Most existing relation extraction models make predictions for each entity pair locally and individually, while ignoring implicit global clues available in the knowledge base , sometimes leading to conflicts among local predictions from different entity pairs.

And, we find that the clues learnt automatically from existing knowledge bases perform comparably to those refined by human.

It uses Freebase as the knowledge base and New York Time corpus as the text corpus, including about 60,000 entity tuples in the training set, and about 90,000 entity tuples in the testing set.

Identifying predefined kinds of relationship between pairs of entities is crucial for many knowledge base related applications(Suchanek et al., 2013).

Many knowledge bases do not have a well-defined typing system, let alone fine-grained typing taxonomies with corresponding type recognizers, which are crucial to explicitly model the typing requirements for arguments of a relation, but rather expensive and time-consuming to collect.

We propose to perform joint inference upon multiple local predictions by leveraging implicit clues that are encoded with relation specific requirements and can be learnt from existing knowledge bases .

Their approach only captures relation dependencies, while we learn implicit relation backgrounds from knowledge bases , including argument type and cardinality requirements.

The clues of detecting these inconsistencies can be learnt from a knowledge base .

As discussed earlier, we will exploit from the knowledge base two categories of clues that implicitly capture relations’ backgrounds: their expected argument types and argument cardinalities, based on which we can discover two categories of disagreements among the candidate predictions, summarized as argument type inconsistencies and Violations of arguments’ uniqueness, which have been rarely considered before.

Most existing knowledge bases represent their knowledge facts in the form of (<subject, relation, 0bject>) triple, which can be seen as relational facts between entity tuples.

Large-scale knowledge bases are important assets in NLP.

We propose a cost-effective method of validating and extending knowledge bases using video games with a purpose.

Large-scale knowledge bases are an essential component of many approaches in Natural Language Processing (NLP).

Semantic knowledge bases such as WordNet (Fellbaum, 1998), YAGO (Suchanek et al., 2007), and BabelNet (Navigli and Ponzetto, 2010) provide ontological structure that enables a wide range of tasks, such as measuring semantic relatedness (Budanitsky and Hirst, 2006) and similarity (Pilehvar et al., 2013), paraphrasing (Kauchak and Barzilay, 2006), and word sense disambiguation (Navigli and Ponzetto, 2012; Moro et al., 2014).

Furthermore, such knowledge bases are essential for building unsupervised algorithms when training data is sparse or unavailable.

Last, three two-player games have focused on validating and extending knowledge bases .

We present an approach to training a joint syntactic and semantic parser that combines syntactic training information from CCGbank with semantic training information from a knowledge base via distant supervision.

We suggest that a large populated knowledge base should play a key role in syntactic and semantic parsing: in training the parser, in resolving syntactic ambiguities when the trained parser is applied to new text, and in its output semantic representation.

Using semantic information from the knowledge base at training and test time will

A semantic representation tied to a knowledge base allows for powerful inference operations — such as identifying the possible entity referents of a noun phrase — that cannot be performed with shallower representations (e.g., frame semantics (Baker et al., 1998) or a direct conversion of syntax to logic (B08, 2005)).

These logical forms are constructed using category and relation predicates from a broad coverage knowledge base .

3.1 Knowledge Base

However, these approaches to semantics do not ground the text to beliefs in a knowledge base .

Finally, some work has looked at applying semantic parsing to answer queries against large knowledge bases , such as YAGO (Yahya et al., 2012) and Freebase (Cai and Yates, 2013b; Cai and Yates, 2013a; Kwiatkowski et al., 2013; Be-rant et al., 2013).

We present a simple, data-driven approach to generation from knowledge bases (KB).

Using the KBGen benchmark, we then showed that the resulting induced FB-LTAG compares favorably with competing symbolic and statistical approaches when used to generate from knowledge base data.

In this paper we present a grammar based approach for generating from knowledge bases (KB) which is linguistically principled and conceptually simple.

To evaluate our approach, we use the benchmark provided by the KBGen challenge (Banik et al., 2012; Banik et al., 2013), a challenge designed to evaluate generation from knowledge bases ; where the input is a KB subset; and where the expected output is a complex sentence conveying the meaning represented by the input.

With the development of the semantic web and the proliferation of knowledge bases, generation from knowledge bases has attracted increased interest and so called ontology verbalisers have been proposed which support the generation of text from (parts of) knowledge bases .

strand of work maps each axiom in the knowledge base to a clause.

The MIAKT project (Bontcheva and Wilks., 2004) and the ONTOGENERATION project (Aguado et al., 1998) use symbolic NLG techniques to produce textual descriptions from some semantic information contained in a knowledge base .

Specifically, the task is to verbalise a subset of a knowledge base .

The KB subsets forming the KB Gen input data were preselected from the AURA biology knowledge base (Gunning et al., 2010), a knowledge base about biology which was manually encoded by biology teachers and encodes knowledge about events, entities, properties and relations where relations include event-to-entity, event-to-event,

We apply the new model to construct a relation knowledge base (KB), and use it as a complement to the existing manually created KBs.

5.2 Knowledge Base (KB) Construction

Further, the medical knowledge is changing extremely quickly, making people hard to understand it, and update it in the knowledge base in a timely manner.

Table 3: Knowledge Base Comparison Recall@20 Recall@50 Recall@3000 Our KB 135/742 182/742 301/742

To achieve this, we parsed all 80M sentences in our medical corpus, looking for the sentences containing the terms that are associated with the CUI pairs in the knowledge base .

For example, we know from the knowledge base that “antibiotic drug” may treat “Lyme disease”.

In candidate answer generation, relations enable the background knowledge base to be used for potential candidate

We also apply our model to build a new medical relation knowledge base as a complement to the existing knowledge bases .

A central challenge in semantic parsing is handling the myriad ways in which knowledge base predicates can be expressed.

Traditionally, semantic parsers are trained primarily from text paired with knowledge base information.

Our goal is to exploit the much larger amounts of raw text not tied to any knowledge base .

We consider the semantic parsing problem of mapping natural language utterances into logical forms to be executed on a knowledge base (KB) (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Kwiatkowski et al., 2010).

Scaling semantic parsers to large knowledge bases has attracted substantial attention recently (Cai and Yates, 2013; Berant et al., 2013; Kwiatkowski et al., 2013), since it drives applications such as question answering (QA) and information extraction (IE).

Our task is as follows: Given (i) a knowledge base IC, and (ii) a training set of question-answer pairs 3/1)};1, output a semantic parser that maps new questions at to answers 3/ via latent logical forms 2.

A knowledge base IC is a set of assertions (61,19, 62) E 5 X ’P X 5 (e.g., (BillGates,PlaceOfBirth, Seattle».

m knowledge bases 2.

The intuition of the paradigm is that one can take advantage of several knowledge bases , such as WordNet3, Freebase4 and YAGO5, to automatically label free texts, like Wikipedia6 and New York Times corpora7, based on some heuristic alignment assumptions.

>) are not only involved in the relation instances8 coming from knowledge bases (President—of(Barack Obama, U.S.) and Born—in (Barack Obama, U .

(2004) used WordNet as the knowledge base to discover more h-pyernym/hyponym relations between entities from news articles.

(2009) adopted Freebase (Bollacker et al., 2008; Bollacker et al., 2007), a large-scale crowdsourcing knowledge base online which contains billions of relation instances and thousands of relation names, to distantly supervise Wikipedia corpus.

(2012) proposed a novel approach to multi-instance multi-label learning for relation extraction, which jointly modeled all the sentences in texts and all labels in knowledge bases for a given entity pair.

A typical knowledge-based question answering (KB-QA) system faces two challenges: one is to transform natural language questions into their meaning representations (MRs); the other is to retrieve answers from knowledge bases (KBs) using generated MRs.

Knowledge-based question answering (KB-QA) computes answers to natural language (NL) questions based on existing knowledge bases (KBs).

Compared to their work, our method gains an improvement in two aspects: (1) Instead of using facts extracted using the open IE method, we leverage a large scale, high-quality knowledge base ; (2) We can handle multiple-relation questions, instead of single-relation queries only, based on our translation based KB-QA framework.

(2013) is one of the latest work which has reported QA results based on a large scale, general domain knowledge base (Freebase), we consider their evaluation result on WEBQUESTIONS as our baseline.

In particular, MetaMap is used to map terms from queries and documents (e.g., medical records) to the semantic concepts from biomedical knowledge bases such as UMLS.

Second, we will study how to leverage other information from knowledge bases to further improve the performance.

In the past decades, significant efforts have been put on constructing biomedical knowledge bases (Aronson and Lang, 2010; Lipscomb, 2000; Corporation, 1999) and developing natural language processing (NLP) tools, such as MetaMap, to utilize the information from the knowledge bases (Aronson, 2001; McInnes et al., 2009).

Indeed, concept-based representation is one of the commonly used approaches that leverage knowledge bases to improve the retrieval performance (Limsopatham et al., 2013d; Limsopatham et al., 2013b).

The basic idea is to represent both queries and documents as “bags of concepts”, where the concepts are identified based on the information from the knowledge bases .

Wikification for tweets aims to automatically identify each concept mention in a tweet and link it to a concept referent in a knowledge base (e.g., Wikipedia).

We use a Wikipedia dump on May 3, 2013 as our knowledge base , which includes 30 million pages.

concept referent in a knowledge base (KB) (e.g., Wikipedia).

Knowledge Base (Wikipedia)

The task of linking concept mentions to a knowledge base has received increased attentions over the past several years, from the linking of concept mentions in a single text (Mihalcea and Csomai, 2007; Milne and Witten, 2008b; Milne and Witten, 2008a; Kulkami et al., 2009; He et al., 2011; Ratinov et al., 2011; Cassidy et al., 2012; Cheng and Roth, 2013), to the linking of a cluster of corefer-

The triple format is the most common representation of facts in knowledge bases .

NELL’s entity typing method has high recall because when entities are not in the knowledge base , it performs on-the-fly type inference using the Web.

We evaluated FactChecker on three datasets: i) KB Fact Candidates: The first dataset consists of fact candidates taken from the fact extraction pipeline of a state-of-the-art knowledge base , NELL (Carlson et al., 2010).

ii) Wikipedia Fact Candidates: For the second dataset, we did not restrict the fact candidates to specific topics from a knowledge base , instead we aimed to evaluate all fact candidates about a given entity.

These projects have produced knowledge bases containing many millions of relational facts between entities.

The set of generated inference rules can be regarded as the knowledge base KB.

o PSL-no-DIR: Our PSL system without distributional inference rules(empty knowledge base ).

Given the logical forms for a pair of sentences, a text T and a hypothesis H, and given a set of weighted rules derived from the distributional semantics (as explained in section 2.6) composing the knowledge base KB, we build a PSL model that supports determining the truth value of H in the most probable interpretation (i.e.

KB: The knowledge base is a set of lexical and phrasal rules generated from distributional semantics, along with a similarity score for each rule (section 2.6).

Distant supervision usually utilizes only unlabeled data and existing knowledge bases to learn relation extraction models.

Our goal is to jointly model human-labeled ground truth and structured data from a knowledge base in distant supervision.

It automatically labels its own training data by heuristically aligning a knowledge base of facts with an unlabeled corpus.

Table 1: Classic errors in the training data generated by a toy knowledge base of only one entry personTit|e(Abu Zubaydah, leader).

Facebook would an ideal ground truth knowledge base .

Inspired by the concept of distant supervision, we collect training tweets by matching attribute ground truth from an outside “knowledge base” such as Facebook or Google Plus.

Lists of universities and companies are taken from knowledge base NELLB.

Figure 1: Illustration of Goolge Plus “knowledge base” .

The creation and use of machine-readable knowledge has not only entailed researchers (Mitchell, 2005; Mirkin et al., 2009; Poon et al., 2010) developing huge, broad-coverage knowledge bases (Hovy et al., 2013; Suchanek and Weikum, 2013), but it has also hit big industry players such as Google (Singhal, 2012) and IBM (Ferrucci, 2012), which are moving fast towards large-scale knowledge-oriented systems.

The creation of very large knowledge bases has been made possible by the availability of collaboratively-curated online resources such as Wikipedia and Wiktionary.

A second project, MENTA (de Melo and Weikum, 2010), creates one of the largest multilingual lexical knowledge bases by interconnecting more than 13M articles in 271 languages.

A good knowledge base should have the capacity of handling this ambiguity.

Such patterns compose our knowledge base as shown below.

As the knowledge is extracted from each cluster individually, we represent our knowledge base as a set of clusters, where each cluster consists of a set of frequent 2-patterns mined using FPM, e. g.,

The reference knowledge base is derived from an October 2008 dump of English Wikipedia, which includes 818,741 nodes.

The Entity Linking (EL) task consists in linking name mentions of named entities (NEs) found in a document to their corresponding entities in a reference Knowledge Base (KB).

It relies on the Wikipedia-derived YAGO2 (Hoffart et al., 2011a) knowledge base .

Answering natural language questions using the Freebase knowledge base has recently been explored as a platform for advancing the state of the art in open domain semantic parsing.

Those efforts map questions to sophisticated meaning representations that are then attempted to be matched against Viable answer candidates in the knowledge base .

Question answering (QA) from a knowledge base (KB) has a long history within natural language processing, going back to the 1960s and 1970s, with systems such as Baseball (Green Jr et al., 1961) and Lunar (Woods, 1977).