Search (116 results, page 1 of 6)

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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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Content

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

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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

Nie zuvor war der Zugriff auf Informationen so einfach und schnell wie heute. Die Suchmaschine Google ist dabei mit einem Marktanteil von 95 Prozent in Deutschland führend. Aber reicht der heutige Status Quo aus? Wir meinen nein - andere meinen ja. Die Verwendung von Stichworten für die Suche ist sehr begrenzt, nicht intelligent und der Algorithmus zum ranking der Suchergebnisse fragwürdig. Wir zeigen neue Wege der semantischen Suche mittels der Verwendung von Hintergrundwissen. Die (semi)automatische Generierung von Ontologien wird ebenfalls als zentraler Bestandteil einer universellen Wissensplattform vorgestellt und gezeigt, wie Anwender mit dieser Technologie signifikant Zeit sparen und deutlich relevantere Informationen finden.

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Abstract

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontologybased KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.

Source

The Semantic Web: research and applications ; second European Semantic WebConference, ESWC 2005, Heraklion, Crete, Greece, May 29 - June 1, 2005 ; proceedings. Eds.: A. Gómez-Pérez u. J. Euzenat

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Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22

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Abstract

Google ist zum Inbegriff einer Suchmaschine geworden. Doch ist in Fachkreisen klar, dass Volltexsuchtmaschinen wie Google auch deutliche Schwächen aufweisen und deshalb für die effiziente Suche in Fachportalen, Intranets und Enterprise-Content-Management-Systemen oft nicht ausreichen. Weil Volltextsuchmaschinen nur mit dem genauen Wortlaut suchen, finden sie einerseits Informationen nicht, die zwar dem Inhalt, nicht aber den genauen Formulierungen der Suchanfrage entsprechen. Bezeichnungsalternativen, sprachlichen Varianten sowie fremdsprachliche Benennungen werden nicht als bedeutungsgleich erkannt. Andererseits entstehen unpräzise Suchergebnisse, weil gleich lautende Bezeichnungen unterschiedlicher Bedeutung nicht unterschieden werden. Diese Probleme geht die semantische Suchmaschine ConWeaver an, die das Fraunhofer Institut Integrierte Informations- und Publikationssysteme (Fraunhofer IPSI) in Darmstadt entwickelt hat. An Stelle eines Volltextindexes setzt sie ein Wissensnetz als Suchindex ein. Im Unterschied zu den meisten anderen ontologiebasierten Softwareprodukten baut die Software ConWeaver dieses Wissensnetz automatisiert auf.

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Abstract

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.

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Abstract

Over the last decade, several ontology reasoners have been proposed to overcome the computational complexity of inference tasks on expressive ontology languages such as OWL 2 DL. Nevertheless, it is well-accepted that there is no outstanding reasoner that can outperform in all input ontologies. Thus, deciding the most suitable reasoner for an ontology based application is still a time and effort consuming task. In this paper, we suggest to develop a new system to provide user support when looking for guidance over ontology reasoners. At first, we will be looking at automatically predict a single reasoner empirical performances, in particular its robustness and efficiency, over any given ontology. Later, we aim at ranking a set of candidate reasoners in a most preferred order by taking into account information regarding their predicted performances. We conducted extensive experiments covering over 2500 well selected real-world ontologies and six state-of-the-art of the most performing reasoners. Our primary prediction and ranking results are encouraging and witnessing the potential benefits of our approach.

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RSWK

Suchmaschine / Information Retrieval

Subject

Suchmaschine / Information Retrieval

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Abstract

Das Werk behandelt die aktuellen Entwicklungen zur inhaltlichen Erschließung von Informationsquellen im Internet. Topic Maps, semantische Modelle vernetzter Informationsressourcen unter Verwendung von XML bzw. HyTime, bieten alle notwendigen Modellierungskonstrukte, um Dokumente im Internet zu klassifizieren und ein assoziatives, semantisches Netzwerk über diese zu legen. Neben Einführungen in XML, XLink, XPointer sowie HyTime wird anhand von Einsatzszenarien gezeigt, wie diese neuartige Technologie für Content Management und Information Retrieval im Internet funktioniert. Der Entwurf einer Abfragesprache wird ebenso skizziert wie der Prototyp einer intelligenten Suchmaschine. Das Buch zeigt, wie Topic Maps den Weg zu semantisch gesteuerten Suchprozessen im Internet weisen.

RSWK

Internet / Navigieren / Suchmaschine / Abfragesprache / Semantisches Netz / ISO-Norm

Subject

Internet / Navigieren / Suchmaschine / Abfragesprache / Semantisches Netz / ISO-Norm

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Content

Aus den so festgelegten Beziehungen zwischen den verlinkten Begriffen sollen Computer automatisch sinnvolle Antworten auf komplexere Anfragen generieren können; z.B. eine Liste erzeugen, in der alle Länder von Afrika aufgeführt sind. Die Ländernamen führen als Link zurück zu dem Eintrag, in dem sie stehen - dem Artikel zum Land, für das man sich interessiert. Aus informationswissenschaftlicher Sicht ist das Informationsergebnis, das die neue Technologie produziert, relativ simpel. Aus sozialwissenschaftlicher Sicht steckt darin aber ein riesiges Potential zur Verbesserung der Bereitstellung von enzyklopädischer Information und Wissen für Menschen auf der ganzen Welt. Spannend ist auch die durch Semantic MediaWiki gegebene Möglichkeit der automatischen Zusammenführung von Informationen, die in den verschiedenen Wiki-Einträgen verteilt sind, bei einer hohen Konsistenz der Ergebnisse. Durch die feststehenden Beziehungen zwischen den Links enthält die automatisch erzeugte Liste nach Angaben der Karlsruher Forscher immer die gleichen Daten, egal, von welcher Seite aus man sie abruft. Die Suchmaschine holt sich die Bevölkerungszahl von Ägypten immer vom festgelegten Ägypten-Eintrag, so dass keine unterschiedlichen Zahlen in der Wiki-Landschaft kursieren können. Ein mit Semantic MediaWiki erstellter Testeintrag zu Deutschland kann unter http://ontoworld.org/index.php/Germany eingesehen werden. Die Faktenbox im unteren Teil des Eintrags zeigt an, was der "Eintrag" der Suchmaschine an Wissen über Deutschland anbieten kann. Diese Ergebnisse werden auch in dem Datenbeschreibungsstandard RDF angeboten. Mehr als das, was in der Faktenbox steht, kann der Eintrag nicht an die Suchmaschine abgeben."

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Abstract

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.

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Abstract

Improving Information retrieval by employing the use of ontologies to overcome the limitations of syntactic search has been one of the inspirations since its emergence. This paper proposes a conceptual framework to exploit ontology based Information retrieval. This framework constitutes of five phases namely Query parsing, word stemming, ontology matching, weight assignment, ranking and Information retrieval. In the first phase, the user query is parsed into sequence of words. The parsed contents are curtailed to identify the significant word by ignoring superfluous terms such as "to", "is","ed", "about" and the like in the stemming phase. The objective of the stemming phase is to throttle feature descriptors to root words, which in turn will increase efficiency; this reduces the time consumed for searching the superfluous terms, which may not significantly influence the effectiveness of the retrieval process. In the third phase ontology matching is carried out by matching the parsed words with the relevant terms in the existing ontology. If the ontology does not exist, it is recommended to generate the required ontology. In the fourth phase the weights are assigned based on the distance between the stemmed words and the terms in the ontology uses improved matchmaking algorithm. The range of weights varies from 0 to 1 based on the level of distance in the ontology (superclass-subclass). The aggregate weights are calculated for the all the combination of stemmed words. The combination with the highest score is ranked as the best and the corresponding information is retrieved. The conceptual workflow is illustrated with an e-governance case study Academic Information System.

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Abstract

Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.

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Date

2. 3.2013 12:29:05

Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

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Content

Wer über Ontologien und Ontologiesysteme spricht, sollte auch das System OpenCyc kennen. Aber was heißt hier "kennen"? Ich habe mich als Leser und experimentierender Benutzer mit diesem System intensiver befasst und unter einer Reihe von spezielleren Fragen an das System meine Erfahrungen in einigen Arbeitspapieren protokolliert. Sie sind über das Internet zugänglich'. Hier möchte ich der allgemeinen Orientierung über OpenCyc dienende Anmerkungen dazugeben. Bereits eine Recherche mit der Suchmaschine Google, den Suchworten "Cyc OpenCyc" und Beschränkung der Quellen auf Sprache Deutsch oder Herkunft Deutschland erbringt einige der auch hier wiedergegebenen vordergründigen Informationen, sie zeigt aber auch, dass sich Professoren oder Studenten im Jahr 2003 z.B. in Ulm, Heidelberg, Berlin, Bamberg, Bonn und Darmstadt mit dem Thema Cyc und OpenCyc beschäftigt haben entsprechend meiner Titelthese.

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Date

29. 7.2011 14:44:56
26.12.2011 13:40:22

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Content

Wenn die Maschine denkt Zur Hybris des Projekts passt, dass der eyePlorer ursprünglich HAL heißen sollte - wie der außer Rand und Band geratene Bordcomputer aus Kubricks "2001: Odyssee im Weltraum". Wenn man die Buchstaben aber jeweils um eine Alphabetposition nach rechts verrückt, ergibt sich IBM. Was passiert mit unserem Wissen, wenn die Maschine selbst anfängt zu denken? Ralf von Grafenstein macht ein ernstes Gesicht. "Es ist nicht unser Ansinnen, sie alleinzulassen. Es geht bei uns ja nicht nur darum, zu finden, sondern auch mitzumachen. Die Community ist wichtig. Der Dialog ist beiderseitig." Der Lotse soll in Form einer wachsamen Gemeinschaft also an Bord bleiben. Begünstigt wird diese Annahme auch durch die aufkommenden Anfasstechnologien, mit denen das iPhone derzeit so erfolgreich ist: "Allein zehn Prozent der menschlichen Gehirnleistung gehen auf den Pinzettengriff zurück." Martin Hirsch wundert sich, dass diese Erkenntnis von der IT-Branche erst jetzt berücksichtigt wird. Auf berührungssensiblen Bildschirmen sollen die Nutzer mit wenigen Handgriffen bald spielerisch Inhalte schaffen und dem System zur Verfügung stellen. So wird aus der Suchmaschine ein "Sparringspartner" und aus einem Informationsknopf ein "Knowledge Nugget". Wie auch immer man die Erkenntniszutaten des Internetgroßmarkts serviert: Wissen als Zeitwort ist ein länglicher Prozess. Im Moment sei die Maschine noch auf dem Stand eines Zweijährigen, sagen ihre Schöpfer. Sozialisiert werden soll sie demnächst im Internet, ihre Erziehung erfolgt dann durch die Nutzer. Als er Martin Hirsch mit seiner Scheibe zum ersten Mal gesehen habe, dachte Ralf von Grafenstein: "Das ist überfällig! Das wird kommen! Das muss raus!" Jetzt ist es da, klein, unschuldig und unscheinbar. Man findet es bei Google."

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Date

4. 9.2011 12:29:09