What if your search engine could automatically tune its own domain-specific relevancy model? What if it could learn the important phrases and topics within your domain, automatically identify alternate spellings (synonyms, acronyms, and related phrases) and disambiguate multiple meanings of those phrases, learn the conceptual relationships embedded within your documents, and even use machine-learned ranking to discover the relative importance of different features and then automatically optimize its own ranking algorithms for your domain?
In this presentation, you’ll learn you how to do just that - to evolving Lucene/Solr implementations into self-learning data systems which are able to accept user queries, deliver relevance-ranked results, and automatically learn from your users’ subsequent interactions to continually deliver a more relevant experience for each keyword, category, and group of users.
Such a self-learning system leverages reflected intelligence to consistently improve its understanding of the content (documents and queries), the context of specific users, and the relevance signals present in the collective feedback from every prior user interaction with the system. Come learn how to move beyond manual relevancy tuning and toward a closed-loop system leveraging both the embedded meaning within your content and the wisdom of the crowds to automatically generate search relevancy algorithms optimized for your domain.
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...Trey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will walk through some of the key building blocks necessary to turn a search engine into a dynamically-learning "intent engine", able to interpret and search on meaning, not just keywords. We will walk through CareerBuilder's semantic search architecture, including semantic autocomplete, query and document interpretation, probabilistic query parsing, automatic taxonomy discovery, keyword disambiguation, and personalization based upon user context/behavior. We will also see how to leverage an inverted index (Lucene/Solr) as a knowledge graph that can be used as a dynamic ontology to extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships.
As an example, most search engines completely miss the mark at parsing a query like (Senior Java Developer Portland, OR Hadoop). We will show how to dynamically understand that "senior" designates an experience level, that "java developer" is a job title related to "software engineering", that "portland, or" is a city with a specific geographical boundary (as opposed to a keyword followed by a boolean operator), and that "hadoop" is the skill "Apache Hadoop", which is also related to other terms like "hbase", "hive", and "map/reduce". We will discuss how to train the search engine to parse the query into this intended understanding and how to reflect this understanding to the end user to provide an insightful, augmented search experience.
Topics: Semantic Search, Apache Solr, Finite State Transducers, Probabilistic Query Parsing, Bayes Theorem, Augmented Search, Recommendations, Query Disambiguation, NLP, Knowledge Graphs
Search engines, and Apache Solr in particular, are quickly shifting the focus away from “big data” systems storing massive amounts of raw (but largely unharnessed) content, to “smart data” systems where the most relevant and actionable content is quickly surfaced instead. Apache Solr is the blazing-fast and fault-tolerant distributed search engine leveraged by 90% of Fortune 500 companies. As a community-driven open source project, Solr brings in diverse contributions from many of the top companies in the world, particularly those for whom returning the most relevant results is mission critical.
Out of the box, Solr includes advanced capabilities like learning to rank (machine-learned ranking), graph queries and distributed graph traversals, job scheduling for processing batch and streaming data workloads, the ability to build and deploy machine learning models, and a wide variety of query parsers and functions allowing you to very easily build highly relevant and domain-specific semantic search, recommendations, or personalized search experiences. These days, Solr even enables you to run SQL queries directly against it, mixing and matching the full power of Solr’s free-text, geospatial, and other search capabilities with the a prominent query language already known by most developers (and which many external systems can use to query Solr directly).
Due to the community-oriented nature of Solr, the ecosystem of capabilities also spans well beyond just the core project. In this talk, we’ll also cover several other projects within the larger Apache Lucene/Solr ecosystem that further enhance Solr’s smart data capabilities: bi-directional integration of Apache Spark and Solr’s capabilities, large-scale entity extraction, semantic knowledge graphs for discovering, traversing, and scoring meaningful relationships within your data, auto-generation of domain-specific ontologies, running SPARQL queries against Solr on RDF triples, probabilistic identification of key phrases within a query or document, conceptual search leveraging Word2Vec, and even Lucidworks’ own Fusion project which extends Solr to provide an enterprise-ready smart data platform out of the box.
We’ll dive into how all of these capabilities can fit within your data science toolbox, and you’ll come away with a really good feel for how to build highly relevant “smart data” applications leveraging these key technologies.
Reflected intelligence evolving self-learning data systemsTrey Grainger
In this presentation, we’ll talk about evolving self-learning search and recommendation systems which are able to accept user queries, deliver relevance-ranked results, and iteratively learn from the users’ subsequent interactions to continually deliver a more relevant experience. Such a self-learning system leverages reflected intelligence to consistently improve its understanding of the content (documents and queries), the context of specific users, and the collective feedback from all prior user interactions with the system. Through iterative feedback loops, such a system can leverage user interactions to learn the meaning of important phrases and topics within a domain, identify alternate spellings and disambiguate multiple meanings of those phrases, learn the conceptual relationships between phrases, and even learn the relative importance of features to automatically optimize its own ranking algorithms on a per-query, per-category, or per-user/group basis.
Self-learned Relevancy with Apache SolrTrey Grainger
Search engines are known for "relevancy", but the relevancy models that ship out of the box (BM25, classic tf-idf, etc.) are just scratching the surface of what's needed for a truly insightful application.
What if your search engine could automatically tune its own domain-specific relevancy model based on user interactions? What if it could learn the important phrases and topics within your domain, learn the conceptual relationships embedded within your documents, and even use machine-learned ranking to discover the relative importance of different features and then automatically optimize its own ranking algorithms for your domain? What if you could further use SQL queries to explore these relationships within your own BI tools and return results in ranked order to deliver relevance-driven analytics visualizations?
In this presentation, we'll walk through how you can leverage the myriad of capabilities in the Apache Solr ecosystem (such as the Solr Text Tagger, Semantic Knowledge Graph, Spark-Solr, Solr SQL, learning to rank, probabilistic query parsing, and Lucidworks Fusion) to build self-learning, relevance-first search, recommendations, and data analytics applications.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
Natural Language Search with Knowledge Graphs (Haystack 2019)Trey Grainger
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
Enhancing relevancy through personalization & semantic searchTrey Grainger
Matching keywords is just step one in the effort to maximize the relevancy of your search platform. In this talk, you'll learn how to implement advanced relevancy techniques which enable your search platform to "learn" from your content and users' behavior. Topics will include automatic synonym discovery, latent semantic indexing, payload scoring, document-to-document searching, foreground vs. background corpus analysis for interesting term extraction, collaborative filtering, and mining user behavior to drive geographically and conceptually personalized search results. You'll learn how CareerBuilder has enhanced Solr (also utilizing Hadoop) to dynamically discover relationships between data and behavior, and how you can implement similar techniques to greatly enhance the relevancy of your search platform.
Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disamb...Trey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will walk through some of the key building blocks necessary to turn a search engine into a dynamically-learning "intent engine", able to interpret and search on meaning, not just keywords. We will walk through CareerBuilder's semantic search architecture, including semantic autocomplete, query and document interpretation, probabilistic query parsing, automatic taxonomy discovery, keyword disambiguation, and personalization based upon user context/behavior. We will also see how to leverage an inverted index (Lucene/Solr) as a knowledge graph that can be used as a dynamic ontology to extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships.
As an example, most search engines completely miss the mark at parsing a query like (Senior Java Developer Portland, OR Hadoop). We will show how to dynamically understand that "senior" designates an experience level, that "java developer" is a job title related to "software engineering", that "portland, or" is a city with a specific geographical boundary (as opposed to a keyword followed by a boolean operator), and that "hadoop" is the skill "Apache Hadoop", which is also related to other terms like "hbase", "hive", and "map/reduce". We will discuss how to train the search engine to parse the query into this intended understanding and how to reflect this understanding to the end user to provide an insightful, augmented search experience.
Topics: Semantic Search, Apache Solr, Finite State Transducers, Probabilistic Query Parsing, Bayes Theorem, Augmented Search, Recommendations, Query Disambiguation, NLP, Knowledge Graphs
Search engines, and Apache Solr in particular, are quickly shifting the focus away from “big data” systems storing massive amounts of raw (but largely unharnessed) content, to “smart data” systems where the most relevant and actionable content is quickly surfaced instead. Apache Solr is the blazing-fast and fault-tolerant distributed search engine leveraged by 90% of Fortune 500 companies. As a community-driven open source project, Solr brings in diverse contributions from many of the top companies in the world, particularly those for whom returning the most relevant results is mission critical.
Out of the box, Solr includes advanced capabilities like learning to rank (machine-learned ranking), graph queries and distributed graph traversals, job scheduling for processing batch and streaming data workloads, the ability to build and deploy machine learning models, and a wide variety of query parsers and functions allowing you to very easily build highly relevant and domain-specific semantic search, recommendations, or personalized search experiences. These days, Solr even enables you to run SQL queries directly against it, mixing and matching the full power of Solr’s free-text, geospatial, and other search capabilities with the a prominent query language already known by most developers (and which many external systems can use to query Solr directly).
Due to the community-oriented nature of Solr, the ecosystem of capabilities also spans well beyond just the core project. In this talk, we’ll also cover several other projects within the larger Apache Lucene/Solr ecosystem that further enhance Solr’s smart data capabilities: bi-directional integration of Apache Spark and Solr’s capabilities, large-scale entity extraction, semantic knowledge graphs for discovering, traversing, and scoring meaningful relationships within your data, auto-generation of domain-specific ontologies, running SPARQL queries against Solr on RDF triples, probabilistic identification of key phrases within a query or document, conceptual search leveraging Word2Vec, and even Lucidworks’ own Fusion project which extends Solr to provide an enterprise-ready smart data platform out of the box.
We’ll dive into how all of these capabilities can fit within your data science toolbox, and you’ll come away with a really good feel for how to build highly relevant “smart data” applications leveraging these key technologies.
Reflected intelligence evolving self-learning data systemsTrey Grainger
In this presentation, we’ll talk about evolving self-learning search and recommendation systems which are able to accept user queries, deliver relevance-ranked results, and iteratively learn from the users’ subsequent interactions to continually deliver a more relevant experience. Such a self-learning system leverages reflected intelligence to consistently improve its understanding of the content (documents and queries), the context of specific users, and the collective feedback from all prior user interactions with the system. Through iterative feedback loops, such a system can leverage user interactions to learn the meaning of important phrases and topics within a domain, identify alternate spellings and disambiguate multiple meanings of those phrases, learn the conceptual relationships between phrases, and even learn the relative importance of features to automatically optimize its own ranking algorithms on a per-query, per-category, or per-user/group basis.
Self-learned Relevancy with Apache SolrTrey Grainger
Search engines are known for "relevancy", but the relevancy models that ship out of the box (BM25, classic tf-idf, etc.) are just scratching the surface of what's needed for a truly insightful application.
What if your search engine could automatically tune its own domain-specific relevancy model based on user interactions? What if it could learn the important phrases and topics within your domain, learn the conceptual relationships embedded within your documents, and even use machine-learned ranking to discover the relative importance of different features and then automatically optimize its own ranking algorithms for your domain? What if you could further use SQL queries to explore these relationships within your own BI tools and return results in ranked order to deliver relevance-driven analytics visualizations?
In this presentation, we'll walk through how you can leverage the myriad of capabilities in the Apache Solr ecosystem (such as the Solr Text Tagger, Semantic Knowledge Graph, Spark-Solr, Solr SQL, learning to rank, probabilistic query parsing, and Lucidworks Fusion) to build self-learning, relevance-first search, recommendations, and data analytics applications.
"Searching for Meaning: The Hidden Structure in Unstructured Data". Presentation by Trey Grainger at the Southern Data Science Conference (SDSC) 2018. Covers linguistic theory, application in search and information retrieval, and knowledge graph and ontology learning methods for automatically deriving contextualized meaning from unstructured (free text) content.
Natural Language Search with Knowledge Graphs (Haystack 2019)Trey Grainger
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
Enhancing relevancy through personalization & semantic searchTrey Grainger
Matching keywords is just step one in the effort to maximize the relevancy of your search platform. In this talk, you'll learn how to implement advanced relevancy techniques which enable your search platform to "learn" from your content and users' behavior. Topics will include automatic synonym discovery, latent semantic indexing, payload scoring, document-to-document searching, foreground vs. background corpus analysis for interesting term extraction, collaborative filtering, and mining user behavior to drive geographically and conceptually personalized search results. You'll learn how CareerBuilder has enhanced Solr (also utilizing Hadoop) to dynamically discover relationships between data and behavior, and how you can implement similar techniques to greatly enhance the relevancy of your search platform.
Crowdsourced query augmentation through the semantic discovery of domain spec...Trey Grainger
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
The Next Generation of AI-powered SearchTrey Grainger
What does it really mean to deliver an "AI-powered Search" solution? In this talk, we’ll bring clarity to this topic, showing you how to marry the art of the possible with the real-world challenges involved in understanding your content, your users, and your domain. We'll dive into emerging trends in AI-powered Search, as well as many of the stumbling blocks found in even the most advanced AI and Search applications, showing how to proactively plan for and avoid them. We'll walk through the various uses of reflected intelligence and feedback loops for continuous learning from user behavioral signals and content updates, also covering the increasing importance of virtual assistants and personalized search use cases found within the intersection of traditional search and recommendation engines. Our goal will be to provide a baseline of mainstream AI-powered Search capabilities available today, and to paint a picture of what we can all expect just on the horizon.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
The Relevance of the Apache Solr Semantic Knowledge GraphTrey Grainger
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
Closing keynote by Trey Grainger from Activate 2018 in Montreal, Canada. Covers trends in the intersection of Search (Information Retrieval) and Artificial Intelligence, and the underlying capabilities needed to deliver those trends at scale.
Intent Algorithms: The Data Science of Smart Information Retrieval SystemsTrey Grainger
Search engines, recommendation systems, advertising networks, and even data analytics tools all share the same end goal - to deliver the most relevant information possible to meet a given information need (usually in real-time). Perfecting these systems requires algorithms which can build a deep understanding of the domains represented by the underlying data, understand the nuanced ways in which words and phrases should be parsed and interpreted within different contexts, score the relationships between arbitrary phrases and concepts, continually learn from users' context and interactions to make the system smarter, and generate custom models of personalized tastes for each user of the system.
In this talk, we'll dive into both the philosophical questions associated with such systems ("how do you accurately represent and interpret the meaning of words?", "How do you prevent filter bubbles?", etc.), as well as look at practical examples of how these systems have been successfully implemented in production systems combining a variety of available commercial and open source components (inverted indexes, entity extraction, similarity scoring and machine-learned ranking, auto-generated knowledge graphs, phrase interpretation and concept expansion, etc.).
South Big Data Hub: Text Data Analysis PanelTrey Grainger
Slides from Trey's opening presentation for the South Big Data Hub's Text Data Analysis Panel on December 8th, 2016. Trey provided a quick introduction to Apache Solr, described how companies are using Solr to power relevant search in industry, and provided a glimpse on where the industry is heading with regard to implementing more intelligent and relevant semantic search.
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineTrey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will describe how to overcome this by leveraging Lucene/Solr to power a knowledge graph that can extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships. For example, if a user types in (Senior Java Developer Portland, OR Hadoop), you or I know that the term “senior” designates an experience level, that “java developer” is a job title related to “software engineering”, that “portland, or” is a city with a specific geographical boundary, and that “hadoop” is a technology related to terms like “hbase”, “hive”, and “map/reduce”. Out of the box, however, most search engines just parse this query as text:((senior AND java AND developer AND portland) OR (hadoop)), which is not at all what the user intended. We will discuss how to train the search engine to parse the query into this intended understanding, and how to reflect this understanding to the end user to provide an insightful, augmented search experience. Topics: Semantic Search, Finite State Transducers, Probabilistic Parsing, Bayes Theorem, Augmented Search, Recommendations, NLP, Knowledge Graphs
Building Search & Recommendation EnginesTrey Grainger
In this talk, you'll learn how to build your own search and recommendation engine based on the open source Apache Lucene/Solr project. We'll dive into some of the data science behind how search engines work, covering multi-lingual text analysis, natural language processing, relevancy ranking algorithms, knowledge graphs, reflected intelligence, collaborative filtering, and other machine learning techniques used to drive relevant results for free-text queries. We'll also demonstrate how to build a recommendation engine leveraging the same platform and techniques that power search for most of the world's top companies. You'll walk away from this presentation with the toolbox you need to go and implement your very own search-based product using your own data.
Natural Language Search with Knowledge Graphs (Activate 2019)Trey Grainger
To optimally interpret most natural language queries, its important to understand a highly-nuanced, contextual interpretation of the domain-specific phrases, entities, commands, and relationships represented or implied within the search and within your domain.
In this talk, we'll walk through such a search system powered by Solr's Text Tagger and Semantic Knowledge graph. We'll have fun with some of the more search-centric use cases of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "best bbq near activate" into:
{!func}mul(min(popularity,1),100) bbq^0.91032 ribs^0.65674 brisket^0.63386 doc_type:"restaurant" {!geofilt d=50 sfield="coordinates_pt" pt="38.916120,-77.045220"}
We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding like this within your search engine.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Balancing the Dimensions of User IntentTrey Grainger
The first step in returning relevant search results is successfully interpreting the user’s intent. This requires combining a holistic understanding of your content, your users, and your domain. Traditional keyword search focuses on the content understanding dimension. Knowledge graphs are then typically built and leveraged to represent an understanding of your domain. Finally, Collaborative recommendations and user profile learning are typically the tools of choice for generating and modeling an understanding of the preferences of each user.
While these systems (search, recommendations, and knowledge graphs) are often built and used in isolation, combining them together is the key to truly understanding a user’s query intent. For example, combining traditional keyword search with your knowledge graph leads to semantic search capabilities, and combining traditional keyword search with recommendations leads to personalized search experiences. Combining all of these dimensions together in an appropriately balanced way will ultimately lead to the most accurate interpretation of a user’s query, resulting in a better query to the core search engine and ultimately a better, more relevant search experience.
In this talk, we’ll demonstrate strategies for delivering and combining each of these dimensions of user intent, and we’ll walk through concrete examples of how to balance the nuances of each so that you also don’t over-personalize, over-contextualize, or under appreciate the nuances of your user’s intent.
Natural Language Search with Knowledge Graphs (Chicago Meetup)Trey Grainger
To optimally interpret most natural language queries, its important to understand a highly-nuanced, contextual interpretation of the domain-specific phrases, entities, commands, and relationships represented or implied within the search and within your domain.
In this talk, we'll walk through such a search system powered by Solr's Text Tagger and Semantic Knowledge graph. We'll have fun with some of the more search-centric use cases of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "best bbq near activate" into:
{!func}mul(min(popularity,1),100) bbq^0.91032 ribs^0.65674 brisket^0.63386 doc_type:"restaurant" {!geofilt d=50 sfield="coordinates_pt" pt="38.916120,-77.045220"}
We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding like this within your search engine.
Building a real time big data analytics platform with solrTrey Grainger
Having “big data” is great, but turning that data into actionable intelligence is where the real value lies. This talk will demonstrate how you can use Solr to build a highly scalable data analytics engine to enable customers to engage in lightning fast, real-time knowledge discovery.
At CareerBuilder, we utilize these techniques to report the supply and demand of the labor force, compensation trends, customer performance metrics, and many live internal platform analytics. You will walk away from this talk with an advanced understanding of faceting, including pivot-faceting, geo/radius faceting, time-series faceting, function faceting, and multi-select faceting. You’ll also get a sneak peak at some new faceting capabilities just wrapping up development including distributed pivot facets and percentile/stats faceting, which will be open-sourced.
The presentation will be a technical tutorial, along with real-world use-cases and data visualizations. After this talk, you'll never see Solr as just a text search engine again.
Crowdsourced query augmentation through the semantic discovery of domain spec...Trey Grainger
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
Building a semantic search system - one that can correctly parse and interpret end-user intent and return the ideal results for users’ queries - is not an easy task. It requires semantically parsing the terms, phrases, and structure within queries, disambiguating polysemous terms, correcting misspellings, expanding to conceptually synonymous or related concepts, and rewriting queries in a way that maps the correct interpretation of each end user’s query into the ideal representation of features and weights that will return the best results for that user. Not only that, but the above must often be done within the confines of a very specific domain - ripe with its own jargon and linguistic and conceptual nuances.
This talk will walk through the anatomy of a semantic search system and how each of the pieces described above fit together to deliver a final solution. We'll leverage several recently-released capabilities in Apache Solr (the Semantic Knowledge Graph, Solr Text Tagger, Statistical Phrase Identifier) and Lucidworks Fusion (query log mining, misspelling job, word2vec job, query pipelines, relevancy experiment backtesting) to show you an end-to-end working Semantic Search system that can automatically learn the nuances of any domain and deliver a substantially more relevant search experience.
The Next Generation of AI-powered SearchTrey Grainger
What does it really mean to deliver an "AI-powered Search" solution? In this talk, we’ll bring clarity to this topic, showing you how to marry the art of the possible with the real-world challenges involved in understanding your content, your users, and your domain. We'll dive into emerging trends in AI-powered Search, as well as many of the stumbling blocks found in even the most advanced AI and Search applications, showing how to proactively plan for and avoid them. We'll walk through the various uses of reflected intelligence and feedback loops for continuous learning from user behavioral signals and content updates, also covering the increasing importance of virtual assistants and personalized search use cases found within the intersection of traditional search and recommendation engines. Our goal will be to provide a baseline of mainstream AI-powered Search capabilities available today, and to paint a picture of what we can all expect just on the horizon.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
The Relevance of the Apache Solr Semantic Knowledge GraphTrey Grainger
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
Closing keynote by Trey Grainger from Activate 2018 in Montreal, Canada. Covers trends in the intersection of Search (Information Retrieval) and Artificial Intelligence, and the underlying capabilities needed to deliver those trends at scale.
Intent Algorithms: The Data Science of Smart Information Retrieval SystemsTrey Grainger
Search engines, recommendation systems, advertising networks, and even data analytics tools all share the same end goal - to deliver the most relevant information possible to meet a given information need (usually in real-time). Perfecting these systems requires algorithms which can build a deep understanding of the domains represented by the underlying data, understand the nuanced ways in which words and phrases should be parsed and interpreted within different contexts, score the relationships between arbitrary phrases and concepts, continually learn from users' context and interactions to make the system smarter, and generate custom models of personalized tastes for each user of the system.
In this talk, we'll dive into both the philosophical questions associated with such systems ("how do you accurately represent and interpret the meaning of words?", "How do you prevent filter bubbles?", etc.), as well as look at practical examples of how these systems have been successfully implemented in production systems combining a variety of available commercial and open source components (inverted indexes, entity extraction, similarity scoring and machine-learned ranking, auto-generated knowledge graphs, phrase interpretation and concept expansion, etc.).
South Big Data Hub: Text Data Analysis PanelTrey Grainger
Slides from Trey's opening presentation for the South Big Data Hub's Text Data Analysis Panel on December 8th, 2016. Trey provided a quick introduction to Apache Solr, described how companies are using Solr to power relevant search in industry, and provided a glimpse on where the industry is heading with regard to implementing more intelligent and relevant semantic search.
Leveraging Lucene/Solr as a Knowledge Graph and Intent EngineTrey Grainger
Search engines frequently miss the mark when it comes to understanding user intent. This talk will describe how to overcome this by leveraging Lucene/Solr to power a knowledge graph that can extract phrases, understand and weight the semantic relationships between those phrases and known entities, and expand the query to include those additional conceptual relationships. For example, if a user types in (Senior Java Developer Portland, OR Hadoop), you or I know that the term “senior” designates an experience level, that “java developer” is a job title related to “software engineering”, that “portland, or” is a city with a specific geographical boundary, and that “hadoop” is a technology related to terms like “hbase”, “hive”, and “map/reduce”. Out of the box, however, most search engines just parse this query as text:((senior AND java AND developer AND portland) OR (hadoop)), which is not at all what the user intended. We will discuss how to train the search engine to parse the query into this intended understanding, and how to reflect this understanding to the end user to provide an insightful, augmented search experience. Topics: Semantic Search, Finite State Transducers, Probabilistic Parsing, Bayes Theorem, Augmented Search, Recommendations, NLP, Knowledge Graphs
Building Search & Recommendation EnginesTrey Grainger
In this talk, you'll learn how to build your own search and recommendation engine based on the open source Apache Lucene/Solr project. We'll dive into some of the data science behind how search engines work, covering multi-lingual text analysis, natural language processing, relevancy ranking algorithms, knowledge graphs, reflected intelligence, collaborative filtering, and other machine learning techniques used to drive relevant results for free-text queries. We'll also demonstrate how to build a recommendation engine leveraging the same platform and techniques that power search for most of the world's top companies. You'll walk away from this presentation with the toolbox you need to go and implement your very own search-based product using your own data.
Natural Language Search with Knowledge Graphs (Activate 2019)Trey Grainger
To optimally interpret most natural language queries, its important to understand a highly-nuanced, contextual interpretation of the domain-specific phrases, entities, commands, and relationships represented or implied within the search and within your domain.
In this talk, we'll walk through such a search system powered by Solr's Text Tagger and Semantic Knowledge graph. We'll have fun with some of the more search-centric use cases of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "best bbq near activate" into:
{!func}mul(min(popularity,1),100) bbq^0.91032 ribs^0.65674 brisket^0.63386 doc_type:"restaurant" {!geofilt d=50 sfield="coordinates_pt" pt="38.916120,-77.045220"}
We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding like this within your search engine.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Balancing the Dimensions of User IntentTrey Grainger
The first step in returning relevant search results is successfully interpreting the user’s intent. This requires combining a holistic understanding of your content, your users, and your domain. Traditional keyword search focuses on the content understanding dimension. Knowledge graphs are then typically built and leveraged to represent an understanding of your domain. Finally, Collaborative recommendations and user profile learning are typically the tools of choice for generating and modeling an understanding of the preferences of each user.
While these systems (search, recommendations, and knowledge graphs) are often built and used in isolation, combining them together is the key to truly understanding a user’s query intent. For example, combining traditional keyword search with your knowledge graph leads to semantic search capabilities, and combining traditional keyword search with recommendations leads to personalized search experiences. Combining all of these dimensions together in an appropriately balanced way will ultimately lead to the most accurate interpretation of a user’s query, resulting in a better query to the core search engine and ultimately a better, more relevant search experience.
In this talk, we’ll demonstrate strategies for delivering and combining each of these dimensions of user intent, and we’ll walk through concrete examples of how to balance the nuances of each so that you also don’t over-personalize, over-contextualize, or under appreciate the nuances of your user’s intent.
Natural Language Search with Knowledge Graphs (Chicago Meetup)Trey Grainger
To optimally interpret most natural language queries, its important to understand a highly-nuanced, contextual interpretation of the domain-specific phrases, entities, commands, and relationships represented or implied within the search and within your domain.
In this talk, we'll walk through such a search system powered by Solr's Text Tagger and Semantic Knowledge graph. We'll have fun with some of the more search-centric use cases of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "best bbq near activate" into:
{!func}mul(min(popularity,1),100) bbq^0.91032 ribs^0.65674 brisket^0.63386 doc_type:"restaurant" {!geofilt d=50 sfield="coordinates_pt" pt="38.916120,-77.045220"}
We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding like this within your search engine.
Building a real time big data analytics platform with solrTrey Grainger
Having “big data” is great, but turning that data into actionable intelligence is where the real value lies. This talk will demonstrate how you can use Solr to build a highly scalable data analytics engine to enable customers to engage in lightning fast, real-time knowledge discovery.
At CareerBuilder, we utilize these techniques to report the supply and demand of the labor force, compensation trends, customer performance metrics, and many live internal platform analytics. You will walk away from this talk with an advanced understanding of faceting, including pivot-faceting, geo/radius faceting, time-series faceting, function faceting, and multi-select faceting. You’ll also get a sneak peak at some new faceting capabilities just wrapping up development including distributed pivot facets and percentile/stats faceting, which will be open-sourced.
The presentation will be a technical tutorial, along with real-world use-cases and data visualizations. After this talk, you'll never see Solr as just a text search engine again.
Building a real time, solr-powered recommendation engineTrey Grainger
Searching text is what Solr is known for, but did you know that many companies receive an equal or greater business impact through implementing a recommendation engine in addition to their text search capabilities? With a few tweaks, Solr (or Lucene) can also serve as a full featured recommendation engine. Machine learning libraries like Apache Mahout provide excellent behavior-based, off-line recommendation algorithms, but what if you want more control? This talk will demonstrate how to effectively utilize Solr to perform collaborative filtering (users who liked this also liked…), categorical classification and subsequent hierarchical-based recommendations, as well as related-concept extraction and concept based recommendations. Sound difficult? It’s not. Come learn step-by-step how to create a powerful real-time recommendation engine using Apache Solr and see some real-world examples of some of these strategies in action.
Tutorial on developing a Solr search component pluginsearchbox-com
In this set of slides we give a step by step tutorial on how to develop a fully functional solr search component plugin. Additionally we provide links to full source code which can be used as a template to rapidly start creating your own search components.
Semantic & Multilingual Strategies in Lucene/SolrTrey Grainger
When searching on text, choosing the right CharFilters, Tokenizer, stemmers, and other TokenFilters for each supported language is critical. Additional tools of the trade include language detection through UpdateRequestProcessors, parts of speech analysis, entity extraction, stopword and synonym lists, relevancy differentiation for exact vs. stemmed vs. conceptual matches, and identification of statistically interesting phrases per language. For multilingual search, you also need to choose between several strategies such as: searching across multiple fields, using a separate collection per language combination, or combining multiple languages in a single field (custom code is required for this and will be open sourced). These all have their own strengths and weaknesses depending upon your use case. This talk will provide a tutorial (with code examples) on how to pull off each of these strategies as well as compare and contrast the different kinds of stemmers, review the precision/recall impact of stemming vs. lemmatization, and describe some techniques for extracting meaningful relationships between terms to power a semantic search experience per-language. Come learn how to build an excellent semantic and multilingual search system using the best tools and techniques Lucene/Solr has to offer!
Magnetic - Query Categorization at ScaleAlex Dorman
presented 09/23/14 at NYC Search, Discovery & Analytics meetup
Classification of short text into a predefined hierarchy of categories is a challenge. The need to categorize short texts arises in multiple domains: keywords and queries in online advertising, improvement of search engine results, analysis of tweets or messages in social networks, etc. We leverage community-moderated, freely-available data sets (Wikipedia, DBPedia, Freebase) and open-source tools (Hadoop, Solr) to build a flexible and extensible classification model.
Magnetic is an online advertising company specializing in search retargeting and applying data science to online search behavior. We create custom real-time audience segments based on what users have searched for across the web. Targeting individual keywords found in user search history is a great way to build an audience. But the need to create manually selected keywords might present operational challenge. The ability to classify queries and keywords helps to create larger audiences with less effort and better accuracy. Among the other use cases for keyword classification in online advertising are reporting on size of inventory available by category, and campaign performance optimization.
We will share our experiences building a real-world data science system that scales to production data volumes of more than 20 million keyword classifications per hour. And will touch on some aspect of knowledge discovery such as language detection, n-gram extraction, and entity recognition.
about the speaker: Alex Dorman, CTO at Magnetic.
Alex has used Hadoop technologies since 2007. Before joining Magnetic, Alex built big data platforms and teams at Proclivity Media and ContextWeb/PulsePoint.
A simple and easy tool to organize PS Queries into logical business processes. Will be an aid to organisations that maintain hundreds of PS Queries and use Query Manager/Viewer to run them. This tool helps you to classify queries into corresponding business processes and later display and run the queries from a custom page.
More details on the framework on my blog - www.peoplesofthrms.blogspot.com
1 1/2 years ago we have rolled out a new integrated full-text search engine for our Intranet based on Apache Solr. The search engine integrates various data sources such as file systems, wikis, internal websites and web applications, shared calendars, our corporate database, CRM system, email archive, task management and defect tracking etc. This talk is an experience report about some of the good things, the bad things and the surprising things we have encountered over two years of developing with, operating and using a Intranet search engine based on Apache Solr.
After setting the scene, we will discuss some interesting requirements that we have for our search engine and how we solved them with Apache Solr (or at least tried to solve). Using these concrete examples, we will discuss some interesting features and limitations of Apache Solr.
In the second part of the talk, we will tell a couple of "war stories" and walk through some interesting, annoying and surprising problems that we faced, how we analyzed the issues, identified the cause of the problems and eventually solved them.
The talk is aimed at software developers and architects with some basic knowledge about Apache Solr, the Apache Lucene project familiy or similar full-text search engines. It is not an introduction into Apache Solr and we will dive right into the interesting and juicy bits.
The Brave New World of Universal Analytics - SMX London 2014Martijn
Measuring the Multi-Platform world. My talk in the Brave New World of Universal Analytics at SMX London 2014. Providing an overview of the UK / US Digital Landscape, best practices on Multi-Platform Analytics and the Mobile Path to Purchase in Retail.
Presented by James Atherton, Search Team Lead, 7digital
A usage/case study, describing our journey as we implemented Lucene/Solr, the lessons we learned along the way and where we hope to go in the future.How we implemented our instant search/search suggest. How we handle trying to index 400 million tracks and metadata for over 40 countries, comprising over 300GB of data, and about 70GB of indexes. Finally where we hope to go in the future.
Scaling Recommendations, Semantic Search, & Data Analytics with solrTrey Grainger
This presentation is from the inaugural Atlanta Solr Meetup held on 2014/10/21 at Atlanta Tech Village.
Description: CareerBuilder uses Solr to power their recommendation engine, semantic search, and data analytics products. They maintain an infrastructure of hundreds of Solr servers, holding over a billion documents and serving over a million queries an hour across thousands of unique search indexes. Come learn how CareerBuilder has integrated Solr into their technology platform (with assistance from Hadoop, Cassandra, and RabbitMQ) and walk through api and code examples to see how you can use Solr to implement your own real-time recommendation engine, semantic search, and data analytics solutions.
Speaker: Trey Grainger is the Director of Engineering for Search & Analytics at CareerBuilder.com and is the co-author of Solr in Action (2014, Manning Publications), the comprehensive example-driven guide to Apache Solr. His search experience includes handling multi-lingual content across dozens of markets/languages, machine learning, semantic search, big data analytics, customized Lucene/Solr scoring models, data mining and recommendation systems. Trey is also the Founder of Celiaccess.com, a gluten-free search engine, and is a frequent speaker at Lucene and Solr-related conferences.
Discovering User's Topics of Interest in Recommender SystemsGabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling.
Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We present some of Smart Canvas features powered by its recommender system, such as:
- Highlight relevant content, explaining to the users which of his topics of interest have generated each recommendation.
- Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests.
- Recommends people with similar interests, explaining which topics brings them together.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Gabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas, a SaaS that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to a content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Short-Bio: Gabriel Moreira is a scientist passionate about solving problems with data. He is Head of Machine Learning at CI&T and Doctoral student at Instituto Tecnológico de Aeronáutica - ITA. where he has also got his Masters on Science. His current research interests are recommender systems and deep learning.
https://www.meetup.com/pt-BR/machine-learning-big-data-engenharia/events/239037949/
Automatic Metadata Generation Charles DuncanJISC CETIS
Slides by Charles Duncan summarising the findings of the automatic metadata generation use cases project, see http://www.intrallect.com/wiki/index.php/AMG-UC
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
DataFest 2017. Introduction to Natural Language Processing by Rudolf Eremyanrudolf eremyan
The objective of this workshop is to show how natural language processing applied in modern applications such as Google Search, Apple Siri, Bing Translator and etc. During the workshop we will go through history if natural language processing, talk about typical problems, consider classical approaches and methods, and compare them with state-of-the-art deep learning techniques.
Author: Rudolf Eremyan
Email: eremyan.rudolf@gmail.com
Phone: +995599607066
LinkedIn: https://www.linkedin.com/in/rudolferemyan/
DataFest Tbilisi 2017 website: https://datafest.ge
The Apache Solr Semantic Knowledge GraphTrey Grainger
What if instead of a query returning documents, you could alternatively return other keywords most related to the query: i.e. given a search for "data science", return me back results like "machine learning", "predictive modeling", "artificial neural networks", etc.? Solr’s Semantic Knowledge Graph does just that. It leverages the inverted index to automatically model the significance of relationships between every term in the inverted index (even across multiple fields) allowing real-time traversal and ranking of any relationship within your documents. Use cases for the Semantic Knowledge Graph include disambiguation of multiple meanings of terms (does "driver" mean truck driver, printer driver, a type of golf club, etc.), searching on vectors of related keywords to form a conceptual search (versus just a text match), powering recommendation algorithms, ranking lists of keywords based upon conceptual cohesion to reduce noise, summarizing documents by extracting their most significant terms, and numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. In this talk, we'll do a deep dive into the internals of how the Semantic Knowledge Graph works and will walk you through how to get up and running with an example dataset to explore the meaningful relationships hidden within your data.
Haystack 2019 - Natural Language Search with Knowledge Graphs - Trey GraingerOpenSource Connections
To optimally interpret most natural language queries, it is necessary to understand the phrases, entities, commands, and relationships represented or implied within the search. Knowledge graphs serve as useful instantiations of ontologies which can help represent this kind of knowledge within a domain.
In this talk, we'll walk through techniques to build knowledge graphs automatically from your own domain-specific content, how you can update and edit the nodes and relationships, and how you can seamlessly integrate them into your search solution for enhanced query interpretation and semantic search. We'll have some fun with some of the more search-centric use cased of knowledge graphs, such as entity extraction, query expansion, disambiguation, and pattern identification within our queries: for example, transforming the query "bbq near haystack" into
{ filter:["doc_type":"restaurant"], "query": { "boost": { "b": "recip(geodist(38.034780,-78.486790),1,1000,1000)", "query": "bbq OR barbeque OR barbecue" } } }
We'll also specifically cover use of the Semantic Knowledge Graph, a particularly interesting knowledge graph implementation available within Apache Solr that can be auto-generated from your own domain-specific content and which provides highly-nuanced, contextual interpretation of all of the terms, phrases and entities within your domain. We'll see a live demo with real world data demonstrating how you can build and apply your own knowledge graphs to power much more relevant query understanding within your search engine.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Reflected Intelligence: Lucene/Solr as a self-learning data system
1. Reflected Intelligence: Lucene/Solr as a self-learning data system
Trey Grainger
SVP of Engineering, Lucidworks
O C T O B E R 1 1 - 1 4 , 2 0 1 6 B O S T O N , M A
2. Trey Grainger
SVP of Engineering
• Previously Director of Engineering @ CareerBuilder
• MBA, Management of Technology – Georgia Tech
• BA, Computer Science, Business, & Philosophy – Furman University
• Information Retrieval & Web Search - Stanford University
Other fun projects:
• Co-author of Solr in Action, plus a handful of research papers
• Frequent conference speaker
• Founder of Celiaccess.com, the gluten-free search engine
• Lucene/Solr contributor
About Me
6. The Three C’s
Content:
Keywords and other features in your documents
Collaboration:
How other’s have chosen to interact with your system
Context:
Available information about your users and their intent
Reflected Intelligence
“Leveraging previous data and interactions to improve how
new data and interactions should be interpreted”
8. ● Recommendation Engines
● Building user profiles from past searches, clicks, and other actions
● Identifying correlations between keywords/phrases
● Building out automatically-generated ontologies from content and queries
● Determining relevancy judgements (precision, recall, nDCG, etc.) from click
logs
● Learning to Rank - using relevancy judgements and machine learning to train
a relevance model
● Discovering misspellings, synonyms, acronyms, and related keywords
● Disambiguation of keyword phrases with multiple meanings
● Learning what’s important in your content
Examples of Reflected Intelligence
11. Term Documents
a doc1 [2x]
brown doc3 [1x] , doc5 [1x]
cat doc4 [1x]
cow doc2 [1x] , doc5 [1x]
… ...
once doc1 [1x], doc5 [1x]
over doc2 [1x], doc3 [1x]
the doc2 [2x], doc3 [2x],
doc4[2x], doc5 [1x]
… …
Document Content Field
doc1 once upon a time, in a land far,
far away
doc2 the cow jumped over the moon.
doc3 the quick brown fox jumped over
the lazy dog.
doc4 the cat in the hat
doc5 The brown cow said “moo”
once.
… …
What you SEND to Lucene/Solr:
How the content is INDEXED into
Lucene/Solr (conceptually):
The inverted index
13. Classic Lucene Relevancy Algorithm (now switched to BM25):
*Source: Solr in Action, chapter 3
Score(q, d) =
∑ ( tf(t in d) · idf(t)2 · t.getBoost() · norm(t, d) ) · coord(q, d) · queryNorm(q)
t in q
Where:
t = term; d = document; q = query; f = field
tf(t in d) = numTermOccurrencesInDocument ½
idf(t) = 1 + log (numDocs / (docFreq + 1))
coord(q, d) = numTermsInDocumentFromQuery / numTermsInQuery
queryNorm(q) = 1 / (sumOfSquaredWeights ½ )
sumOfSquaredWeights = q.getBoost()2 · ∑ (idf(t) · t.getBoost() )2
t in q
norm(t, d) = d.getBoost() · lengthNorm(f) · f.getBoost()
14. • Term Frequency: “How well a term describes a document?”
– Measure: how often a term occurs per document
• Inverse Document Frequency: “How important is a term overall?”
– Measure: how rare the term is across all documents
TF * IDF
*Source: Solr in Action, chapter 3
15. News Search : popularity and freshness drive relevance
Restaurant Search: geographical proximity and price range are critical
Ecommerce: likelihood of a purchase is key
Movie search: More popular titles are generally more relevant
Job search: category of job, salary range, and geographical proximity matter
TF * IDF of keywords can’t hold it’s own against good
domain-specific relevance factors!
That’s great, but what about domain-specific knowledge?
16. John lives in Boston but wants to move to New York or possibly another big city. He is
currently a sales manager but wants to move towards business development.
Irene is a bartender in Dublin and is only interested in jobs within 10KM of her location
in the food service industry.
Irfan is a software engineer in Atlanta and is interested in software engineering jobs at a
Big Data company. He is happy to move across the U.S. for the right job.
Jane is a nurse educator in Boston seeking between $40K and $60K
*Example from chapter 16 of Solr in Action
Consider what you know about users
21. Taxonomies / Entity Extraction
Identifying the important entities within your domain
22.
23. Building a Taxonomy of Entities
Many ways to generate this:
• Topic Modelling
• Clustering of documents
• Statistical Analysis of interesting phrases
-Word2Vec / Dice Conceptual Search
• Buy a dictionary (often doesn’t work for
domain-specific search problems)
• Generate a model of domain-specific phrases by
mining query logs for commonly searched phrases within the domain*
* K. Aljadda, M. Korayem, T. Grainger, C. Russell. "Crowdsourced Query Augmentation through Semantic Discovery of Domain-specific Jargon," in IEEE Big Data 2014.
24. Differentiating related terms
Synonyms: cpa => certified public accountant
rn => registered nurse
r.n. => registered nurse
Ambiguous Terms: driver => driver (trucking) ~80% likelihood
driver => driver (software) ~20% likelihood
Related Terms: r.n. => nursing, bsn
hadoop => mapreduce, hive, pig
Source: Trey Grainger, “Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disambiguation”, Bay Area Search Meetup, November 2015.
31. Probabilistic Query Parser
Goal: given a query, predict which
combinations of keywords should be
combined together as phrases
Example:
senior java developer hadoop
Possible Parsings:
senior, java, developer, hadoop
"senior java", developer, hadoop
"senior java developer", hadoop
"senior java developer hadoop”
"senior java", "developer hadoop”
senior, "java developer", hadoop
senior, java, "developer hadoop" Source: Trey Grainger, “Searching on Intent: Knowledge Graphs, Personalization,
and Contextual Disambiguation”, Bay Area Search Meetup, November 2015.
32. Input: senior hadoop developer java ruby on rails perl
Source: Trey Grainger, “Searching on Intent: Knowledge Graphs, Personalization, and Contextual Disambiguation”, Bay Area Search Meetup, November 2015.
33. Semantic Search Architecture – Query Parsing
Identification of phrases in queries using two steps:
1) Check a dictionary of known terms that is continuously
built, cleaned, and refined based upon common inputs from
interactions with real users of the system. The SolrTextTagger
works well for this.*
2) Also invoke a statistical phrase identifier to dynamically
identify unknown phrases using statistics from a corpus of data
(language model)
*K. Aljadda, M. Korayem, T. Grainger, C. Russell. "Crowdsourced Query Augmentation
through Semantic Discovery of Domain-specific Jargon," in IEEE Big Data 2014.
36. id: 1
job_title: Software Engineer
desc: software engineer at a
great company
skills: .Net, C#, java
id: 2
job_title: Registered Nurse
desc: a registered nurse at
hospital doing hard work
skills: oncology, phlebotemy
id: 3
job_title: Java Developer
desc: a software engineer or a
java engineer doing work
skills: java, scala, hibernate
field term postings list
doc pos
desc
a
1 4
2 1
3 1, 5
at
1 3
2 4
company 1 6
doing
2 6
3 8
engineer
1 2
3 3, 7
great 1 5
hard 2 7
hospital 2 5
java 3 6
nurse 2 3
or 3 4
registered 2 2
software
1 1
3 2
work
2 10
3 9
job_title java developer 3 1
… … … …
field doc term
desc
1
a
at
company
engineer
great
software
2
a
at
doing
hard
hospital
nurse
registered
work
3
a
doing
engineer
java
or
software
work
job_title 1
Software
Engineer
… … …
Terms-Docs Inverted IndexDocs-Terms Uninverted IndexDocuments
Source: Trey Grainger,
Khalifeh AlJadda, Mohammed
Korayem, Andries Smith.“The
Semantic Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any relationship
within a domain”. DSAA 2016.
Knowledge
Graph
37. Source: Trey Grainger,
Khalifeh AlJadda, Mohammed
Korayem, Andries Smith.“The
Semantic Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any relationship
within a domain”. DSAA 2016.
Knowledge
Graph
Set-theory View
Graph View
How the Graph Traversal Works
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
skill:
Java
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
Data Structure View
Java
Scala Hibernate
docs
1, 2, 6
docs
3, 4
Oncology
doc 5
38. Source: Trey Grainger,
Khalifeh AlJadda, Mohammed
Korayem, Andries Smith.“The
Semantic Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any relationship
within a domain”. DSAA 2016.
Knowledge
Graph
Multi-level Traversal
Data Structure View
Graph View
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
skill:
Java
skill: Java
skill: Scala
skill:
Hibernate
skill:
Oncology
doc 1
doc 2
doc 3
doc 4
doc 5
doc 6
job_title:
Software
Engineer
job_title:
Data
Scientist
job_title:
Java
Developer
……
Inverted Index
Lookup
Doc Values Index
Lookup
Doc Values Index
Lookup
Inverted Index
Lookup
Java
Java
Developer
Hibernate
Scala
Software
Engineer
Data
Scientist
has_related_job_title
has_related_job_title
39. Source: Trey Grainger, Khalifeh AlJadda, Mohammed Korayem, Andries Smith.“The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain”. DSAA 2016.
Knowledge
Graph
Scoring nodes in the Graph
Foreground vs. Background Analysis
Every term scored against it’s context. The more
commonly the term appears within it’s foreground
context versus its background context, the more
relevant it is to the specified foreground context.
countFG(x) - totalDocsFG * probBG(x)
z = --------------------------------------------------------
sqrt(totalDocsFG * probBG(x) * (1 - probBG(x)))
{ "type":"keywords”, "values":[
{ "value":"hive", "relatedness": 0.9765, "popularity":369 },
{ "value":"spark", "relatedness": 0.9634, "popularity":15653 },
{ "value":".net", "relatedness": 0.5417, "popularity":17683 },
{ "value":"bogus_word", "relatedness": 0.0, "popularity":0 },
{ "value":"teaching", "relatedness": -0.1510, "popularity":9923 },
{ "value":"CPR", "relatedness": -0.4012, "popularity":27089 } ] }
+
-
Foreground Query:
"Hadoop"
40. Source: Trey Grainger,
Khalifeh AlJadda, Mohammed
Korayem, Andries Smith.“The
Semantic Knowledge Graph: A
compact, auto-generated
model for real-time traversal
and ranking of any relationship
within a domain”. DSAA 2016.
Knowledge
Graph
Multi-level Graph Traversal with Scores
software engineer*
(materialized node)
Java
C#
.NET
.NET
Developer
Java
Developer
Hibernate
ScalaVB.NET
Software
Engineer
Data
Scientist
Skill
Nodes
has_related_skillStarting
Node
Skill
Nodes
has_related_skill Job Title
Nodes
has_related_job_title
0.90
0.88 0.93
0.93
0.34
0.74
0.91
0.89
0.74
0.89
0.780.72
0.48
0.93
0.76
0.83
0.80
0.64
0.61
0.780.55
45. How do we handle phrases with ambiguous meanings?
Example Related Keywords (representing multiple meanings)
driver truck driver, linux, windows, courier, embedded, cdl,
delivery
architect autocad drafter, designer, enterprise architect, java
architect, designer, architectural designer, data architect,
oracle, java, architectural drafter, autocad, drafter, cad,
engineer
… …
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.
46. Query Log Mining: Discovering ambiguous phrases
1) Classify users who ran each
search in the search logs
(i.e. by the job title
classifications of the jobs to
which they applied)
3) Segment the search term => related search terms list by classification,
to return a separate related terms list per classification
2) Create a probabilistic graphical model of those classifications mapped
to each keyword phrase.
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.
47. Semantic Knowledge Graph: Discovering ambiguous phrases
1) Exact same concept, but use
a document classification
field (i.e. category) as the first
level of your graph, and the
related terms as the second
level to which you traverse.
2) Has the benefit that you don’t need query logs to mine, but it will be representative
of your data, as opposed to your user’s intent, so the quality depends on how clean and
representative your documents are.
48. Disambiguated meanings (represented as term vectors)
Example Related Keywords (Disambiguated Meanings)
architect 1: enterprise architect, java architect, data architect, oracle, java, .net
2: architectural designer, architectural drafter, autocad, autocad drafter, designer,
drafter, cad, engineer
driver 1: linux, windows, embedded
2: truck driver, cdl driver, delivery driver, class b driver, cdl, courier
designer 1: design, print, animation, artist, illustrator, creative, graphic artist, graphic,
photoshop, video
2: graphic, web designer, design, web design, graphic design, graphic designer
3: design, drafter, cad designer, draftsman, autocad, mechanical designer, proe,
structural designer, revit
… …
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.
49. Using the disambiguated meanings
In a situation where a user searches for an ambiguous phrase, what information can we
use to pick the correct underlying meaning?
1. Any pre-existing knowledge about the user:
• User is a software engineer
• User has previously run searches for “c++” and “linux”
2. Context within the query:
User searched for windows AND driver vs. courier OR driver
3. If all else fails (and there is no context), use the most commonly occurring meaning.
driver 1: linux, windows, embedded
2: truck driver, cdl driver, delivery driver, class b driver, cdl, courier
Source: M. Korayem, C. Ortiz, K. AlJadda, T. Grainger. "Query Sense Disambiguation Leveraging Large Scale User Behavioral Data". IEEE Big Data 2015.
52. How to Measure Relevancy?
A B C
Retrieved
Documents
Related
Documents
Precision = B/A
Recall = B/C
Problem:
Assume Prec = 90% and Rec = 100% but assume the 10% irrelevant documents were ranked at
the top of the retrieved documents, is that OK?
53. Normalized Discounted Cumulative Gain
Rank Relevancy
3 0.95
1 0.70
2 0.60
4 0.45
Rank Relevancy
1 0.95
2 0.85
3 0.80
4 0.65
Ranking
Ideal
Given
• Position is
considered in
quantifying
relevancy.
• Labeled dataset
is required.
55. Learning to Rank (LTR)
● It applies machine learning techniques to discover the best combination of features that
provide best ranking.
● It requires labeled set of documents with relevancy scores for given set of queries
● Features used for ranking are usually more computationally expensive than the ones
used for matching
● It typically re-ranks a subset of the matched documents (e.g. top 1000)
58. LambdaMart Example
Source: T. Grainger, K. AlJadda. ”Reflected Intelligence: Evolving self-learning data systems". Georgia Tech, 2016
59. Obtaining Relevancy Judgements
• Typical Methodologies
1) Hire employees, contractors, or interns
-Pros:
Accuracy
-Cons:
Expensive
Not scalable (cost or man-power-wise)
Data Becomes Stale
• 2) Crowdsource
-Pros:
Less cost, more scalable
-Cons:
Less accurate
Data still becomes stale
Source: T. Grainger, K. AlJadda. ”Reflected Intelligence: Evolving self-learning data systems". Georgia Tech, 2016
60. Reflected Intelligence: Possible to infer relevancy judgements?
Rank Document ID
1 Doc1
2 Doc2
3 Doc3
4 Doc4
Query
Query
Doc1 Doc2 Doc3
0
1 1
Query
Doc1 Doc2 Doc3
1
0 0
Source: T. Grainger, K. AlJadda. ”Reflected Intelligence: Evolving self-learning data systems". Georgia Tech, 2016