Diversifying Search Results Using Time

Getting an overview of a historic entity or event can be difficult in search results, especially if important dates concerning the entity or event are not known beforehand. For such information needs, users would benefit if returned results covered diverse dates, thus giving an overview of what has happened throughout history. Diversifying search results based on important dates can be a building block for applications, for instance, in digital humanities. Historians would thus be able to quickly explore longitudinal document collections by querying for entities or events without knowing associated important dates apriori. In this work, we describe an approach to diversify search results using temporal expressions (e.g., in the 1990s) from their contents. Our approach first identifies time intervals of interest to the given keyword query based on pseudo-relevant documents. It then re-ranks query results so as to maximize the coverage of identified time intervals. We present a novel and objective evaluation for our proposed approach. We test the effectiveness of our methods on the New York Times Annotated corpus and the Living Knowledge corpus, collectively consisting of around 6 million documents. Using history-oriented queries and encyclopedic resources we show that our method indeed is able to present search results diversified along time

Populating knowledge bases with temporal information

Recent progress in information extraction has enabled the automatic construction of large knowledge bases. Knowledge bases contain millions of entities (e.g. persons, organizations, events, etc.), their semantic classes, and facts about them. Knowledge bases have become a great asset for semantic search, entity linking, deep analytics, and question answering. However, a common limitation of current knowledge bases is the poor coverage of temporal knowledge. First of all, so far, knowledge bases have focused on popular events and ignored long tail events such as political scandals, local festivals, or protests. Secondly, they do not cover the textual phrases denoting events and temporal facts at all. The goal of this dissertation, thus, is to automatically populate knowledge bases with this kind of temporal knowledge. The dissertation makes the following contributions to address the afore mentioned limitations. The first contribution is a method for extracting events from news articles. The method reconciles the extracted events into canonicalized representations and organizes them into fine-grained semantic classes. The second contribution is a method for mining the textual phrases denoting the events and facts. The method infers the temporal scopes of these phrases and maps them to a knowledge base. Our experimental evaluations demonstrate that our methods yield high quality output compared to state-of- the-art approaches, and can indeed populate knowledge bases with temporal knowledge.Der Fortschritt in der Informationsextraktion ermöglicht heute das automatischen Erstellen von Wissensbasen. Derartige Wissensbasen enthalten Entitäten wie Personen, Organisationen oder Events sowie Informationen über diese und deren semantische Klasse. Automatisch generierte Wissensbasen bilden eine wesentliche Grundlage für das semantische Suchen, das Verknüpfen von Entitäten, die Textanalyse und für natürlichsprachliche Frage-Antwortsysteme. Eine Schwäche aktueller Wissensbasen ist jedoch die unzureichende Erfassung von temporalen Informationen. Wissenbasen fokussieren in erster Linie auf populäre Events und ignorieren weniger bekannnte Events wie z.B. politische Skandale, lokale Veranstaltungen oder Demonstrationen. Zudem werden Textphrasen zur Bezeichung von Events und temporalen Fakten nicht erfasst. Ziel der vorliegenden Arbeit ist es, Methoden zu entwickeln, die temporales Wissen au- tomatisch in Wissensbasen integrieren. Dazu leistet die Dissertation folgende Beiträge: 1. Die Entwicklung einer Methode zur Extrahierung von Events aus Nachrichtenartikeln sowie deren Darstellung in einer kanonischen Form und ihrer Einordnung in detaillierte semantische Klassen. 2. Die Entwicklung einer Methode zur Gewinnung von Textphrasen, die Events und Fakten in Wissensbasen bezeichnen sowie einer Methode zur Ableitung ihres zeitlichen Verlaufs und ihrer Dauer. Unsere Experimente belegen, dass die von uns entwickelten Methoden zu qualitativ deutlich besseren Ausgabewerten führen als bisherige Verfahren und Wissensbasen tatsächlich um temporales Wissen erweitern können

Implicit Entity Networks: A Versatile Document Model

The time in which we live is often referred to as the Information Age. However, it can also aptly be characterized as an age of constant information overload. Nowhere is this more present than on the Web, which serves as an endless source of news articles, blog posts, and social media messages. Of course, this overload is even greater in professions that handle the creation or extraction of information and knowledge, such as journalists, lawyers, researchers, clerks, or medical professionals. The volume of available documents and the interconnectedness of their contents are both a blessing and a curse for the contemporary information consumer. On the one hand, they provide near limitless information, but on the other hand, their consumption and comprehension requires an amount of time that many of us cannot spare. As a result, automated extraction, aggregation, and summarization techniques have risen in popularity, even though they are a long way from being comprehensive. When we, as humans, are faced with an overload of information, we tend to look for patterns that bring order into the chaos. In news, we might identify familiar political figures or celebrities, whereas we might look for expressive symptoms in medicine, or precedential cases in law. In other words, we look for known entities as reference points, and then explore the content along the lines of their relations to others entities. Unfortunately, this approach is not reflected in current document models, which do not provide a similar focus on entities. As a direct result, the retrieval of entity-centric knowledge and relations from a flood of textual information becomes more difficult than it has to be, and the inclusion of external knowledge sources is impeded. In this thesis, we introduce implicit entity networks as a comprehensive document model that addresses this shortcoming and provides a holistic representation of document collections and document streams. Based on the premise of modelling the cooccurrence relations between terms and entities as first-class citizens, we investigate how the resulting network structure facilitates efficient and effective entity-centric search, and demonstrate the extraction of complex entity relations, as well as their summarization. We show that the implicit network model is fully compatible with dynamic streams of documents. Furthermore, we introduce document aggregation methods that are sensitive to the context of entity mentions, and can be used to distinguish between different entity relations. Beyond the relations of individual entities, we introduce network topics as a novel and scalable method for the extraction of topics from collections and streams of documents. Finally, we combine the insights gained from these applications in a versatile hypergraph document model that bridges the gap between unstructured text and structured knowledge sources

Leveraging Semantic Annotations for Event-focused Search & Summarization

Today in this Big Data era, overwhelming amounts of textual information across different sources with a high degree of redundancy has made it hard for a consumer to retrospect on past events. A plausible solution is to link semantically similar information contained across the different sources to enforce a structure thereby providing multiple access paths to relevant information. Keeping this larger goal in view, this work uses Wikipedia and online news articles as two prominent yet disparate information sources to address the following three problems: • We address a linking problem to connect Wikipedia excerpts to news articles by casting it into an IR task. Our novel approach integrates time, geolocations, and entities with text to identify relevant documents that can be linked to a given excerpt. • We address an unsupervised extractive multi-document summarization task to generate a fixed-length event digest that facilitates efficient consumption of information contained within a large set of documents. Our novel approach proposes an ILP for global inference across text, time, geolocations, and entities associated with the event. • To estimate temporal focus of short event descriptions, we present a semi-supervised approach that leverages redundancy within a longitudinal news collection to estimate accurate probabilistic time models. Extensive experimental evaluations demonstrate the effectiveness and viability of our proposed approaches towards achieving the larger goal.Im heutigen Big Data Zeitalters existieren überwältigende Mengen an Textinformationen, die über mehrere Quellen verteilt sind und ein hohes Maß an Redundanz haben. Durch diese Gegebenheiten ist eine Retroperspektive auf vergangene Ereignisse für Konsumenten nur schwer möglich. Eine plausible Lösung ist die Verknüpfung semantisch ähnlicher, aber über mehrere Quellen verteilter Informationen, um dadurch eine Struktur zu erzwingen, die mehrere Zugriffspfade auf relevante Informationen, bietet. Vor diesem Hintergrund benutzt diese Dissertation Wikipedia und Onlinenachrichten als zwei prominente, aber dennoch grundverschiedene Informationsquellen, um die folgenden drei Probleme anzusprechen: • Wir adressieren ein Verknüpfungsproblem, um Wikipedia-Auszüge mit Nachrichtenartikeln zu verbinden und das Problem in eine Information-Retrieval-Aufgabe umzuwandeln. Unser neuartiger Ansatz integriert Zeit- und Geobezüge sowie Entitäten mit Text, um relevante Dokumente, die mit einem gegebenen Auszug verknüpft werden können, zu identifizieren. • Wir befassen uns mit einer unüberwachten Extraktionsmethode zur automatischen Zusammenfassung von Texten aus mehreren Dokumenten um Ereigniszusammenfassungen mit fester Länge zu generieren, was eine effiziente Aufnahme von Informationen aus großen Dokumentenmassen ermöglicht. Unser neuartiger Ansatz schlägt eine ganzzahlige lineare Optimierungslösung vor, die globale Inferenzen über Text, Zeit, Geolokationen und mit Ereignis-verbundenen Entitäten zieht. • Um den zeitlichen Fokus kurzer Ereignisbeschreibungen abzuschätzen, stellen wir einen semi-überwachten Ansatz vor, der die Redundanz innerhalb einer langzeitigen Dokumentensammlung ausnutzt, um genaue probabilistische Zeitmodelle abzuschätzen. Umfangreiche experimentelle Auswertungen zeigen die Wirksamkeit und Tragfähigkeit unserer vorgeschlagenen Ansätze zur Erreichung des größeren Ziels

Identifying Time Intervals of Interest to Queries

ABSTRACT We investigate how time intervals of interest to a query can be identified automatically based on pseudo-relevant documents, taking into account both their publication dates and temporal expressions from their contents. Our approach is based on a generative model and is able to determine time intervals at di↵erent temporal granularities (e.g., day, month, or year). We evaluate our approach on twenty years' worth of newspaper articles from The New York Times using two novel testbeds consisting of temporally unambiguous and temporally ambiguous queries, respectively