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

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

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

Analyzing, Mining, and Predicting Networked Behaviors

Network structure exists in various types of data in the real world, such as online and offline social networks, traffic networks, computer networks, brain networks, and countless other cases where there are relationships between different entities in the data. What are the roles of network structures in these data? First, the network captures inherent characteristics of the data themselves. This is clear from the definition of the network, which represents the relationship between entities: e.g., the social links among people in a social network describe how they interact with each other; a road network summarizes how the roads are laid out geographically; a brain network obtained from fMRI images represents pairs of brain regions that are active at the same time; a computer network constrains the paths via which internet packages and thus information or viruses can spread. Second, the network structures affect the evolution of the data over time. For example, new friendship links in an online social network are frequently created between friends of friends. Similarly, the current road network structure is without a doubt taken into consideration when roads are added or temporarily closed. As we grow, our brains also grow, including the additions of useful links or the clean up of unnecessary links between brain regions. Third, the network structures act as guidance for many different processes happening in the data. For instance, the links between users on social network dictate how gossips can spread; the roads influence how traffic flows in a city; the links between brain regions affects the way we think and how effectively we do things; the connections between computers route the transfer of any information on the internet.In this thesis, I studied the network effect in various networked behaviors, including analyzing such effect, finding its patterns, and predicting future networked behaviors. First, I gained insights into the data by analyzing the accompanied network structures as well as its evolution. Second, I proposed algorithms for mining different network patterns that help summarize the effect of the network structures on different networked behaviors. Finally, I proposed models to predict the evolution of networked behaviors over time. Toward these tasks, I explored a wide variety of network data, including protein-protein interaction networks, online social networks, collaboration networks, chemical compounds, and traffic networks. Overall, I tackled these network data in different aspects and developed a number of methods for effectively mining and forecasting networked behaviors in data