This document discusses various internet search methods including keyword searches, field searches, Boolean logic searches, and miscellaneous search methods. Keyword searches involve entering a search string or phrase. Field searches allow searching within specific fields like title or domain. Boolean logic uses operators like AND and OR to refine searches. Miscellaneous methods support different languages, spell checking, phone number searches, and math/equivalents.
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
his talk will feature some of my recent research into the alternative uses for Solr facets and facet metadata. I will develop the idea that facets can be used to discover similarities between items and attributes in a search index, and show some interesting applications of this idea. A common takeaway is that using facets and facet metadata in non-conventional ways enables the semantic context of a query to be automatically tuned. This has important implications for user-centric and semantically focused relevance.
This document provides an overview of Vespa, an open source big data serving engine for storing, searching, ranking and organizing large amounts of data in real-time. It describes what Vespa is and what it can be used for, how to configure Vespa applications, and key concepts around search definitions, linguistics, ranking, tensors, and integrating TensorFlow models for ranking. The document also includes examples of code snippets and commands for testing Vespa using Docker.
This document discusses internet searching and online databases. It provides tips for effective internet searching, including using quotation marks, Boolean operators, and keywords. It also discusses four main steps search engines use to find information: crawling the web, indexing pages, ranking pages, and displaying results. The document then discusses online databases and provides tips for searching them such as using appropriate search terms, selecting relevant databases, starting with general searches, and using advanced search features.
Extending Solr: Building a Cloud-like Knowledge Discovery PlatformTrey Grainger
Trey Grainger discusses CareerBuilder's large-scale search platform built on Apache Solr. The platform handles over 150 search servers and indexes over 100 million documents in multiple languages and fields. Grainger describes CareerBuilder's approaches to multi-lingual analysis, custom scoring, and implementing a "Solr cloud" to make search capabilities easily accessible. He also discusses how the search platform is used for knowledge discovery and data analytics applications beyond just search.
1. The document discusses various techniques for searching online information, including using search engines, subject directories, and subject gateways.
2. It explains that search engines have huge databases but emphasize quantity over quality, while subject directories and subject gateways have smaller, more curated databases organized by subject.
3. Effective search strategies discussed include phrase searching, truncation, wildcards, Boolean operators, and setting limits to focus searches.
Interleaving, Evaluation to Self-learning Search @904LabsJohn T. Kane
Presented at Open Source Connections Haystack Relevance Conference on 904Labs' "Interleaving: from Evaluation to Self-Learning". 904Labs is the first to commercialize "Online Learning to Rank" as a state-of-art for technical Self-learning Search Ranking that automatically takes into account your customers human behaviors for personalized search results.
This document discusses various internet search methods including keyword searches, field searches, Boolean logic searches, and miscellaneous search methods. Keyword searches involve entering a search string or phrase. Field searches allow searching within specific fields like title or domain. Boolean logic uses operators like AND and OR to refine searches. Miscellaneous methods support different languages, spell checking, phone number searches, and math/equivalents.
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
his talk will feature some of my recent research into the alternative uses for Solr facets and facet metadata. I will develop the idea that facets can be used to discover similarities between items and attributes in a search index, and show some interesting applications of this idea. A common takeaway is that using facets and facet metadata in non-conventional ways enables the semantic context of a query to be automatically tuned. This has important implications for user-centric and semantically focused relevance.
This document provides an overview of Vespa, an open source big data serving engine for storing, searching, ranking and organizing large amounts of data in real-time. It describes what Vespa is and what it can be used for, how to configure Vespa applications, and key concepts around search definitions, linguistics, ranking, tensors, and integrating TensorFlow models for ranking. The document also includes examples of code snippets and commands for testing Vespa using Docker.
This document discusses internet searching and online databases. It provides tips for effective internet searching, including using quotation marks, Boolean operators, and keywords. It also discusses four main steps search engines use to find information: crawling the web, indexing pages, ranking pages, and displaying results. The document then discusses online databases and provides tips for searching them such as using appropriate search terms, selecting relevant databases, starting with general searches, and using advanced search features.
Extending Solr: Building a Cloud-like Knowledge Discovery PlatformTrey Grainger
Trey Grainger discusses CareerBuilder's large-scale search platform built on Apache Solr. The platform handles over 150 search servers and indexes over 100 million documents in multiple languages and fields. Grainger describes CareerBuilder's approaches to multi-lingual analysis, custom scoring, and implementing a "Solr cloud" to make search capabilities easily accessible. He also discusses how the search platform is used for knowledge discovery and data analytics applications beyond just search.
1. The document discusses various techniques for searching online information, including using search engines, subject directories, and subject gateways.
2. It explains that search engines have huge databases but emphasize quantity over quality, while subject directories and subject gateways have smaller, more curated databases organized by subject.
3. Effective search strategies discussed include phrase searching, truncation, wildcards, Boolean operators, and setting limits to focus searches.
Interleaving, Evaluation to Self-learning Search @904LabsJohn T. Kane
Presented at Open Source Connections Haystack Relevance Conference on 904Labs' "Interleaving: from Evaluation to Self-Learning". 904Labs is the first to commercialize "Online Learning to Rank" as a state-of-art for technical Self-learning Search Ranking that automatically takes into account your customers human behaviors for personalized search results.
This document discusses various techniques used in web search engines for indexing and ranking documents. It covers topics like inverted indices, stopword removal, stemming, relevance feedback, vector space models, and Bayesian inference networks. Web search engines prepare an index of keywords for documents and return ranked lists in response to queries by measuring similarities between query and document vectors based on term frequencies and inverse document frequencies.
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.
Implementing Conceptual Search in Solr using LSA and Word2Vec: Presented by S...Lucidworks
The document discusses implementing conceptual search in Solr. It describes how conceptual search aims to improve recall without reducing precision by matching documents based on concepts rather than keywords alone. It explains how Word2Vec can be used to learn related concepts from documents and represent words as vectors, which can then be embedded in Solr through synonym filters and payloads to enable conceptual search queries. This allows retrieving more relevant documents that do not contain the exact search terms but are still conceptually related.
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
This document discusses integrating faceted search capabilities from Apache Solr with structured semantic data from Ontopia. It describes faceted search and how Solr and Ontopia each address search and data representation. An approach is proposed to index Ontopia data in Solr to enable faceted search over semantic concepts. Examples and demos are referenced to illustrate faceted search interfaces.
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...Lucidworks
Trey Grainger gave a presentation about using Lucene/Solr as a self-learning data system through the concept of "reflected intelligence". The presentation covered topics like basic keyword search, taxonomies/entity extraction, query intent, and relevancy tuning. It proposed that by leveraging previous user data and interactions, new data and interactions could be better interpreted to continuously improve the system.
Directories and search engines are methods for finding information on the web. Directories organize web pages into a hierarchical structure on specific topics, classified by human editors. Search engines allow users to search large databases for pages matching search queries. There are general search engines covering many topics and specialized engines for particular topics like news or shopping. Metasearch engines send queries to multiple search engines at once. Effective search strategies include determining the best search engine based on interface, documentation, speed, database size, and relevancy scoring.
The Intent Algorithms of Search & Recommendation EnginesTrey Grainger
Trey Grainger gave a guest lecture on the intent algorithms of search and recommendation engines. He discussed how search engines work from basic keyword search to more advanced semantic search that incorporates user intent, personalization, and augmented intelligence. Grainger also covered how Lucidworks' products like Apache Solr and Fusion power search for many large companies through highly scalable and customizable search platforms.
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.
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.
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.
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.).
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!
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.
Information retrieval systems use indexes and inverted indexes to quickly search large document collections by mapping terms to their locations. Boolean retrieval uses an inverted index to process Boolean queries by intersecting postings lists to find documents that contain sets of terms. Key aspects of information retrieval systems include precision, recall, and ranking search results by relevance.
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
Exploring Direct Concept Search - Steve Rowe, LucidworksLucidworks
This document discusses direct concept search using word embeddings. It describes mapping query and index terms to vector representations in a conceptual space to improve recall by expanding queries with related concepts. Word2vec is used to generate 127-dimensional word embeddings from Wikipedia text. The embeddings are indexed in Lucene to enable nearest neighbor search. Queries are expanded by searching for terms nearest to query terms in the embedding space. While building high-dimensional point indexes is slow in Lucene, this approach demonstrates the potential of using word embeddings for query expansion in information retrieval.
This document provides information about Boolean logic and search operators. It discusses:
- George Boole and the development of Boolean algebra for logic operations.
- The basic Boolean logic operators of AND, OR, and NOT and how they combine search terms.
- Additional Google search techniques like phrase searching, truncation, and advanced search options.
This document discusses schema design concepts for document databases like MongoDB. It covers key concepts like embedding related data for optimal performance and flexible schemas. The document recommends embedding over referencing in most cases, especially for one-to-one and one-to-many relationships where related objects are often viewed together. Many-to-many relationships are more flexible, with embedding recommended for some use cases and referencing for others depending on the needs of the application. The goal is to design schemas that match how the application will use the data.
Cal Lane is known for intricate ironworks. She is renowned for her detailed iron embroidery and should be called the Iron Embroiderer. Her elaborate iron presentations are not well known by many photographers.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Present continuous feat. what’s the newsAldyansyah -
The document provides examples of how to ask and answer questions about what someone is doing at the present moment using present continuous tense. It gives the structure for open questions using "what are you doing now" as well as yes/no questions using "are you watching TV now." Examples of affirmative and negative responses are provided using contractions and verb phrases like "I'm sitting" and "I'm not watching TV." The purpose is to teach how to talk about actions happening now.
This document discusses various techniques used in web search engines for indexing and ranking documents. It covers topics like inverted indices, stopword removal, stemming, relevance feedback, vector space models, and Bayesian inference networks. Web search engines prepare an index of keywords for documents and return ranked lists in response to queries by measuring similarities between query and document vectors based on term frequencies and inverse document frequencies.
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.
Implementing Conceptual Search in Solr using LSA and Word2Vec: Presented by S...Lucidworks
The document discusses implementing conceptual search in Solr. It describes how conceptual search aims to improve recall without reducing precision by matching documents based on concepts rather than keywords alone. It explains how Word2Vec can be used to learn related concepts from documents and represent words as vectors, which can then be embedded in Solr through synonym filters and payloads to enable conceptual search queries. This allows retrieving more relevant documents that do not contain the exact search terms but are still conceptually related.
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
This document discusses integrating faceted search capabilities from Apache Solr with structured semantic data from Ontopia. It describes faceted search and how Solr and Ontopia each address search and data representation. An approach is proposed to index Ontopia data in Solr to enable faceted search over semantic concepts. Examples and demos are referenced to illustrate faceted search interfaces.
Reflected Intelligence - Lucene/Solr as a self-learning data system: Presente...Lucidworks
Trey Grainger gave a presentation about using Lucene/Solr as a self-learning data system through the concept of "reflected intelligence". The presentation covered topics like basic keyword search, taxonomies/entity extraction, query intent, and relevancy tuning. It proposed that by leveraging previous user data and interactions, new data and interactions could be better interpreted to continuously improve the system.
Directories and search engines are methods for finding information on the web. Directories organize web pages into a hierarchical structure on specific topics, classified by human editors. Search engines allow users to search large databases for pages matching search queries. There are general search engines covering many topics and specialized engines for particular topics like news or shopping. Metasearch engines send queries to multiple search engines at once. Effective search strategies include determining the best search engine based on interface, documentation, speed, database size, and relevancy scoring.
The Intent Algorithms of Search & Recommendation EnginesTrey Grainger
Trey Grainger gave a guest lecture on the intent algorithms of search and recommendation engines. He discussed how search engines work from basic keyword search to more advanced semantic search that incorporates user intent, personalization, and augmented intelligence. Grainger also covered how Lucidworks' products like Apache Solr and Fusion power search for many large companies through highly scalable and customizable search platforms.
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.
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.
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.
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.).
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!
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.
Information retrieval systems use indexes and inverted indexes to quickly search large document collections by mapping terms to their locations. Boolean retrieval uses an inverted index to process Boolean queries by intersecting postings lists to find documents that contain sets of terms. Key aspects of information retrieval systems include precision, recall, and ranking search results by relevance.
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
Exploring Direct Concept Search - Steve Rowe, LucidworksLucidworks
This document discusses direct concept search using word embeddings. It describes mapping query and index terms to vector representations in a conceptual space to improve recall by expanding queries with related concepts. Word2vec is used to generate 127-dimensional word embeddings from Wikipedia text. The embeddings are indexed in Lucene to enable nearest neighbor search. Queries are expanded by searching for terms nearest to query terms in the embedding space. While building high-dimensional point indexes is slow in Lucene, this approach demonstrates the potential of using word embeddings for query expansion in information retrieval.
This document provides information about Boolean logic and search operators. It discusses:
- George Boole and the development of Boolean algebra for logic operations.
- The basic Boolean logic operators of AND, OR, and NOT and how they combine search terms.
- Additional Google search techniques like phrase searching, truncation, and advanced search options.
This document discusses schema design concepts for document databases like MongoDB. It covers key concepts like embedding related data for optimal performance and flexible schemas. The document recommends embedding over referencing in most cases, especially for one-to-one and one-to-many relationships where related objects are often viewed together. Many-to-many relationships are more flexible, with embedding recommended for some use cases and referencing for others depending on the needs of the application. The goal is to design schemas that match how the application will use the data.
Cal Lane is known for intricate ironworks. She is renowned for her detailed iron embroidery and should be called the Iron Embroiderer. Her elaborate iron presentations are not well known by many photographers.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Present continuous feat. what’s the newsAldyansyah -
The document provides examples of how to ask and answer questions about what someone is doing at the present moment using present continuous tense. It gives the structure for open questions using "what are you doing now" as well as yes/no questions using "are you watching TV now." Examples of affirmative and negative responses are provided using contractions and verb phrases like "I'm sitting" and "I'm not watching TV." The purpose is to teach how to talk about actions happening now.
The document lists names of famous figures whose wax figures are displayed at Madame Tussauds wax museum in London. Some of the names mentioned include Wolfgang Amadeus Mozart, Vincent Van Gogh, Adolf Hitler, Angelina Jolie, Ayrton Senna, Michael Jackson, Charles Chaplin, Elvis Presley, Fidel Castro, Johnny Depp, John Wayne, Luciano Pavarotti, Mahatma Gandhi, Marilyn Monroe, Nicolas Cage, Sean Connery, Nelson Mandela, Margaret Thatcher, Prince Philip, Prince Charles, Lady Diana, Prince William, Prince Harry, Humphrey Bogart, Harrison Ford, David Beckham, Victoria Adams, Pope John Paul II,
Migrating from Laserfiche version 7 to version 8 as presented at the First Annual Statewide Laserfiche User Group Seminar held in Chesterfield, VA, by Roz Collins.
Este documento parece ser una presentación de PowerPoint que contiene varios títulos de obras de arte como "Metade & Metade", "Nascer do Sol", "Coração", "Arvore", "Caracol", y "Folha de Arvore". El documento también incluye enlaces para enviar la presentación a un amigo o suscribirse a recibir más presentaciones gratis por correo electrónico.
Laserfiche10 highlights- how the new features can benefit your mobile and wor...Christopher Wynder
Laserfiche 10 brings a lot of additional features for information management, workflow building and mobile content access. This slide deck provides the overview of how Laserfiche 10 can benefit clients looking to automate their processes.
Laserfiche 9.2 is almost ready for its debut! This latest update brings new ways to collaborate with colleagues and better integrates Laserfiche Forms and Laserfiche Mobile.
Livio Costantini Tovek presented on tools for accessing unstructured information including Tovek Tools, an enterprise search engine and analytical system. The presentation covered basic information retrieval concepts, the Verity Query Language, and Topic Trees which allow searching for concepts through a predefined hierarchical structure defined by subject experts. Topic Trees address the semantic ambiguity of text by establishing relationships between keywords and providing rules for evaluating documents.
The talk at TYPO3 DevDays 2015 in Nuremberg which explains the deep insights of how search works. TF-IDF algorithm, vector space model and how that is used in Lucene and therefore Solr and Elasticsearch.
The document compares and contrasts concept-based search using SySearch versus traditional keyword search. SySearch uses concepts extracted from documents and queries to understand information needs better than keyword matching alone. It ranks results by estimating the probability of relevance using a Bayesian approach rather than binary keyword matching. This allows it to better support natural language queries and retrieve more relevant results.
Overview of structured search technology. Using the structure of a document to create better search results for document search and retrieval.
How both search precision and recall is improved when the structure of a document is used.
How a keyword match in a title of a document can be used to boost the search score.
Case studies with the eXist native XML database.
Steps to set up a pilot project.
1. The document discusses various techniques for searching online information, including using search engines, subject directories, and subject gateways.
2. It explains that search engines have huge databases but emphasize quantity over quality, while subject directories and subject gateways have smaller, more curated databases organized by subject.
3. Effective search strategies discussed include phrase searching, truncation, wildcards, Boolean operators, and setting limits to refine searches.
Post-conference workshop at tcworld India 2012. Provides background on structured authoring, XML, planning your topics, writing topics, and writing for re-use.
The document discusses metadata repositories and their role in search and discovery. It provides examples of metadata repositories like library card catalogs and bibliographic databases. It describes how metadata repositories store metadata separately from content in order to standardize, share, and search metadata more easily. Commercial metadata repository products are also discussed, including their features and pricing.
Philly PHP: April '17 Elastic Search Introduction by Aditya BhamidpatiRobert Calcavecchia
Philly PHP April 2017 Meetup: Introduction to Elastic Search as presented by Aditya Bhamidpati on April 19, 2017.
These slides cover an introduction to using Elastic Search
Mike King examines the state of the SEO industry and talks through knowing information retrieval will help improve our understanding of Google. This talk debuted at MozCon
This document provides an overview of search domain basics. It discusses search goals and business models, structured versus unstructured content, common search terminologies, and technologies behind search. Key points include that most content is unstructured, SQL has limitations for search, sample search requests on Java classes, common search terminologies like stemming and stop words, differences between web search and enterprise search, and technologies used in search like indexing, caching, and parallelization.
A Novel Approach for Keyword extraction in learning objects using text miningIJSRD
Keyword extraction, concept finding are in learning objects is very important subject in today’s eLearning environment. Keywords are subset of words that contains the useful information about the content of the document. Keyword extraction is a process that is used to get the important keywords from documents. In this proposed System Decision tree algorithm is used for feature selection process using wordnet dictionary. WordNet is a lexical database of English which is used to find similarity from the candidate words. The words having highest similarity are taken as keywords.
The document discusses how traditional search options in Clarify systems were not intuitive and challenging to administer. It introduces Dovetail Seeker as a contemporary search engine that provides an easy and intuitive search experience across database content, attachments, files and more. Seeker can power search capabilities for agents, customers and other applications. It indexes data from the Clarify database and allows for advanced search options. The document demonstrates how Seeker improves upon legacy search and enables new search uses and integrations.
The document discusses various aspects of search engines and information retrieval systems. It covers topics like how search engines work, indexing content, query processing, relevance ranking, displaying search results and improving search quality. Some key points include how search engines convert information needs to queries, index content ahead of time, match query terms to indexed words, use relevance algorithms to sort results, and factors that influence search quality like content coverage, query clarity and system failures.
This document describes a research project that developed a client-side search module for a native XML website using XML technologies like XSLT, XPath, and DOM as well as JavaScript. The researcher created both a basic search and an advanced search utility. The basic search allowed searching across all text fields in a table using one text box, while the advanced search provided more options to search specific columns and refine searches. The project showed that an XML website can be effectively searched using a combination of XML technologies and JavaScript. The researcher plans to expand the search capabilities with more advanced regular expressions and server-side searching in the future.
This document discusses fuzzy type-ahead search in XML data. It proposes a method called TASX that allows users to search XML data interactively as they type query keywords, even with minor errors. TASX uses effective index structures and algorithms to achieve high search efficiency. It examines ranking functions and early termination techniques to progressively identify the top-k most relevant answers from the XML data in response to partial keyword queries. The experimental results show that TASX achieves high search efficiency and result quality for fuzzy type-ahead search in XML data.
Optimized Technique for Academic Search engine Optimizationkomalkumari103
Optimized technique for academic search engine optimization (ASEO) is proposed to improve ranking and exposure of academic articles. The proposed system uses a novel Lingo clustering algorithm to group similar entities, reducing the original dataset. Semantics and context of search queries are also considered to better determine relevance. Evaluation shows the technique improves precision, recall, and F1-score for academic search results. Future work could incorporate additional analysis like part-of-speech tagging and reference other paper sections to develop article impact factors.
Searching the all-time growing amount of global data and research results and retrieving only the relevant and up-to date information becomes more and more challenging. The amount of data including the big data issue in the IoT world makes it even more challenging. How can an employee keeping himself up to date and include the relevant information into his work and ensure his work includes the most relevant and latest information. Most search engines today provide some sort of semantic based answers to the queries you enter into the system. However, most search engines do not know you well enough to provide you with the best answers based on who you are, and what you really want for an answer. Here is today's challenge combined with the growing amount of data and media you find it in. The answer might be closer than you think.
Full text search allows searching large documents and databases by examining all words in stored documents to match search terms, rather than just searching for exact matches. It works by indexing documents, including word positions, and applying rules like removing common words. Queries can then search for keywords, use wildcards, and order results by relevance. Common full text search solutions include APIs like Lucene and Xapian, and servers like Sphinx and Solr, which are used by many large companies and websites to enable powerful searching of large amounts of text data.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
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Discover innovative uses of Revit in urban planning and design, enhancing city landscapes with advanced architectural solutions. Understand how architectural firms are using Revit to transform how processes and outcomes within urban planning and design fields look. They are supplementing work and putting in value through speed and imagination that the architects and planners are placing into composing progressive urban areas that are not only colorful but also pragmatic.
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Garments ERP Software in Bangladesh _ Pridesys IT Ltd.pdfPridesys IT Ltd.
Pridesys Garments ERP is one of the leading ERP solution provider, especially for Garments industries which is integrated with
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The Most Inspiring Entrepreneurs to Follow in 2024.pdfthesiliconleaders
In a world where the potential of youth innovation remains vastly untouched, there emerges a guiding light in the form of Norm Goldstein, the Founder and CEO of EduNetwork Partners. His dedication to this cause has earned him recognition as a Congressional Leadership Award recipient.
Anny Serafina Love - Letter of Recommendation by Kellen Harkins, MS.AnnySerafinaLove
This letter, written by Kellen Harkins, Course Director at Full Sail University, commends Anny Love's exemplary performance in the Video Sharing Platforms class. It highlights her dedication, willingness to challenge herself, and exceptional skills in production, editing, and marketing across various video platforms like YouTube, TikTok, and Instagram.
IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
Unveiling the Dynamic Personalities, Key Dates, and Horoscope Insights: Gemin...my Pandit
Explore the fascinating world of the Gemini Zodiac Sign. Discover the unique personality traits, key dates, and horoscope insights of Gemini individuals. Learn how their sociable, communicative nature and boundless curiosity make them the dynamic explorers of the zodiac. Dive into the duality of the Gemini sign and understand their intellectual and adventurous spirit.
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