Entity relatedness for retrospective analyses of global events

Tracking global events through time would ease many diachronic analyses which are currently carried out manually by social scientists. While entity linking algorithms can be adapted to track events that go by a common name, such a name is often not established in early stages leading up to the event. This study evaluates the utility of entity relatedness for the task of identifying related entities and textual resources that describe the involvement of the entity in the event. In a small study we find that simple relatedness methods obtain MAP score of 0.74 outperforming many advanced baseline systems such as Stics and Wiki2Vec. A small adaptation of this method provides sufficient explanations of entity involvement or 68% of relevant entities

TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and other info available at http://cs.stanford.edu/~althoff/timemachine

Building Entity-Centric Event Collections

Web archives preserve an unprecedented abundance of materials regarding major events and transformations in our society. In this paper, we present an approach for building event-centric sub-collections from such large archives, which includes not only the core documents related to the event itself but, even more importantly, documents describing related aspects (e.g., premises and consequences). This is achieved by 1) identifying relevant concepts and entities from a knowledge base, and 2) detecting their mentions in documents, which are interpreted as indicators for relevance. We extensively evaluate our system on two diachronic corpora, the New York Times Corpus and the US Congressional Record, and we test its performance on the TREC KBA Stream corpus, a large and publicly available web archive

yourHistory – Semantic linking for a personalized timeline of historic events

In this paper we present yourHistory: a Facebook application that aims to generate a tailor-made, personalized timeline of historic events, by matching a semantically enriched Facebook profile to a pool of candidate historic events extracted from DBPedia. Two aspects are central to our application: (i) semantic linking technologies backed by rich open web knowledge bases for generating semantically enriched user profiles, and (ii) semantic relatedness metrics for ranking historic events to user profiles. This paper describes the development of a Facebook application that aims to be engaging for users, whilst at the same time being a source for data that can be applied to evaluating or improving the application. We describe our Wikipedia-based semantic relatedness metric for event ranking, but also the restrictions and constraints concerning privacy-sensitive and ethical matters, around data storage and user consent. Finally, we reflect on how this type of user data can be applied for evaluating or improving both the semantic linking and event ranking methods in future work