We leverage distant supervision using relations from the knowledge base FreeBase, but do not require any manual heuristic nor manual seed list selections.

The detection of relations between entities for the automatic population of knowledge bases is very useful for solving tasks such as Entity Disambiguation, Information Retrieval and Question Answering.

The availability of high-coverage, general-purpose knowledge bases enable the automatic identification and disambiguation of entities in text and its applications (Bunescu and Pasca, 2006; Cucerzan, 2007; McNamee and Dang, 2009; Kwok et al., 2001; Pasca et al., 2006; Weld et al., 2008; Pereira et al., 2009; Kasneci et al., 2009).

These systems do not need any manual data or rules, but the relational facts they extract are not immediately disambiguated to entities and relations from a knowledge base .

Similar to other distant supervision methods, our approach takes as input an existing knowledge base containing entities and relations, and a textual corpus.

In this work it is not necessary for the corpus to be related to the knowledge base .

In what follows we assume that all the relations studied are binary and hold between exactly two entities in the knowledge base .

In relation extraction, distant supervision seeks to extract relations between entities from text by using a knowledge base , such as Freebase, as a source of supervision.

When a sentence and a knowledge base refer to the same entity pair, this approach heuristically labels the sentence with the corresponding relation in the knowledge base .

A particularly attractive approach, called distant supervision (DS), creates labeled data by heuristically aligning entities in text with those in a knowledge base , such as Freebase (Mintz et al., 2009).

With DS it is assumed that if a sentence contains an entity pair in a knowledge base, such a sentence actually expresses the corresponding relation in the knowledge base .

DS uses a knowledge base to create labeled data for relation extraction by heuristically matching entity pairs.

The increasingly popular approach, called distant supervision (DS), or weak supervision, utilizes a knowledge base to heuristically label a corpus (Wu and Weld, 2007; Bellare and McCallum, 2007; Pal

(2009) who used Freebase as a knowledge base by making the DS assumption and trained relation extractors on Wikipedia.

Although compiling time-aware knowledge bases is an important open challenge (Weikum et al., 2011), it has remained unexplored until very recently (Wang et al., 2011; Talukdar et al., 2012).

We have also studied the limits of the distant supervision approach to relation extraction, showing empirically that its performance depends not only on the nature of reference knowledge base and document corpus (Riedel et al., 2010), but also on the relation to be extracted.

From a reference Knowledge Base (KB), we extract a set of relation triples or seeds: (entity,relati0n,value), where the relation is one of the target relations.

It has been shown that this assumption is more often violated when training knowledge base and document collection are of different type, e. g. Wikipedia and newswire (Riedel et al., 2010).

Compiling a Knowledge Base of temporally anchored facts is an open research challenge (Weikum et al., 2011).

There have been attempts to extend an existing knowledge base .

While ACE required only to identify time expressions and classify their relation to events, KBP requires to infer explicitly the start/end time of relations, which is a realistic approach in the context of building time-aware knowledge bases .

Given a knowledge base as a BLP, standard logical inference (SLD resolution) is used to automatically construct a Bayes net for a given problem.

The final knowledge base included all unique rules learned from any subset.

Since manually developing such a knowledge base is difficult and arduous, an effective alternative is to automatically learn such rules by mining a substantial database of facts that an IE system has already automatically extracted from a large corpus of text (Nahm and Mooney, 2000).

Typically, an ILP system takes a set of positive and negative instances for a target relation, along with a background knowledge base (in our case, other facts extracted from the same document) from which the positive instances are potentially inferable.

We apply the models to resolve entity types in new queries and to assign prior type distributions over an existing knowledge base .

Our proposed models can be efficiently trained using an EM algorithm and can be further used to assign prior type distributions to entities in an existing knowledge base and to insert new entities into it.

Fitting to an existing Knowledge Base: Although in general our model decodes type distributions for arbitrary entities, in many practical cases it is beneficial to constrain the types to those admissible in a fixed knowledge base (such as Freebase).