Die Sphere-Search-Suchmaschine zur graphbasierten Suche auf heterogenen, semistrukturierten Daten

In dieser Arbeit wird die neuartige SphereSearch-Suchmaschine vorgestellt, die ein einheitliches ranglistenbasiertes Retrieval auf heterogenen XML- und Web-Daten ermöglicht. Ihre Fähigkeiten umfassen die Auswertung von vagen Struktur- und Inhaltsbedingungen sowie ein auf IR-Statistiken und einem graph-basierten Datenmodell basierendes Relevanz-Ranking. Web-Dokumente im HTML- und PDFFormat werden zunächst automatisch in ein XML-Zwischenformat konvertiert und anschließend mit Hilfe von Annotations-Tools durch zusätzliche Tags semantisch angereichtert. Die graph-basierte Suchmaschine bietet auf semi-strukturierten Daten vielfältige Suchmöglichkeiten, die von keiner herkömmlichen Web- oder XMLSuchmaschine ausgedrückt werden können: konzeptbewusste und kontextbewusste Suche, die sowohl die implizite Struktur von Daten als auch ihren Kontext berücksichtigt. Die Vorteile der SphereSearch-Suchmaschine werden durch Experimente auf verschiedenen Dokumentenkorpora demonstriert. Diese umfassen eine große, vielfältige Tags beinhaltende, nicht-schematische Enzyklopädie, die um externe Dokumente erweitert wurde, sowie einen Standard-XML-Benchmark.This thesis presents the novel SphereSearch Engine that provides unified ranked retrieval on heterogeneous XML andWeb data. Its search capabilities include vague structure and text content conditions, and relevance ranking based on IR statistics and a graph-based data model. Web pages in HTML or PDF are automatically converted into an intermediate XML format, with the option of generating semantic tags by means of linguistic annotation tools. For semi-structured data the graphbased query engine is leveraged to provide very rich search options that cannot be expressed in traditional Web or XML search engines: concept-aware and linkaware querying that takes into account the implicit structure and context of Web pages. The benefits of the SphereSearch engine are demonstrated by experiments with a large and richly tagged but non-schematic open encyclopedia extended with external documents and a standard XML benchmark

Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors

Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8%

Resolving XML Semantic Ambiguity

ABSTRACT XML semantic-aware processing has become a motivating and important challenge in Web data management, data processing, and information retrieval. While XML data is semi-structured, yet it remains prone to lexical ambiguity, and thus requires dedicated semantic analysis and sense disambiguation processes to assign well-defined meaning to XML elements and attributes. This becomes crucial in an array of applications ranging over semantic-aware query rewriting, semantic document clustering and classification, schema matching, as well as blog analysis and event detection in social networks and tweets. Most existing approaches in this context: i) ignore the problem of identifying ambiguous XML nodes, ii) only partially consider their structural relations/context, iii) use syntactic information in processing XML data regardless of the semantics involved, and iv) are static in adopting fixed disambiguation constraints thus limiting user involvement. In this paper, we provide a new XML Semantic Disambiguation Framework titled XSDF designed to address each of the above motivations, taking as input: an XML document and a general purpose semantic network, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts. Experiments demonstrate the effectiveness of our approach in comparison with alternative methods. Categories and Subject Descriptors General Terms Algorithms, Measurement, Performance, Design, Experimentation. Keywords XML semantic-aware processing, a m b i g u i t y d e g r e e , s p h e r e neighborhood, XML context vector, semantic network, semantic disambiguation