We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions . In our assumption , if two pairs of entities belong to the same relation , then those two pairs are analogous . Following this idea , we collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision . We leverage this dataset to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation . We evaluate our model in a one-shot learning task by showing a promising generalization capability in order to classify unseen relation types , which makes this approach suitable to perform automatic knowledge base population with minimal supervision . Moreover , the model can be used to generate pretrained embeddings which provide a valuable signal when integrated into an existing neuralbased model by outperforming the state-ofthe-art methods on a downstream relation extraction task . The task of identifying semantic relationships between entities in unstructured textual corpora , namely Relation Extraction ( RE ) , is often a prerequisite for many other natural language understanding tasks , e.g. automatic knowledge base population , question answering , etc . RE is commonly addressed as a classification task ( Bunescu et al . , 2005 ) , where a model is trained to classify relation mentions in text among a predefined set of relation types . For instance , given the sentence " Robert Plant is the singer of the band Led Zeppelin " , an effective RE system might extract the triple memberOf(ROBERT PLANT , LED ZEP-PELIN ) , where memberOf is a relation label expressed by the linguistic context " is the singer of the band " . Since a given relation can be expressed using different textual patterns surrounding entities , the state-of-the-art RE models which follow this approach need a considerable amount of examples for each relation to reach satisfactory performance . Distant supervision ( Mintz et al . , 2009 ) instead uses training examples from a knowledge base , guaranteeing a large amount of ( popular ) relation examples without human intervention , which can be used effectively by neural networks ( Lin et al . , 2016 ; Glass et al . , 2018 ) . However , even with this technique , approaching RE as a classification task presents several limitations : ( 1 ) distant supervision models are not accurate in extracting relations with a long-tailed distribution , because they typically have a small set of instances in knowledge bases ; ( 2 ) in most domains , relation types are very specific and only a few examples of each relation are available ; ( 3 ) these models can not be applied to recognize new relation types not observed during training . In this paper , we address RE from a different perspective by reducing it to an analogy problem . Our assumption states that if two pairs of entities , ( A , B ) and ( C , D ) , have at least one relation in common r , then those two pairs are analogous . Viceversa , solving proportional analogies , such as A : B = C : D , consists of identifying the implicit relations shared between two pairs of entities . For example , ROME : ITALY = PARIS : FRANCE is a valid analogy because capitalOf is a relation in common . Based on this idea , we propose an end-to-end neural model able to measure the degree of analogical similarity between two entity pairs , instead of predicting a confidence score for each relation type . An entity pair is represented through its mentions in a textual corpus , sequences of sentences where entities in the pair co-occur . If a mention represents a specific relation type , then this relationship is expressed by the linguistic context surrounding the two entities . E.g. , " Rome is the capital of Italy " or " The capital of France is Paris " referring to the example above . Thus , given two analogous entity pairs represented by their textual mentions sets as input , the model is trained to minimize the difference between the representations of relations having the same linguistic patterns . In other words , the model learns the different paraphrases expressing the same relation . In our research hypothesis , a model trained in such way is able to recognize analogies between unseen entity pairs belonging to new unseen relation types by : ( 1 ) generalizing over the sequence of words in the mentions ; ( 2 ) projecting the sequence of words in the mentions into a vector space representing relational semantics . This approach poses several research questions : ( RQ1 ) How to collect and organize a dataset for training ? ( RQ2 ) What kinds of models are effective for this task ? ( RQ3 ) How should the model be evaluated ? Knowledge bases , such as Wikidata or DBpedia , consist of large relational data sources organized in the form of triples , predicate(SUBJECT , OBJECT ) . We exploit this information to build a reliable set of analogous facts used as ground truth . Then , we adopt distant supervision to retrieve relation mentions in web-scale textual corpora by matching the subject-object entities which co-occur in the same sentences ( Riedel et al . , 2010 ; ElSahar et al . , 2018 ; Glass and Gliozzo , 2018a ) . Through this technique we can train our model on millions of analogy examples without human supervision . Since our goal is to train a model able to compute the relational similarity given two sets of textual mentions , we use siamese networks to learn discriminative features between those two instances ( Hadsell et al . , 2006 ) . This kind of neural network has been used in both computer vision ( Koch et al . , 2015 ) and natural language processing ( Mueller and Thyagarajan , 2016 ; Neculoiu et al . , 2016 ) in order to map two similar instances close in a feature space . However , in our setting each instance consists of a set of mentions , therefore it is inherently a multi-instance learning task 1 . We propose a hierarchical siamese network 1 Due to the weak supervision , the whole set of mentions with an attention mechanism at both word level ( Yang et al . , 2016 ) and at the set level ( Ilse et al . , 2018 ) in order to select the textual mention which better describes the relation . To the best of our knowledge , this is the first application of a siamese network by pairing sets of instances , so it can be considered a novelty of this work . We evaluate the generalization capability of our model in recognizing unseen relation types through an one-shot relational classification task introduced in this paper . We train the parameters of the model on a subset of most frequent relations of one of three different distantly supervised datasets used in our experiments . Then , we evaluate it on the long-tailed relations of each dataset . During the test phase , only a single example for each unseen relation is provided . This example is not used to update the parameters of the model as in a classification task , but rather to produce the vector representation of the relation itself . Entity pairs having mention sets close to this representation are more likely to be analogous . The experiments show promising results of our approach on this task , compared with the recent deep models commonly used for encoding textual representations ( Conneau et al . , 2017 ) . However , when the number of the unseen relation types increases , the performance of our model become far from the results obtained in the one-shot image classification ( Koch et al . , 2015 ) , opening an interesting challenge for future work . Finally , our model shows a transfer capability in other tasks through the use of its pre-trained vectors . Indeed , a branch of the hierarchical siamese network can be used to generate entity-entity representations given sets of mentions as input , that we call analogy embeddings . In our experiments , we integrate those representations into an existing end-to-end model based on convolutional networks ( Glass and Gliozzo , 2018b ) , outperfoming the state-of-the-art systems on two shared datasets commonly used for distantly supervised relation extraction . In this paper , we proposed a novel approach to learn representations of relations in text . Alignments between knowledge bases and textual corpora are used as ground truth in order to collect a set of analogies between entity pairs . We designed a hierarchical siamese network trained to recognize those analogies . The experiments showed the two main advantages of our approach . First , the model can generalize on new unseen relation types , obtaining promising results in one-shot learning compared with the state-of-the-art sentence encoders . Second , the model can generate low-rank representations can help existing neuralbased models designed for other tasks . As future work , we plan to continue our investigation by extending the method with other ideas . For instance , the use of positional embeddings , as well as the use of placeholders replacing the entities in the textual mentions are promising future directions . Finally , we plan also to explore the use of analogy embeddings in other tasks , such as question answering and knowledge base population .

Challenge: Current extraction-based models require many training samples for relation extraction tasks, and classification-based approaches also have several limitations such as non-accurate extraction for long-tailed distributions.
Approach: They propose to regard the relation extraction task as an analogy problem and develop a hierarchical siamese network that measures analogical similarity between two entities.
Outcome: The proposed model trained by newly collected analogy pairs outperforms existing models with minimal supervision however it degrates when the number of unseen types increases.