Search (40 results, page 1 of 2)

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Content

Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.

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Abstract

While classifications are heavily used to categorize web content, the evolution of the web foresees a more formal structure - ontology - which can serve this purpose. Ontologies are core artifacts of the Semantic Web which enable machines to use inference rules to conduct automated reasoning on data. Lightweight ontologies bridge the gap between classifications and ontologies. A lightweight ontology (LO) is an ontology representing a backbone taxonomy where the concept of the child node is more specific than the concept of the parent node. Formal lightweight ontologies can be generated from their informal ones. The key applications of formal lightweight ontologies are document classification, semantic search, and data integration. However, these applications suffer from the following problems: the disambiguation accuracy of the state of the art NLP tools used in generating formal lightweight ontologies from their informal ones; the lack of background knowledge needed for the formal lightweight ontologies; and the limitation of ontology reuse. In this dissertation, we propose a novel solution to these problems in formal lightweight ontologies; namely, faceted lightweight ontology (FLO). FLO is a lightweight ontology in which terms, present in each node label, and their concepts, are available in the background knowledge (BK), which is organized as a set of facets. A facet can be defined as a distinctive property of the groups of concepts that can help in differentiating one group from another. Background knowledge can be defined as a subset of a knowledge base, such as WordNet, and often represents a specific domain.

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

Converting UDC numbers manually to a complex format such as the one mentioned above is an unrealistic expectation; supporting building these representations, as far as possible automatically, is a well-founded requirement. An additional advantage of this approach is that the existing records could also be processed and converted. In my dissertation I would like to prove also that it is possible to design and implement an algorithm that is able to convert pre-coordinated UDC numbers into the introduced format by identifying all their elements and revealing their whole syntactic structure as well. In my dissertation I will discuss a feasible way of building a UDC-specific XML schema for describing the most detailed and complicated UDC numbers (containing not only the common auxiliary signs and numbers, but also the different types of special auxiliaries). The schema definition is available online at: http://piros.udc-interpreter.hu#xsd. The primary goal of my research is to prove that it is possible to support building, retrieving, and analyzing UDC numbers without compromises, by taking the whole syntactic richness of the scheme by storing the UDC numbers reserving the meaning of pre-coordination. The research has also included the implementation of a software that parses UDC classmarks attended to prove that such solution can be applied automatically without any additional effort or even retrospectively on existing collections.

Content

Vgl. auch: New automatic interpreter for complex UDC numbers. Unter: <https%3A%2F%2Fudcc.org%2Ffiles%2FAttilaPiros_EC_36-37_2014-2015.pdf&usg=AOvVaw3kc9CwDDCWP7aArpfjrs5b>

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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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Abstract

Die Arbeit geht dem Status quo der unternehmensweiten Suche in österreichischen Großunternehmen nach und beleuchtet Faktoren, die darauf Einfluss haben. Aus der Analyse des Ist-Zustands wird der Bedarf an Enterprise-Search-Software abgeleitet und es werden Rahmenbedingungen für deren erfolgreiche Einführung skizziert. Die Untersuchung stützt sich auf eine im Jahr 2009 durchgeführte Onlinebefragung von 469 österreichischen Großunternehmen (Rücklauf 22 %) und daran anschließende Leitfadeninterviews mit zwölf Teilnehmern der Onlinebefragung. Der theoretische Teil verortet die Arbeit im Kontext des Informations- und Wissensmanagements. Der Fokus liegt auf dem Ansatz der Enterprise Search, ihrer Abgrenzung gegenüber der Suche im Internet und ihrem Leistungsspektrum. Im empirischen Teil wird zunächst aufgezeigt, wie die Unternehmen ihre Informationen organisieren und welche Probleme dabei auftreten. Es folgt eine Analyse des Status quo der Informati-onssuche im Unternehmen. Abschließend werden Bekanntheit und Einsatz von Enterprise-Search-Software in der Zielgruppe untersucht sowie für die Einführung dieser Software nötige Rahmenbedingungen benannt. Defizite machen die Befragten insbesondere im Hinblick auf die übergreifende Suche im Unternehmen und die Suche nach Kompetenzträgern aus. Hier werden Lücken im Wissensmanagement offenbar. 29 % der Respondenten der Onlinebefragung geben zu-dem an, dass es in ihren Unternehmen gelegentlich bis häufig zu Fehlentscheidungen infolge defizitärer Informationslagen kommt. Enterprise-Search-Software kommt in 17 % der Unternehmen, die sich an der Onlinebefragung beteiligten, zum Einsatz. Die durch Enterprise-Search-Software bewirkten Veränderungen werden grundsätzlich posi-tiv beurteilt. Alles in allem zeigen die Ergebnisse, dass Enterprise-Search-Strategien nur Erfolg haben können, wenn man sie in umfassende Maßnahmen des Informations- und Wissensmanagements einbettet.

Date

22. 1.2016 20:40:31

Location

A

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Footnote

Vgl. unter: https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwizweHljdbcAhVS16QKHXcFD9QQFjABegQICRAB&url=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F271200105_Die_Autonomie_des_Menschen_und_der_Maschine_-_gegenwartige_Definitionen_von_Autonomie_zwischen_philosophischem_Hintergrund_und_technologischer_Umsetzbarkeit_Redigierte_Version_der_Magisterarbeit_Karls&usg=AOvVaw06orrdJmFF2xbCCp_hL26q.

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Abstract

In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.

Content

A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.

Date

10. 1.2013 19:22:47

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Date

3. 2.2013 18:00:22

Location

A

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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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Date

11. 9.2012 19:43:22

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Pages

22 S

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Date

4. 2.2015 9:22:39

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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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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

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.

Footnote

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington.

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Abstract

Natural language contradictions are of complex nature. As will be shown in Chapter 5, the realization of contradictions is not limited to the examples such as Socrates is a man and Socrates is not a man (under the condition that Socrates refers to the same object in the real world), which is discussed by Aristotle (Section 3.1.1). Empirical evidence (see Chapter 5 for more details) shows that only a few contradictions occurring in the real life are of that explicit (prototypical) kind. Rather, con-tradictions make use of a variety of natural language devices such as, e.g., paraphrasing, synonyms and antonyms, passive and active voice, diversity of negation expression, and figurative linguistic means such as idioms, irony, and metaphors. Additionally, the most so-phisticated kind of contradictions, the so-called implicit contradictions, can be found only when applying world knowledge and after conducting a sequence of logical operations such as e.g. in: (1.1) The first prize was given to the experienced grandmaster L. Stein who, in total, col-lected ten points (7 wins and 3 draws). Those familiar with the chess rules know that a chess player gets one point for winning and zero points for losing the game. In case of a draw, each player gets a half point. Built on this idea and by conducting some simple mathematical operations, we can infer that in the case of 7 wins and 3 draws (the second part of the sentence), a player can only collect 8.5 points and not 10 points. Hence, we observe that there is a contradiction between the first and the second parts of the sentence.
Implicit contradictions will only partially be the subject of the present study, aiming primarily at identifying the realization mechanism and cues (Chapter 5) as well as finding the parts of contradictions by applying the state of the art algorithms for natural language processing without conducting deep meaning processing. Further in focus are the explicit and implicit contradictions that can be detected by means of explicit linguistic, structural, lexical cues, and by conducting some additional processing operations (e.g., counting the sum in order to detect contradictions arising from numerical divergencies). One should note that an additional complexity in finding contradictions can arise in case parts of the contradictions occur on different levels of realization. Thus, a contradiction can be observed on the word- and phrase-level, such as in a married bachelor (for variations of contradictions on lexical level, see Ganeev 2004), on the sentence level - between parts of a sentence or between two or more sentences, or on the text level - between the portions of a text or between the whole texts such as a contradiction between the Bible and the Quran, for example. Only contradictions arising at the level of single sentences occurring in one or more texts, as well as parts of a sentence, will be considered for the purpose of this study. Though the focus of interest will be on single sentences, it will make use of text particularities such as coreference resolution without establishing the referents in the real world. Finally, another aspect to be considered is that parts of the contradictions are not neces-sarily to appear at the same time. They can be separated by many years and centuries with or without time expression making their recognition by human and detection by machine challenging. According to Aristotle's ontological version of the LNC (Section 3.1.1), how-ever, the same time reference is required in order for two statements to be judged as a contradiction. Taking this into account, we set the borders for the study by limiting the ana-lyzed textual data thematically (only nine world events) and temporally (three days after the reported event had happened) (Section 5.1). No sophisticated time processing will thus be conducted.

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Abstract

The current web consists of documents that are highly heterogeneous and hard for machines to understand. The Semantic Web is a progressive movement of the Word Wide Web, aiming at converting the current web of unstructured documents to the web of data. In the Semantic Web, web documents are annotated with metadata using standardized ontology language. These annotated documents are directly processable by machines and it highly improves their usability and usefulness. In Ericsson, similar problems occur. There are massive documents being created with well-defined structures. Though these documents are about domain specific knowledge and can have rich relations, they are currently managed by a traditional search engine, which ignores the rich domain specific information and presents few data to users. Motivated by the Semantic Web, we aim to find standard ways to process these documents, extract rich domain specific information and annotate these data to documents with formal markup languages. We propose this project to develop a domain specific search engine for processing different documents and building explicit relations for them. This research project consists of the three main focuses: examining different domain specific documents and finding ways to extract their metadata; integrating a text search engine with an ontology server; exploring novel ways to build relations for documents. We implement this system and demonstrate its functions. As a prototype, the system provides required features and will be extended in the future.

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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.

Type

a

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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

Multiword expressions (MWEs) are lexical items that can be decomposed into single words and display lexical, syntactic, semantic, pragmatic and/or statistical idiosyncrasy (Sag et al., 2002; Kim, 2008; Calzolari et al., 2002). The proper treatment of multiword expressions such as rock 'n' roll and make a decision is essential for many natural language processing (NLP) applications like information extraction and retrieval, terminology extraction and machine translation, and it is important to identify multiword expressions in context. For example, in machine translation we must know that MWEs form one semantic unit, hence their parts should not be translated separately. For this, multiword expressions should be identified first in the text to be translated. The chief aim of this thesis is to develop machine learning-based approaches for the automatic detection of different types of multiword expressions in English and Hungarian natural language texts. In our investigations, we pay attention to the characteristics of different types of multiword expressions such as nominal compounds, multiword named entities and light verb constructions, and we apply novel methods to identify MWEs in raw texts. In the thesis it will be demonstrated that nominal compounds and multiword amed entities may require a similar approach for their automatic detection as they behave in the same way from a linguistic point of view. Furthermore, it will be shown that the automatic detection of light verb constructions can be carried out using two effective machine learning-based approaches.
In this thesis, we focused on the automatic detection of multiword expressions in natural language texts. On the basis of the main contributions, we can argue that: - Supervised machine learning methods can be successfully applied for the automatic detection of different types of multiword expressions in natural language texts. - Machine learning-based multiword expression detection can be successfully carried out for English as well as for Hungarian. - Our supervised machine learning-based model was successfully applied to the automatic detection of nominal compounds from English raw texts. - We developed a Wikipedia-based dictionary labeling method to automatically detect English nominal compounds. - A prior knowledge of nominal compounds can enhance Named Entity Recognition, while previously identified named entities can assist the nominal compound identification process. - The machine learning-based method can also provide acceptable results when it was trained on an automatically generated silver standard corpus. - As named entities form one semantic unit and may consist of more than one word and function as a noun, we can treat them in a similar way to nominal compounds. - Our sequence labelling-based tool can be successfully applied for identifying verbal light verb constructions in two typologically different languages, namely English and Hungarian. - Domain adaptation techniques may help diminish the distance between domains in the automatic detection of light verb constructions. - Our syntax-based method can be successfully applied for the full-coverage identification of light verb constructions. As a first step, a data-driven candidate extraction method can be utilized. After, a machine learning approach that makes use of an extended and rich feature set selects LVCs among extracted candidates. - When a precise syntactic parser is available for the actual domain, the full-coverage identification can be performed better. In other cases, the usage of the sequence labeling method is recommended.