Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Interpreting and Answering Keyword Queries using Web Knowledge Bases

Many keyword queries issued to Web search engines target information about real world entities, and interpreting these queries over Web knowledge bases can allow a search system to provide exact answers to keyword queries. Such an ability provides a useful service to end users, as their information need can be directly addressed and they need not scour textual results for the desired information. However, not all keyword queries can be addressed by even the most comprehensive knowledge base, and therefore equally important is the problem of recognizing when a reference knowledge base is not capable of modelling the keyword query's intention. This may be due to lack of coverage of the knowledge base or lack of expressiveness in the underlying query representation formalism. This thesis presents an approach to computing structured representations of keyword queries over a reference knowledge base. Keyword queries are annotated with occurrences of semantic constructs by learning a sequential labelling model from an annotated Web query log. Frequent query structures are then mined from the query log and are used along with the annotations to map keyword queries into a structured representation over the vocabulary of a reference knowledge base. The proposed approach exploits coarse linguistic structure in keyword queries, and combines it with rich structured query representations of information needs. As an intermediate representation formalism, a novel query language is proposed that blends keyword search with structured query processing over large Web knowledge bases. The formalism for structured keyword queries combines the flexibility of keyword search with the expressiveness of structures queries. A solution to the resulting disambiguation problem caused by introducing keywords as primitives in a structured query language is presented. Expressions in our proposed language are rewritten using the vocabulary of the knowledge base, and different possible rewritings are ranked based on their syntactic relationship to the keywords in the query as well as their semantic coherence in the underlying knowledge base. The problem of ranking knowledge base entities returned as a query result is also explored from the perspective of personalized result ranking. User interest models based on entity types are learned from a Web search session by cross referencing clicks on URLs with known entity homepages. The user interest model is then used to effectively rerank answer lists for a given user. A methodology for evaluating entity-based search engines is also proposed and empirically evaluated

Searching and ranking in entity-relationship graphs

The Web bears the potential to become the world';s most comprehensive knowledge base. Organizing information from the Web into entity-relationship graph structures could be a first step towards unleashing this potential. In a second step, the inherent semantics of such structures would have to be exploited by expressive search techniques that go beyond today';s keyword search paradigm. In this realm, as a first contribution of this thesis, we present NAGA (Not Another Google Answer), a new semantic search engine. NAGA provides an expressive, graph-based query language that enables queries with entities and relationships. The results are retrieved based on subgraph matching techniques and ranked by means of a statistical ranking model. As a second contribution, we present STAR (Steiner Tree Approximation in Relationship Graphs), an efficient technique for finding "close'; relations (i.e., compact connections) between k(> 2) entities of interest in large entity-relationship graphs. Our third contribution is MING (Mining Informative Graphs). MING is an efficient method for retrieving "informative'; subgraphs for k(> 2) entities of interest from an entity-relationship graph. Intuitively, these would be subgraphs that can explain the relations between the k entities of interest. The knowledge discovery tasks supported by MING have a stronger semantic flavor than the ones supported by STAR. STAR and MING are integrated into the query answering component of the NAGA engine. NAGA itself is a fully implemented prototype system and is part of the YAGONAGA project.Das Web birgt in sich das Potential zur umfangreichsten Wissensbasis der Welt zu werden. Das Organisieren der Information aus dem Web in Entity-Relationship-Graphstrukturen könnte ein erster Schritt sein, um dieses Potential zu entfalten. In einem zweiten Schritt müssten ausdrucksstarke Suchtechniken entwickelt werden, die über das heutige Keyword-basierte Suchparadigma hinausgehen und die inhärente Semantik solcher Strukturen ausnutzen. In diesem Rahmen stellen wir als ersten Beitrag dieser Arbeit NAGA (Not Another Google Answer) vor, eine neue semantische Suchmaschine. NAGA bietet eine ausdrucksstarke, graphbasierte Anfragesprache, die Anfragen mit Entitäten und Relationen ermöglicht. Die Ergebnisse werden durch Subgraph-Matching-Techniken gefunden und mithilfe eines statistischen Modells in eine Rangliste gebracht. Als zweiten Beitrag stellen wir STAR (Steiner Tree Approximation in Relationship Graphs) vor, eine effiziente Technik, um "nahe'; Relationen (d.h. kompakte Verbindungen) zwischen k(> 2) Entitäten in großen Entity-Relationship-Graphen zu finden. Unser dritter Beitrag ist MING (Mining Informative Graphs). MING ist eine effiziente Methode, die das Finden von "informativen'; Subgraphen für k(> 2) Entitäten aus einem Entity-Relationship-Graphen ermöglicht. Dies sind Subgraphen, die die Beziehungen zwischen den k Entitäten erklären können. Im Vergleich zu STAR unterstützt MING Aufgaben der Wissensexploration, die einen stärkeren semantischen Charakter haben. Sowohl STAR als auch MING sind in die Query-Answering-Komponente der NAGA-Suchmaschine integriert. NAGA selbst ist ein vollständig implementiertes Prototypsystem und Teil des YAGO-NAGA-Projekts

Was Suchmaschinen nicht können. Holistische Entitätssuche auf Web Daten

Mehr als 50% aller Web Suchanfragen sind entitätsbezogen. Benutzer suchen entweder nach Entitäten oder nach Entitätsinformationen. Dennoch solche Anfragen von Suchmaschinen nicht gut unterstützt. Aufbauend auf dem Konzept des semiotischen Dreiecks aus der kognitiven Psychologie, haben wir drei Anfragetypen zur Entitätssuche identifiziert: typbasierte Anfragen – Suche nach Entitäten eines gegebenen Typs, prototypbasierte Anfragen – Suche nach Entitäten mit bestimmten Eigenschaften, und instanzbasierte Anfragen – Suche nach Entitäten die ähnlich zu einer gegebene Entität sind. Für typbasierte Anfragen haben wir eine Methode entwickelt die query expansion mit einer self-supervised vocabulary learning Technik auf strukturierten und unstrukturierten Daten verbindet. Unser Ansatz liefert einen guten Kompromiss zwischen Precision und Recall. Für prototypbasierte Anfragen stellen wir ProSWIP vor. Dies ist ein eigenschaftsbasiertes System um Entitäten aus dem Web abzurufen. Da aber die Anzahl der Eigenschaften die durch die Benutzer bereitgestellt werden relativ klein sein kann, baut ProSWIP auf direkten Fragen und Benutzer Feedback um die Menge der Eigenschaften zu einer Menge welche die Intentionen der Benutzer korrekt erfasst zu erweitern. Unsere Experimente zeigen dass mit maximal vier Fragen eine perfekte Precision erreicht wird. In dem Fall von instanzbasierten Anfragen besteht die Schwierigkeit darin eine Anfrageform zu finden die die Benutzerintentionen eindeutig macht. Wir stellen eine minimalistische instanzbasierte Anfrage, die aus einem Beispiel und dem entsprechenden Entitätstypen besteht vor. Mit Hilfe des Konzepts der Familienähnlichkeit entwickeln wir eine praktische Lösung um Entitäten mit Bezug zur der Anfragenentität direkt aus dem Web abzurufen. Unser Ansatz erzielt sogar für Anfragen, die für standard Entitätssuchaufgaben wie related entity finding problematisch waren, gute Ergebnisse. Entitätszusammenfassung ist ein anderer Typ von entitätszentrischen Anfragen, der Informationen bezüglich einer Entität bereitstellt. Googles Knowledge Graph ist der Stand der Technik für solche Aufgaben. Aber das Zurückgreifen auf manuell erstellte Knowledgebases schließt weniger bekannten Entitäten für das Knowledge Graph aus. Wir schlagen daher vor datengetriebene Ansätze zu nutzen. Wir sind überzeugt dass das Bewältigen dieser vier Anfragetypen eine holistische Entitätssuche auf Web Daten für die nächste Generation von Suchmaschinen ermöglicht.More than 50% of all Web queries are entity related. Users search either for entities or for entity information. Still, search engines do not accommodate entity-centric search very well. Building on the concept of the semiotic triangle from cognitive psychology, which models entity types in terms of intensions and extensions, we identified three types of queries for retrieving entities: type-based queries - searching for entities of a given type, prototype-based queries - searching for entities having certain properties, and instance-based queries - searching for entities being similar to a given entity. For type-based queries we present a method that combines query expansion with a self-supervised vocabulary learning technique built on both structured and unstructured data. Our approach is able to achieve a good tradeoff between precision and recall. For prototype-based queries we propose ProSWIP, a property-based system for retrieving entities from the Web. Since the number of properties given by the users can be quite small, ProSWIP relies on direct questions and user feedback to expand the set of properties to a set that captures the user’s intentions correctly. Our experiments show that within a maximum of four questions the system achieves perfect precision of the selected entities. In the case of instance-based queries the first challenge is to establish a query form that allows for disambiguating user intentions without putting too much cognitive pressure on the user. We propose a minimalistic instance-based query comprising the example entity and intended entity type. With this query and building on the concept of family resemblance we present a practical way for retrieving entities directly from the Web. Our approach can even cope with queries which have proven problematic for benchmark tasks like related entity finding. Providing information about a given entity, entity summarization is another kind of entity-centric query. Google’s Knowledge Graph is the state of the art for this task. But relying entirely on manually curated knowledge bases, the Knowledge Graph does not include all new and less known entities. We propose to use a data-driven approach. Our experiments on real-world entities show the superiority of our method. We are confident that mastering these four query types enables holistic entity search on Web data for the next generation of search engines

From people to entities : typed search in the enterprise and the web

[no abstract

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches