to filter them manually to select the relevant information. If they want to go further, they must reformulate and target their search queries even more until they reach the documents that best meet their expectations. In this way, even if the systems manage to find more relevant results, their presentation remains problematic. In order to target research to more user-specific information needs and improve the relevance and exploration of its research findings, advanced SRIs adopt different data personalization techniques that assume that current research of user is directly related to his profile and / or previous browsing / search experiences.
However, this assumption does not hold in all cases, the needs of the user evolve over time and can move away from his previous interests stored in his profile. In other cases, the user's profile may be misused to extract or infer new information needs. This problem is much more accentuated with ambiguous queries. When multiple POIs linked to a search query are identified in the user's profile, the system is unable to select the relevant data from that profile to respond to that request. This has a direct impact on the quality of the results provided to this user. In order to overcome some of these limitations, in this research thesis, we have been interested in the development of techniques aimed mainly at improving the relevance of the results of current SRIs and facilitating the exploration of major collections of documents. To do this, we propose a solution based on a new concept and model of indexing and information retrieval called multi-spaces projection. This proposal is based on the exploitation of different categories of semantic and social information that enrich the universe of document representation and search queries in several dimensions of interpretations. The originality of this representation is to be able to distinguish between the different interpretations used for the description and the search for documents. This gives a better visibility on the results returned and helps to provide a greater flexibility of search and exploration, giving the user the ability to navigate one or more views of data that interest him the most. In addition, the proposed multidimensional representation universes for document description and search query interpretation help to improve the relevance of the user's results by providing a diversity of research / exploration that helps meet his diverse needs and those of other different users. This study exploits different aspects that are related to the personalized search and aims to solve the problems caused by the evolution of the information needs of the user. Thus, when the profile of this user is used by our system, a technique is proposed and used to identify the interests most representative of his current needs in his profile. This technique is based on the combination of three influential factors, including the contextual, frequency and temporal factor of the data. The ability of users to interact, exchange ideas and opinions, and form social networks on the Web, has led systems to focus on the types of interactions these users have at the level of interaction between them as well as their social roles in the system. This social information is discussed and integrated into this research work. The impact and how they are integrated into the IR process are studied to improve the relevance of the results.

Date

29. 9.2018 18:57:38

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Abstract

More and more information is available on-line every day. The greater the amount of on-line information, the greater the demand for tools that process and disseminate this information. Processing electronic information in the form of text and answering users' queries about that information intelligently is one of the great challenges in natural language processing and information retrieval. The research presented in this talk is centered on the latter of these two tasks: intelligent information retrieval. In order for information to be retrieved, it first needs to be formalized in a database or knowledge base. The ontology for this formalization and assumptions it is based on are crucial to successful intelligent information retrieval. We have concentrated our effort on developing an ontology for representing knowledge in the domains of experimental sciences, molecular biology in particular. We show that existing ontological models cannot be readily applied to represent this domain adequately. For example, the fundamental notion of ontology design that every "real" object is defined as an instance of a category seems incompatible with the universe where objects can change their category as a result of experimental procedures. Another important problem is representing complex structures such as DNA, mixtures, populations of molecules, etc., that are very common in molecular biology. We present extensions that need to be made to an ontology to cover these issues: the representation of transformations that change the structure and/or category of their participants, and the component relations and spatial structures of complex objects. We demonstrate examples of how the proposed representations can be used to improve the quality and completeness of answers to user queries; discuss techniques for evaluating ontologies and show a prototype of an Information Retrieval System that we developed.

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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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Abstract

Information management in corporate environments is a growing problem as companies' information assets grow and their need to use them in their operations. Several management models have been practiced with application on the most diverse fronts, practices that integrate the so-called Enterprise Content Management. This study proposes a conceptual model of semantic corporate information ecosystem, based on the Universal Document Model proposed by Dagobert Soergel. It focuses on unstructured information objects, especially multimedia, increasingly used in corporate environments, adding semantics and expanding their recovery potential in the composition and reuse of dynamic documents on demand. The proposed model considers stable elements in the organizational environment, such as actors, processes, business metadata and information objects, as well as some basic infrastructures of the corporate information environment. The main objective is to establish a conceptual model that adds semantic intelligence to information assets, leveraging pre-existing infrastructure in organizations, integrating and relating objects to other objects, actors and business processes. The approach methodology considered the state of the art of Information Organization, Representation and Retrieval, Organizational Content Management and Semantic Web technologies, in the scientific literature, as bases for the establishment of an integrative conceptual model. Therefore, the research will be qualitative and exploratory. The predicted steps of the model are: Environment, Data Type and Source Definition, Data Distillation, Metadata Enrichment, and Storage. As a result, in theoretical terms the extended model allows to process heterogeneous and unstructured data according to the established cut-outs and through the processes listed above, allowing value creation in the composition of dynamic information objects, with semantic aggregations to metadata.

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Date

30. 5.2010 16:22:35

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Date

18.10.2010 21:05:29

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Abstract

My thesis discusses two related problems that have taken center stage in the recent literature on concepts: 1) What are the individuation conditions of concepts? Under what conditions is a concept Cv(1) the same concept as a concept Cv(2)? 2) What are the possession conditions of concepts? What conditions must be satisfied for a thinker to have a concept C? The thesis defends a novel account of concepts, which I call "pluralist-contextualist": 1) Pluralism: Different concepts have different kinds of individuation and possession conditions: some concepts are individuated more "coarsely", have less demanding possession conditions and are widely shared, while other concepts are individuated more "finely" and not shared. 2) Contextualism: When a speaker ascribes a propositional attitude to a subject S, or uses his ascription to explain/predict S's behavior, the speaker's intentions in the relevant context determine the correct individuation conditions for the concepts involved in his report. In chapters 1-3 I defend a contextualist, non-Millian theory of propositional attitude ascriptions. Then, I show how contextualism can be used to offer a novel perspective on the problem of concept individuation/possession. More specifically, I employ contextualism to provide a new, more effective argument for Fodor's "publicity principle": if contextualism is true, then certain specific concepts must be shared in order for interpersonally applicable psychological generalizations to be possible. In chapters 4-5 I raise a tension between publicity and another widely endorsed principle, the "Fregean constraint" (FC): subjects who are unaware of certain identity facts and find themselves in so-called "Frege cases" must have distinct concepts for the relevant object x. For instance: the ancient astronomers had distinct concepts (HESPERUS/PHOSPHORUS) for the same object (the planet Venus). First, I examine some leading theories of concepts and argue that they cannot meet both of our constraints at the same time. Then, I offer principled reasons to think that no theory can satisfy (FC) while also respecting publicity. (FC) appears to require a form of holism, on which a concept is individuated by its global inferential role in a subject S and can thus only be shared by someone who has exactly the same inferential dispositions as S. This explains the tension between publicity and (FC), since holism is clearly incompatible with concept shareability. To solve the tension, I suggest adopting my pluralist-contextualist proposal: concepts involved in Frege cases are holistically individuated and not public, while other concepts are more coarsely individuated and widely shared; given this "plurality" of concepts, we will then need contextual factors (speakers' intentions) to "select" the specific concepts to be employed in our intentional generalizations in the relevant contexts. In chapter 6 I develop the view further by contrasting it with some rival accounts. First, I examine a very different kind of pluralism about concepts, which has been recently defended by Daniel Weiskopf, and argue that it is insufficiently radical. Then, I consider the inferentialist accounts defended by authors like Peacocke, Rey and Jackson. Such views, I argue, are committed to an implausible picture of reference determination, on which our inferential dispositions fix the reference of our concepts: this leads to wrong predictions in all those cases of scientific disagreement where two parties have very different inferential dispositions and yet seem to refer to the same natural kind.

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Date

26. 9.2006 21:00:22

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Date

20. 1.2015 18:30:22