Information extraction traditionally focuses on extracting relations between identifiable entities, such as . Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60 average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28 more than the existing Wikidata facts for these relations.
Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1 20 93 - 1 20 01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches. In this paper, we describe HeidelTime, a system for the extraction and normalization of temporal expressions. HeidelTime is a rule-based system mainly using regular expression patterns for the extraction of temporal expressions and knowledge resources as well as linguistic clues for their normalization. In the TempEval-2 challenge, HeidelTime achieved the highest F-Score (86 ) for the extraction and the best results in assigning the correct value attribute, i.e., in understanding the semantics of the temporal expressions. HTML tables and spreadsheets on the Internet or in enterprise intranets often contain valuable information, but are created ad-hoc. As a result, they usually lack systematic names for column headers and clear vocabulary for cell values. This limits the re-use of such tables and creates a huge heterogeneity problem when comparing or aggregating multiple tables. This paper aims to overcome this problem by automatically canonicalizing header names and cell values onto concepts, classes, entities and uniquely represented quantities registered in a knowledge base. To this end, we devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities and quantities. We give specific consideration to quantities which are mapped into a "measure, value, unit" triple over a taxonomy of physical (e.g. power consumption), monetary (e.g. revenue), temporal (e.g. date) and dimensionless (e.g. counts) measures. Our experiments with Web tables from diverse domains demonstrate the viability of our method and its benefits over baselines.  We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity (e.g., atomic number(Aluminium, 13), inflation rate(India, 10.9 )). This task presents peculiar challenges not found in standard Information Extraction (IE), such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.  With the success of Open Data a huge amount of tabular data sources became available that could potentially be mapped and linked into the Web of (Linked) Data. Most existing approaches to “semantically label” such tabular data rely on mappings of textual information to classes, properties, or instances in RDF knowledge bases in order to link – and eventually transform – tabular data into RDF. However, as we will illustrate, Open Data tables typically contain a large portion of numerical columns and or non-textual headers; therefore solutions that solely focus on textual “cues” are only partially applicable for mapping such data sources. We propose an approach to find and rank candidates of semantic labels and context descriptions for a given bag of numerical values. To this end, we apply a hierarchical clustering over information taken from DBpedia to build a background knowledge graph of possible “semantic contexts” for bags of numerical values, over which we perform a nearest neighbour search to rank the most likely candidates. Our evaluation shows that our approach can assign fine-grained semantic labels, when there is enough supporting evidence in the background knowledge graph. In other cases, our approach can nevertheless assign high level contexts to the data, which could potentially be used in combination with other approaches to narrow down the search space of possible labels.