We present an approach for expanding taxonomies with synonyms, or aliases. We target large shopping taxonomies, with thousands of nodes. A comprehensive set of entity aliases is an important component of identifying entities in unstructured text such as product reviews or search queries. Our method consists of two stages: we generate synonym candidates from WordNet and shopping search queries, then use a binary classiﬁer to ﬁlter candidates. We process taxonomies with thousands of synonyms in order to generate over 90,000 synonyms. We show that using the taxonomy to derive contextual features improves classiﬁcation performance over using features from the target node alone.We show that our approach has potential for transfer learning between diﬀerent taxonomy domains, which reduces the need to collect training data for new taxonomies. Semantic Networks (SN) represent entities, relationships between entities, and their properties. Semantic Networks may represent a broad variety of information, from named entities, such as persons or places, to abstract concepts. The term "knowledge graph" is also used to describe this form of structured data. One of the properties commonly encoded in a SN are the primary name and aliases of an entity in multiple languages. For example, Wikidata 1 entity Q2 has multilingual names, such as Earth or Blue Planet (in English), or Tierra (in Spanish). Semantic networks may include sub-structures based on a subset of the relations defined, for example, taxonomies which define type-subtype relations; for example, ConceptNet includes the WordNet taxonomy BID28 as a subset of its nodes and relations.Synonyms, or aliases, are equivalent names for entities in a SN. For example, "washing machine" and "washer" can refer to the same concept of an appliance type. Synonyms enable improved performance in a variety of SN applications. For entity extraction from text BID7 Hersh, 2005, Agrawal et al., 2008] , wikification BID18 BID5 , or natural language instruction grounding BID16 , a broader set of synonyms improves recall. In applications which use SN to generate prompts for users, such as conversational agents BID11 BID31 or generating explanations of the system's state in natural language , a richer set of synonyms results in more varied utterances.In this paper, we focus on the problem of expanding taxonomies with synonyms for applications in which entities are complex concepts arranged into taxonomies designed to facilitate browsing the product catalog on amazon.com. The ontologies contain product type taxonomies, which are the focus for this work, in addition to other information such as attributes for refining products in search results. In addition to distinct product types, the taxonomies contain nodes which are complex concepts, for example combinations of types and attributes, or groupings of multiple types. For example, the node "Gloves & Protective Gear" groups together gloves and other gear; the node "Automatic Irrigation Equipment" describes irrigation equipment that has automation features.The primary application of the synonyms generated using our method is to identify direct references to the taxonomy nodes in text such as search queries. Having a broader set of synonyms for taxonomy nodes enables a broader query coverage for experiences that are specific to products in the taxonomy, for example, showing the best selling products under a given category. It is thus important to the users' experience that node synonyms are as accurate as possible, within the broader context of the taxonomy. For example, given the node "household bathroom surface cleaners" we output synonyms such as "family bathroom surface cleaner" and "house bath surface cleansing." Our method is robust to errors of word sense compatibility, for example we reject "mack game restrainer" as a synonym for "mac game controllers," or "store circuit board" is a rejected candidate for "memory cards."The taxonomies are authored by experts familiar with the respective shopping domains to facilitate navigation and browsing (Section 4.1). They contain over 4,300 nodes and have depths of over 30 nodes; in addition to taxonomical relationships, they represent type properties, possible values, node equivalence, and other information. In this paper, we identify each taxonomy by its root node name. For the example shown in Figure 1 , the taxonomy "Baby Products" includes, among 15 other nodes, a category node named "Car Seats and Accessories." This has the children "Car Seats," "Car Seat Bases," "Car Beds," and "Accessories." The "Accesories" node has 17 children (e.g. "Cup Holders" and "Seat Liners"), while the "Car Seats" node has five children grouped by age group and chair type. We note the fine granularity of nodes, which includes distinctions based on product types, features, indented use, and other criteria dependent on the domain; concepts range from general to specific in fine increments, with children refining and specifying the parent node. The taxonomy nodes we target have complex names, for example "Convertible Child Safety Car Seats" and are thus unlikely to be frequently found in large natural language text corpora with sufficient frequency in order to extract synonyms from unstructured text.We present a method that leverages similarity within the taxonomy to evaluate synonym candidates obtained using low-precision, high-recall methods. Our goal is to enable collecting possible synonyms from a broad range of sources, and output a final set of synonyms consistent to a single standard. This method enables expansion with synonyms for complex SN that are not common in typical text corpora, such as shopping taxonomies for browsing. The main advantages of our approach are that: 1) it does not depend on frequent mentions in corpora of entities in the taxonomy; 2) it identifies synonyms that fit within the broader structure of a taxonomy contained within the graph, and outputs synonyms of similar specificity to the original name; 3) the classifier uses domain-independent features, enabling cross-domain predictions.Our method consists of the following stages ( Figure 2 ):1. Generate synonym candidates for each node of the taxonomy. We experimented with two methods of candidate generation. First, we primarily used a method based on Figure 1 : Sample section of a taxonomy used in this work, which is designed for exploring and filtering online shopping catalogs. We highlight the path from the root node, "Baby Products," to a leaf node, "Child Safety Booster Car Seats." Each node prefixed by a + sign indicates the node has children; leaf nodes are marked by a -. For compactness, we enumerate instead of indenting some of the 15 children of the root node.Figure 2: Overview of our method. We start with product taxonomies designed for browsing a large online shopping catalog, described in Section 4.1, and generate synonym candidates for each node using a thesaurus such as WordNet (Section 3.1). We then classify the set of candidates using a binary classifier (Section 3.2) to output the final set of synonyms.WordNet BID23 , to generate the cartesian product of concept-level synonyms that are present in the node's name (Section 3.1). Secondly, we show additional results on classifying shopping search queries (Section 4.4).2. Filter synonym candidates using a binary classifier (Section 3.2). The classifier uses features derived from a) similarity between the candidate the target node, and b) similarity features between the candidate and other nodes in the taxonomy. Our goal is to avoid producing synonyms more general or more specific than the original node name, such that the synonyms are consistent with the taxonomy as a whole. The classifier uses features independent of the taxonomy vocabulary, making our method suitable for transfer learning by predicting on new taxonomies that do not have training data available. Transfer learning is one method of interest to reduce the need to collect training labels for new taxonomies.The rest of the paper is structured as follows. We first review relevant literature. We then describe the taxonomies we use in this work (Section 4.1), and the methods of obtaining synonym candidates and classifying them. We then evaluate the binary synonym classifier using a corpus of annotations collected using crowdsourcing for synonyms generated using the thesaurus. We also include cross-domain learning experiments to evaluate the potential for training the classifier on one taxonomy and predicting on synonyms for different taxonomy (Section 4.3). Furthermore, we conducted a separate evaluation using an alternative method of selecting synonym candidates, which we will briefly summarize: we associated search queries with taxonomy names using customer purchases, and used these search terms as synonym candidates (Section 4.4). We evaluate the impact of using domain-specific knowledge, specifically lists of known brand names, which may be closely associated but not synonymous with product categories, to improve synonym filtering. We conclude the paper with observations about the role of taxonomy-wide similarity in predicting synonymy and describe future directions. Entity aliases are an important component of ontology construction, enabling entity recognition in text and generating natural language references to entities. We demonstrate a method for identifying synonyms for large taxonomies used for online shopping. Our method consists of two complementary approaches of selecting synonym candidates, and a candidate filtering stage which uses a classifier that includes structural similarity features. We show that using structural similarity features, such as comparing synonym candidates with the parent, children, or root nodes in the taxonomy, improves classification accuracy, and that the method is applicable to transfer learning between taxonomies. We include an additional evaluation on using search queries associated statistically with the taxonomy nodes via user behavior. This method extracts a broader vocabulary for the candidates, including tokens that are not common words, such as proper names, model numbers, or years. We show that using domain knowledge such as brand name definitions improves classification performance for candidates extracted from search queries, which conflate in the same context types, brands and other terms.In future work we will experiment with taxonomies in languages other than English. We will explore the potential for predicting synonyms in other languages than the training language, similar to the experiments we showed for cross-domain prediction.
We use machine learning to generate synonyms for large shopping taxonomies.
{"src": "scitldr-AIC_train", "paper_id": "rJx2g-qaTm"}