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: The authors propose a new algorithm, contrastive entity linkage (CEL), to identify duplicates and variations of entities in catalogues. The authors introduce the concept of base entities and entity variations. A base entity is defined using a set of attributes, and all variations must have the same values for base attributes, but differ in the non-base attributes. The key idea is to mine significant phrases from the unstructured attributes of a record such as the title or a product. The significant phrases are added as a new attribute, and a classifier is trained using the new "variational" attribute. Experiments show that inclusion of the variational attribute improves entity resolution results.