Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license 1.
Reasoning with temporal information in knowledge bases has a long history and has resulted in numerous temporal logics @cite_15 . Several recent approaches extend statistical relational learning frameworks with temporal reasoning capabilities @cite_8 @cite_5 @cite_3 .
@cite_5 @cite_15 @cite_3 @cite_8
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Recent research has made significant advances in automatically constructing knowledge bases by extracting relational facts (e.g., Bill Clinton-presidentOf-US) from large text corpora. Temporally scoping such relational facts in the knowledge base (i.e., determining that Bill Clinton-presidentOf-US is true only during the period 1993 - 2001) is an important, but relatively unexplored problem. In this paper, we propose a joint inference framework for this task, which leverages fact-specific temporal constraints, and weak supervision in the form of a few labeled examples. Our proposed framework, CoTS (Coupled Temporal Scoping), exploits temporal containment, alignment, succession, and mutual exclusion constraints among facts from within and across relations. Our contribution is multi-fold. Firstly, while most previous research has focused on micro-reading approaches for temporal scoping, we pose it in a macro-reading fashion, as a change detection in a time series of facts' features computed from a large number of documents. Secondly, to the best of our knowledge, there is no other work that has used joint inference for temporal scoping. We show that joint inference is effective compared to doing temporal scoping of individual facts independently. We conduct our experiments on large scale open-domain publicly available time-stamped datasets, such as English Gigaword Corpus and Google Books Ngrams, demonstrating CoTS's effectiveness. Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases. In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base. We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90 for extraction from semi-structured data and almost 70 for extraction from text. Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporal-probabilistic database model for cleaning uncertain temporal facts obtained from information extraction methods. Specifically, we consider a combination of temporal deduction rules, temporal consistency constraints and probabilistic inference based on the common possible-worlds semantics with data lineage, and we study the theoretical properties of this data model. We further develop a query engine which is capable of scaling to very large temporal knowledge bases, with nearly interactive query response times over millions of uncertain facts and hundreds of thousands of grounded rules. Our experiments over two real-world datasets demonstrate the increased robustness of our approach compared to related techniques based on constraint solving via Integer Linear Programming (ILP) and probabilistic inference via Markov Logic Networks (MLNs). We are also able to show that our runtime performance is more than competitive to current ILP solvers and the fastest available, probabilistic but non-temporal, database engines. A probabilistic temporal knowledge base contains facts that are annotated with a time interval and a confidence score. The interval defines the time span for which it can be assumed that the fact is true with a probability that is expressed by the confidence score. Given a probabilistic temporal knowledge base, we propose the use of Markov Logic in combination with Allen’s interval calculus to select the most probable consistent subset of facts by computing the MAP state. We apply our approach on a specific domain of DBpedia, namely the domain of academics. We simulate a scenario of extending a knowledge base automatically in an open setting by adding erroneous facts to the facts stated in DBpedia. Our results in- dicate that we can eliminate a large fraction of these errors without removing too many correctly stated facts.