Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage

1 . Introduction . Are the two tablets “ TabMaker Color HD 16 GB ” and “ TabMaker Color HD 8 GB ” the same or different ? It is unclear – while they are essentially the same product – they have the same manufacturer and brand , they also have an important difference , storage size . In domains such as products and music , the presence of large numbers of nearly identical but distinct entities makes entity linkage challenging . We refer to these entities as entity variations , and in fact , whether these entities are distinct or not may even be context dependent . More formally , entity variations are sets of entities that share the same value across core attributes called base attributes , but differ from each other along a few crucial attributes which we refer to as variational attributes . For example , variations of the tablet in the motivating example have different values for the variational attribute storage size . two e-commerce catalogs ( details described in Section 5 ) . These catalogs contain two attributes , brand and title . Fig . 1 shows the distribution of duplicates , variations and distinct entity pairs sampled and grouped into five uniform interval buckets using Jaccard similarity computed on the title . Across all ranges , variations are mixed in with duplicates and distinct pairs , with some buckets containing up to 70 % variation pairs . There are no structured attributes to help distinguish them . As we show in our experiments , even state-of-the-art approaches that compute multiple similarity measures incorrectly link variations as duplicates . To address these challenges , in this paper we propose a novel framework , contrastive entity linkage ( CEL ) , that identifies both duplicates and variations together . Duplicate Variation Variational Attrib . Matching Matching Extraction ER approaches Li et al . [ 2015 ] Attribute extraction CEL discovery : We propose a scalable , unsupervised variational attribute discovery approach , VarSpot . VarSpot analyzes similarities and differences between entities within the same catalog and uses the notion of contrast features to model variational attributes . Effectiveness : We perform empirical evaluation in three different domains to show the Contrastive Entity Linkage generality of our approach . Using three different state-of-the-art entity linkage frameworks , including rule-based and deep learning based frameworks , we show that models with contrast features significantly outperform models without them when identifying duplicates and variations . Further , through annotations using Mechanical Turk , we show the interpretable nature of contrast features . 2 . Related Work . There is a wide body of research , spanning several decades , on the task of entity linkage [ Ananthakrishna et al. , 2002 , Dong et al. , 2005 , Bhattacharya and Getoor , 2007 , Dong et al. , 2009 , Sarawagi and Bhamidipaty , 2002 , Domingos , 2004 , Mudgal et al. , 2018 , Trivedi et al. , 2018 ] . See [ Elmagarmid et al. , 2007 , Köpcke and Rahm , 2010 , Doan and Halevy , 2005 , Koudas et al. , 2006 , Getoor and Machanavajjhala , 2012 , Christen , 2012 ] for survey papers comparing various approaches . Starting from the seminal paper by Fellugi and Sunter [ Fellegi and Sunter , 1969 ] , several rule-based approaches [ Fan et al. , 2009 , Singh et al. , 2017 ] , learning approaches [ Bilenko et al. , 2006 , Konda et al. , 2016 , Singla and Domingos , 2006 ] , crowdsourcing approaches [ Gokhale et al. , 2014 , Wang et al. , 2012 , Stonebraker et al. , 2013 ] and graph-based approaches [ Zhu et al. , 2016 ] have been proposed . Recently , approaches that use deep learning models have been proposed [ Mudgal et al. , 2018 , Ebraheem et al. , 2017 ] . Here we review recent work in entity linkage on products , followed by approaches that identify variations of entities . Finally , we review the recent work for task of attribute value extraction from text . Entity linkage for products : The task of entity linkage for products has received a fair bit of attention recently [ Kannan et al. , 2011 , Köpcke et al. , 2012 , Horch et al. , 2016 , Londhe et al. , 2014 ] . Supervised approaches such as [ Kannan et al. , 2011 , Köpcke et al. , 2012 , Ristoski et al. , 2017 ] extract product attributes from the product title and description and use these extracted attributes to perform entity linkage . Kannan et al . [ 2011 ] use an inverted index to extract the set of product attributes from the product title and then use logistic regression to learn their importance . Köpcke et al . [ 2012 ] use regular expressions to extract attributes and use search engines to refine them . They train a SVM using the extracted attributes to link offers . Ristoski et al . [ 2017 ] use a convolutional neural network and a CRF to extract product features and train various supervised models such as random forests and SVM . Approaches such as Horch et al . [ 2016 ] learn a new similarity score by combining several distance scores such as Jaccard and Sorensen distance . Londhe et al . [ 2014 ] propose an unsupervised approach to link products using a community detection algorithm . They make use of a search engine to enrich the text of products . While these approaches identify duplicates , they do not identify variations . Variation Clustering : Recasens et al . [ 2011 ] provide a theoretical framework for incorporating near-identity relations while performing entity linkange . Approaches such as Li et al . [ 2015 ] identify records in a catalog that are variations of each other . They take as input a catalog and a partitioning of the record attributes into common-valued , dominant-valued and multivalued attributes . Using a small set of labeled data , they learn weights for the attribute values . Using these weights , they cluster the records in the catalog such that all variations are present in a cluster . Our approach on the other hand , mines variational attributes present in the unstructured text in an unsupervised manner , and uses them to identify both duplicates and variations across two different catalogs . On et al . [ 2007 ] links groups of entity variations across catalogs that are the same . Our task is different , where the goal is to identify both duplicates and those that are variations of each other . Attribute value extraction : Unsupervised phrase extraction techniques such as Hasan and Ng [ 2010 ] , El-Kishky et al . [ 2014 ] return a ranked list of phrases ( see Hasan and Ng [ 2014 ] for a survey ) . However , they do not explicitly extract product attributes . The task of extracting entity attribute values from unstructured text has received significant attention as well [ Zheng et al. , 2018 , Ghani et al. , 2006 , Petrovski and Bizer , 2017 , Ling and Weld , 2012 , Putthividhya and Hu , 2011 , Kannan et al. , 2011 , Köpcke et al. , 2012 , Ristoski et al. , 2017 ] . These approaches can be broadly classified into closed-word approaches , which assume a predefined set of attribute values [ Ghani et al. , 2006 , Ling and Weld , 2012 , Putthividhya and Hu , 2011 ] , and open-world approaches which do not make such assumptions [ Zheng et al. , 2018 ] . Rule-based and linguistic approaches approaches such as Chiticariu et al . [ 2010 ] , Nadeau and Sekine [ 2007 ] , Mikheev et al . [ 1999 ] leverage the syntactic structure of the text to extract to attributes . CRF-based systems such as Putthividhya and Hu [ 2011 ] make use of seed dictionary to bootstrap the models . Recently , neural network based models that combine LSTMs and CRF have been proposed [ Kozareva et al. , 2016 , Zheng et al. , 2018 ] . However , all these techniques extract attribute values for a pre-defined fixed set of attributes and are supervised in nature . 3 . Preliminaries . In this section , we introduce necessary terms and notation , followed by the formal definition of three-way entity linkage . Base entity and entity variations : A base entity is an abstract , canonical entity associated with a set of attributes called base attributes . Entity variations are a set of related entities that are instantiations of the same base entity . All entity variations share the same value for the base attributes . Entity variations are also associated with a set of variational attributes whose values differ across variations . We denote the function that maps an entity to its base entity by B . Records and attributes : A record represents a real-world entity in a data source and is associated with a set of attributes called record attributes . Records usually have a combination of structured attributes such as brand and price , and unstructured text attributes such as title and description . The attribute values in a record may be missing or incorrect . Not all base and variational attributes are present as structured record attributes . We use ri to denote a record , am to denote the record attribute and ri ( am ) to denote the value of the am th attribute of ri . The set of all attributes is given by A . We denote the function that maps a record to the real-world entity it represents by E. The set of all records present in a data source is referred to as a catalog , and is denoted by C. Having described the needed terms , we now formally define the problem of three-way entity linkage . Definition . Given a pair of records ( ri , rj ) from two catalogs C1 , C2 , the task of 3-way entity linkage is to identify whether these records correspond to duplicates ( E ( ri ) = E ( rj ) ) , variations ( { B ( E ( ri ) ) = B ( E ( rj ) ) ∧ E ( ri ) 6= E ( rj ) } ) or distinct entities ( { B ( E ( ri ) ) 6= B ( E ( rj ) ) ∧ E ( ri ) 6= E ( rj ) } ) . Contrastive Entity Linkage Algorithm 1 VarSpot : An approach to identify contrast features Input : Product catalogs C1 , C2 Output : Contrast features with weights f # Phase 1 : Link each catalog to itself to identify potential variations L1 ← Link ( C1 ) L2 ← Link ( C2 ) # Phase 2 : Extract contrast features from identified variations f1 = ContrastSpot ( L1 ) f2 = ContrastSpot ( L2 ) # Aggregate the contrast features from the two catalogs f = f1 ∪ f2 return f 4 . Contrastive Entity Linkage . In this section , we introduce the notion of contrast features that capture the value of variational attributes , and propose VarSpot , a novel algorithm to identify them . We then describe our overall approach , Contrastive entity linkage , that uses VarSpot to identify both duplicates and variations . 4.1 Contrast Features . Contrast features are phrases or n-grams that represent values of variational attributes present in unstructured text attributes such as title . For the motivating example in the introduction , the phrase 16 GB and 8 GB are contrast features as they correspond to values of the variational attribute storage size . Since contrast features are attribute values of all variational attributes , they are more general . While pack of 2 , 75 oz , dark roast are some examples contrast features in the groceries domain , radio edit , remix , live are examples of contrast features in the music domain .

Summary: Paper is about finding variational attributes for catalogue named entities.  Examples of such attributes is "capacity" for a memory card (e.g. Sandisk flash drive "64GB") and identification of such attributes helps in duplicate detection in E-commerce cataloging and search.  The proposed approach is unsupervised where they first detect some candidate entity variations (pairs of entities with high similarity scores) and then in each pair detect the "significant" phrase that "contrasts" one entity from the other one in the pair as the contrastive feature.  The significance phrases (ngrams) is estimated exhaustively from a corpora with a PMI-like metric.  Authors experiment with three entity linking systems with diverse architecture ranging from rule-based to logistic regression and neural-based models on three domains (music, grocessary and software catalogs).  Results are promising and show that most systems benefit from these features.