Building product graphs automatically

Automated system tripled the number of facts in a product graph.

Knowledge graphs are data structures that capture relationships between data in a very flexible manner. They can help make information retrieval more precise, and they can also be used to uncover previously unknown relationships in large data sets.

Manually assembling knowledge graphs is extremely time consuming, so researchers in the field have long been investigating techniques for producing them automatically. The approach has been successful for domains such as movie information, which feature relatively few types of relationships and abound in sources of structured data.

Automatically producing knowledge graphs is much more difficult in the case of retail products, where the types of relationships between data items are essentially unbounded — color for clothes, flavor for candy, wattage for electronics, and so on — and where much useful information is stored in free-form product descriptions, customer reviews, and question-and-answer forums.

AutoKnow.png
The inputs to AutoKnow include an existing product taxonomy, user logs, and a product catalogue. AutoKnow automatically combines data from all three sources into a product graph, adding new product types to the taxonomy, adding new values for product attributes, correcting errors, and identifying synonyms.
Credit: Stacy Reilly

This year, at the Association for Computing Machinery’s annual conference on Knowledge Discovery and Data Mining (KDD), my colleagues and I will present a system we call AutoKnow, a suite of techniques for automatically augmenting product knowledge graphs with both structured data and data extracted from free-form text sources.

With AutoKnow, we increased the number of facts in Amazon’s consumables product graph (which includes the categories grocery, beauty, baby, and health) by almost 200%, identifying product types with 87.7% accuracy.

We also compared each of our system’s five modules, which execute tasks such as product type extraction and anomaly detection, to existing systems and found that they improved performance across the board, often quite dramatically (an improvement of more than 300% in the case of product type extraction).

The AutoKnow framework

Knowledge graphs typically consist of entities — the nodes of the graph, often depicted as circles — and relations between the entities — usually depicted as line segments connecting nodes. The entity “drink”, for example, might be related to the entity “coffee” by the relationship “contains”. The entity “bag of coffee” might be related to the entity “16 ounces” by the relationship “has_volume”.

In a narrow domain such as movie information, the number of entity types — such as director, actor, and editor — is limited, as are the number of relationships — directed, performed in, edited, and so on. Moreover, movie sources often provide structured data, explicitly listing cast and crew.

In a retail domain, on the other hand, the number of product types tends to grow as the graph expands. Each product type has its own set of attributes, which may be entirely different from the next product type’s — color and texture, for instance, versus battery type and effective range. And the vital information about a product — that a coffee mug gets too hot to hold, for instance — could be buried in the free-form text of a review or question-and-answer section.

AutoKnow addresses these challenges with five machine-learning-based processing modules, each of which builds on the outputs of the one that precedes it:

  1. Taxonomy enrichment extends the number of entity types in the graph;
  2. Relation discovery identifies attributes of products, those attributes’ range of possible values (different flavors or colors, for instance), and, crucially, which of those attributes are important to customers;
  3. Data imputation uses the entity types and relations discovered by the previous modules to determine whether free-form text associated with products contains any information missing from the graph;
  4. Data cleaning sorts through existing and newly extracted data to see whether any of it was misclassified in the source texts; and
  5. Synonym finding attempts to identify entity types and attribute values that have the same meaning.

The ontology suite

The inputs to AutoKnow include an existing product graph; a catalogue of products that includes some structured information, such as labeled product names, and unstructured product descriptions; free-form product-related information, such as customer reviews and sets of product-related questions and answers; and product query data.

To identify new products, the taxonomy enrichment module uses a machine learning model that labels substrings of the product titles in the source catalogue. For instance, in the product title “Ben & Jerry’s black cherry cheesecake ice cream”, the model would label the substring “ice cream” as the product type.

The same model also labels substrings that indicate product attributes, for use during the relation discovery step. In this case, for instance, it would label “black cherry cheesecake” as the flavor attribute. The model is trained on product descriptions whose product types and attributes have already been classified according to a hand-engineered taxonomy.

Next, the taxonomy enrichment module classifies the newly extracted product types according to their hypernyms, or the broader product categories that they fall under. Ice cream, for instance, falls under the hypernym “Ice cream and novelties”, which falls under the hypernym “Frozen”, and so on.

The hypernym classifier uses data about customer interactions, such as which products customers viewed or purchased after a single query. Again, the machine learning model is trained on product data labeled according to an existing taxonomy.

Relation discovery

The relation discovery module classifies product attributes according to two criteria. The first is whether the attribute applies to a given product. The attribute flavor, for instance, applies to food but not to clothes.

The second criterion is how important the attribute is to buyers of a particular product. Brand name, it turns out, is more important to buyers of snack foods than to buyers of produce.

Both classifiers analyze data provided by providers — product descriptions — and by customers — reviews and Q&As. With both types of input data, the classifiers consider the frequency with which attribute words occur in texts associated with a given product; with the provider data, they also consider how frequently a given word occurs across instances of a particular product type.

The models were trained on data that had been annotated to indicate whether particular attributes applied to the associated products.

The data suite

Step three, data imputation, looks for terms in product descriptions that may fit the new product and attribute categories identified in the previous steps, but which have not yet been added to the graph.

This step uses embeddings, which represent descriptive terms as points in a vector space, where related terms are grouped together. The idea is that, if a number of terms clustered together in the space share the same attribute or product type, the unlabeled terms in the same cluster should, too.

Previously, my Amazon colleagues and I, together with colleagues at the University of Utah, demonstrated state-of-the-art data imputation results by training a sequence-tagging model, much like the one I described above, which labeled “black cherry cheesecake” as a flavor.

Here, however, we vary that approach by conditioning the sequence-tagging model on the product type: that is, the tagged sequence output by the model depends on the product type, whose embedding we include among the inputs.

Cleaning module.png
The architecture of the AutoKnow cleaning module.

The next step is data cleaning, which uses a machine learning model based on the Transformer architecture. The inputs to the model are a textual product description, an attribute (flavor, volume, color, etc.), and a value for that attribute (chocolate, 16 ounces, blue, etc.). Based on the product description, the model decides whether the attribute value is misassigned.

To train the model, we collect valid attribute-value pairs that occur across many instances of a single product type (all ice cream types, for instance, have flavors); these constitute the positive examples. We also generate negative examples by replacing the values in valid attribute-value pairs with mismatched values.

Finally, we analyze our product and attribute sets to find synonyms that should be combined in a single node of the product graph. First, we use customer interaction data to identify items that were viewed during the same queries; their product and attribute descriptions are candidate synonyms.

Then we use a combination of techniques to filter the candidate terms. These include edit distance (a measure of the similarity of two strings of characters) and a neural network. In tests, this approach yielded a respectable .83 area under the precision-recall curve.

In ongoing work, we’re addressing a number of outstanding questions, such as how to handle products with multiple hypernyms (products that have multiple “parents” in the product hierarchy), cleaning data before it’s used to train our models, and using image data as well as textual data to improve our models’ performance.

Watch a video presentation of the AutoKnow paper from Jun Ma, senior applied scientist.

AutoKnow: Self-driving knowledge collection for products of thousands of types | Amazon Science

About the Author
Xin Luna Dong is a principal scientist in the Amazon Product Graph group.

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Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. You will support interesting, analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant
US, VA, Arlington
Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. These are exciting fast-paced businesses in which work on extremely interesting analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant
US, NY, New York
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. In this role, you will be designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.We’re looking for talented data scientists capable of applying classical ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.The primary responsibilities of this role are to:· Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industries· Interact with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve them· Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 25%.Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by protecting Amazon customers from hackers and bad actors? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer every day? Are you excited by the prospect of analyzing and modeling terabytes of data and create state-of-art algorithms to solve real world problems? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Amazon Account Integrity team.The Amazon Account Integrity team works to ensure that customers are protected from bad actors trying to access their accounts. Our greatest challenge is protecting customer trust without unjustly harming good customers. To strike the right balance, we invest in mechanisms which allow us to accurately identify and mitigate risk, and to quickly correct and learn from our mistakes. This strategy includes continuously evolving enforcement policies, iterating our Machine Learning risk models, and exercising high‐judgement decision‐making where we cannot apply automation.
US, CA, San Francisco
LOCATION: San Francisco, CAMULTIPLE POSITIONS AVAILABLE1. Analyze real user data (search query logs) using SQL or equivalent data query language.2. Train machine learning / deep learning based models using ML platforms and libraries such as Tensorflow, Pytorch, Pyspark etc.3. Apply natural language processing techniques to improve ranking of search results and develop new ranking features and techniques building upon the latest results from the academic research community4. Boost search conversion by classifying user search queries and recommending relevant content5. Contribute to operational excellence in search team's scientific features, constructively identifying inefficient processes and proposing solutions6. Experiment with different models, analyze results using statistical methods and iterate on improving the results7. Propose and validate hypotheses to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.8. Design, develop, and implement production level code that serves millions of search requests. Own the full development cycle: design, development, impact assessment, A/B testing (including interpretation of results) and production deployment.9. Telecommuting benefits available#0000
US, CA, Pasadena
LOCATION: Pasadena, CAMULTIPLE POSITIONS AVAILABLE1. Assist large enterprises with researching and learning about new technologies in cloud computing. Understand their business needs in different industries and guide them to a solution using AWS Services.2. Develop approaches to industry problems in optimization, simulation and machine learning and execute customer projects and cases studies end-to-end.3. Develop a deep understanding of emerging technologies and innovate in co-designing novel algorithms on these platforms.4. Collaborate with AWS Services and research teams to continually improve the customer experience.5. Collaborate across the entire AWS organization to bring access to product and service teams, get the right solutions delivered and drive feature innovation based upon customer needs.6. Influence a team of scientists who are working on procedures to build quantum computers more reliably and develop methods to benchmark the performance of quantum hardware.7. Lead the exploratory research and prototyping of new schemes and simulation software for error correction resource estimates and benchmarking.8. Publish in scientific journals, create white papers, write blogs, and build demos and other reusable collateral that can be used by customers.9. Lead research and publication efforts focused on quantum error correction and quantum bench marking.10. Domestic and some international travel may be required up to 25% of the time.11. Telecommuting benefits available.#0000
US, WA, Seattle
Millions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem, the International Seller Services org (ISS) plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon.ISS is looking for a results driven Economist to join its Econometrics and Science team in Seattle. The Economist will work closely with other research scientists, machine learning experts, and economists to design new frameworks that systematically identify low touch machine driven recommendations that propel seller growth while creating a meaningful economic impact for Amazon. Research science at Amazon is a highly experimental activity, although theoretical analysis and innovation are also welcome. Our economists and scientists work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with other scientists within Amazon.The key strategic objectives for this role include:· Model seller behavior, identify success metrics, impacts, and key drivers of seller success· Conceptualize and lead global research initiatives· Guide the technical approach for the design and implementation of successful models and algorithms in support of expert cross-functional teams delivering on demanding projects· Conduct, direct, and coordinate all phases of research projects, demonstrating skill in all stages of the analysis process, including defining key research questions, recommending measures, working with multiple data sources, evaluating methodology and design, executing analysis plans, interpreting and communicating results· Functionally decompose complex problems into simple, straight-forward solutions.If you have an entrepreneurial spirit, you know how to deliver, and you are deeply quantitative, highly innovative, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.