Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
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.
Slides from Enterprise Search & Analytics Meetup @ Cisco Systems - http://www.meetup.com/Enterprise-Search-and-Analytics-Meetup/events/220742081/
Relevancy and Search Quality Analysis - By Mark David and Avi Rappoport
The Manifold Path to Search Quality
To achieve accurate search results, we must come to an understanding of the three pillars involved.
1. Understand your data
2. Understand your customers’ intent
3. Understand your search engine
The first path passes through Data Analysis and Text Processing.
The second passes through Query Processing, Log Analysis, and Result Presentation.
Everything learned from those explorations feeds into the final path of Relevancy Ranking.
Search quality is focused on end users finding what they want -- technical relevance is sometimes irrelevant! Working with the short head (very frequent queries) has the most return on investment for improving the search experience, tuning the results, for example, to emphasize recent documents or de-emphasize archive documents, near-duplicate detection, exposing diverse results in ambiguous situations, using synonyms, and guiding search via best bets and auto-suggest. Long-tail analysis can reveal user intent by detecting patterns, discovering related terms, and identifying the most fruitful results by aggregated behavior. all this feeds back into the regression testing, which provides reliable metrics to evaluate the changes.
By merging these insights, you can improve the quality of the search overall, in a scalable and maintainable fashion.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
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.
Slides from Enterprise Search & Analytics Meetup @ Cisco Systems - http://www.meetup.com/Enterprise-Search-and-Analytics-Meetup/events/220742081/
Relevancy and Search Quality Analysis - By Mark David and Avi Rappoport
The Manifold Path to Search Quality
To achieve accurate search results, we must come to an understanding of the three pillars involved.
1. Understand your data
2. Understand your customers’ intent
3. Understand your search engine
The first path passes through Data Analysis and Text Processing.
The second passes through Query Processing, Log Analysis, and Result Presentation.
Everything learned from those explorations feeds into the final path of Relevancy Ranking.
Search quality is focused on end users finding what they want -- technical relevance is sometimes irrelevant! Working with the short head (very frequent queries) has the most return on investment for improving the search experience, tuning the results, for example, to emphasize recent documents or de-emphasize archive documents, near-duplicate detection, exposing diverse results in ambiguous situations, using synonyms, and guiding search via best bets and auto-suggest. Long-tail analysis can reveal user intent by detecting patterns, discovering related terms, and identifying the most fruitful results by aggregated behavior. all this feeds back into the regression testing, which provides reliable metrics to evaluate the changes.
By merging these insights, you can improve the quality of the search overall, in a scalable and maintainable fashion.
"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.
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
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.
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.
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.
In this talk we outline some of the key challenges in text analytics, describe some of Endeca's current research work in this area, examine the current state of the text analytics market and explore some of the prospects for the future.
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.
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.
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.
Best Practices for Large Scale Text Mining ProcessingOntotext
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
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.
We research hierarchy of topics extracted from documents (news, publications, discussions etc.).
Our system is targeted at data researchers.
It provides:
-Trend tracking
-Similar and related topics detection
-Topic segmentation, which aims to solve information overload (http://mlvl.github.io/Hierarchie/) problem
The topic model we use is not a collection of tags but is the combination of NLP + statistical analysis.
Introduction to Enterprise Search. A two hour class to introduce Enterprise Search. It covers:
The problems enterprise search can solve
History of (web) search
How we search and find?
Current state of Enterprise Search + stats
Technical concept
Information quality
Feedback cycle
Five dimensions of Findability
"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.
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
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.
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.
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.
In this talk we outline some of the key challenges in text analytics, describe some of Endeca's current research work in this area, examine the current state of the text analytics market and explore some of the prospects for the future.
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.
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.
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.
Best Practices for Large Scale Text Mining ProcessingOntotext
Q&A:
NOW facilitates semantic search by having annotations attached to search strings. How compolex does that get, e.g. with wildcards between annotated strings?
NOW’s searchbox is quite basic at the moment, but still supports a few scenarios.
1. Pure concept/faceted search - search for all documents containing a concept or where a set of concepts are co-occurring. Ranking is based on frequence of occurrence.
2. Concept/faceted + Full Text search - search for both concepts and particular textual term of phrase.
3. Full text search
With search, pretty much anything can be done to customise it. For the NOW showcase we’ve kept it fairly simple, as usually every client has a slightly different case and wants to tune search in a slightly different direction.
The search in NOW is faceted which means that you search with concepts (facets) and you retrieve all documents which contain mentions of the searched concept. If you search by more than one facet the engine retrieves documents which contain mentions of both concepts but there is no restriction that they occur next to each other.
Is the tagging service expandable (say with custom ontologies)? also is it a something you offer as a service? it is unclear to me from the website.
The TAG service is used for demonstration purposes only. The models behind it are trained for annotating news articles. The pipeline is customizable for every concrete scenario, different domains and entities of interest. You can access several of our pipelines as a service through the S4 platform or you can have them hosted as an on premise solution. In some cases our clients want domain adaptation or improvements in particular area, or to tag with their internal dataset - in this case we offer again an on premise deployment and also a managed service hosted on our hardware.
Hdoes your system accomodate cluster analysis using unsupervised keyword/phrase annotation for knowledge discovery?
As much as the patterns of user behaviour are also considered knowledge discovery we employ these for suggesting related reads. Apart from these we have experience tailoring custom clustering pipelines which also rely on features like keyword and named entities.
For topic extraction how many topics can we extract? from twitter corpus wgat csn we infer?
For topic extraction we have determined that we obtain best results when suggesting 3 categories. These are taken from IPTC but only the uppermost levels which are less than 20.
The twitter corpus example is from a project Ontotext participates in called Pheme. The goal of the project is to detect rumours and to check their veracity, thus help journalists in their hunt for attractive news.
Do you provide Processing Resources and JAPE rules for GATE framework and that can be used with GATE embedded?
We are contributing to the GATE framework and everything which has been wrapped up as PRs has been included the corresponding GATE distributions.
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.
We research hierarchy of topics extracted from documents (news, publications, discussions etc.).
Our system is targeted at data researchers.
It provides:
-Trend tracking
-Similar and related topics detection
-Topic segmentation, which aims to solve information overload (http://mlvl.github.io/Hierarchie/) problem
The topic model we use is not a collection of tags but is the combination of NLP + statistical analysis.
Introduction to Enterprise Search. A two hour class to introduce Enterprise Search. It covers:
The problems enterprise search can solve
History of (web) search
How we search and find?
Current state of Enterprise Search + stats
Technical concept
Information quality
Feedback cycle
Five dimensions of Findability
Enterprise Search is still considered as a one-time IT Project in most cases, although it should rather be a Business Process, with well-defined lifecycle, metrics and analytics. Measuring and controlling success is very challenging – in most cases, it has to be a string collaboration between internal and external experts. In this session, I’m introducing several roles of this process as well as useful metrics and best practices. Attendees will get a practical plan for quality management of their Enterprise Search solution as the key takeaway.
Introduction to enterprise search for intranets and websitesKristian Norling
An introduction to Enterprise Search. A two hour course to introduce Enterprise Search by Kristian Norling. This is a class I love to do, so if you have interest in it for on premise/in-house class or at a conference or such, please contact me.
The course covers:
- Problems for enterprise search to solve.
- Web Search
- How we search and find?
- Current state of Enterprise Search, including stats
- Technical concept
- Information quality and metadata
- Feedback cycle
- Five dimensions of Findability
Contextual search is a form of optimizing web-based search results based on context provided by the user and the computer being used to enter the query.Contextual search services differ from current search engines based on traditional information retrieval that return lists of documents based on their relevance to the query. Rather, contextual search attempts to increase the precision of results based on how valuable they are to individual users.
KMWorld, Enterprise Search and Discovery 2019
Congratulations! You’ve just been given the responsibility for search at your organization! Perhaps there is a new initiative to improve search, or perhaps the previous search manager mysteriously disappeared. In any case, you’ve discovered that search is a deceptively tricky domain, and that the expectations of many of your stakeholders are difficult to meet or even to define. This workshop provides an orientation and exposure to the key issues, effective processes, and technology—independent of what brand of search engine you use. It provides lay-of-the-land information and approaches to get you off to a good start. Topics include getting started and where to find practical guidance in search management; kinds of tasks and roles involved in managing search; building a cross-functional team; assessing the current state of search; establishing a vision and creating a findability strategy; getting stakeholders together and constructively involved; discovering and managing expectations; top misconceptions about search and how to educate your organization; top five and next five tools and techniques for improving search; updates and improvements; and measuring search: KPIs, tools, and techniques for internal search engine optimization. If you have been in the search manager’s role for a while but feel like you are missing a grounding in successful practices and management techniques, this workshop is still useful.
Findability is the ease of which someone can locate the information they want. Often, it is confused with search – but search is just one method of achieving findability. Search allows people to enter in words that they hope are contained in the content they want to retrieve. Findability includes any method of locating this content, including but not limited to searching. Pingar DiscoveryOne improves findability.
How Text Analytics Increases Search RelevanceZanda Mark
To learn more visit: http://pingar.com/discoveryone/
Findability is the ease of which someone can locate the information they want. Often, it is confused with search – but search is just one method of achieving findability. Search allows people to enter in words that they hope are contained in the content they want to retrieve. Findability includes any method of locating this content, including but not limited to searching. Pingar DiscoveryOne improves findability.
Organizations are beginning to recognize that search is not a stand-alone technology or application, but must be integrated with business processes and corporate objectives as a key infrastructure component.
Why? Providing enriched metadata to the search engine index significantly improves search applications, eDiscovery, FOIA requests, and collaboration.
In this webinar COMPU-DATA International and Concept Searching will demonstrate their combined offering that uses unique, language independent technology and integrated enterprise metadata repository management, to deliver intelligent metadata enabled search.
What you will learn about during this session:
• How our innovative technology delivers both high precision and high recall, using industry unique compound term processing
• How to accomplish federated search as content is created or ingested
• How to enable true concept based searching
• How to eliminate end user tagging
• How to integrate the combined solution with any search engine including SharePoint, the former FAST products, Google Search Appliance, IBM Vivisimo, and Solr
• How the combined solution can be extended to address records identification, protection of privacy information, migration, and text analytics with the same technology
• Benefit from industry-specific use cases:
• Developing a powerful search solution for the US Army, creating easy access to millions of records, with an integrated solution to consolidate many data sources, accessing high volumes of data
• Solving search, migration, records management, and data privacy challenges to manage the intranet for a global company which designs, manufactures, and distributes appliances to more than 70 countries
Content without access is worthless. Searching for company data has mostly been a poor experience. This needs to change…
This slidedeck is from a webinar performed on september 19th 2018, presented by Joel Oleson and Maarten Visser were they discussed the current issues with Enterprise Search and look at what Microsoft is doing in this space. Besides best practices and tips they will also look at the Meetroo Entree product and how it helps organisations to improve the Search Experience.
This session will discuss building an effective search and information architecture strategy for SharePoint.
One of the reasons many SharePoint implementations fail to meet user expectations is the lack of investment in its underlying information architecture. Some organizations see SharePoint as an out-of-the-box solution that they can simply plug in and throw content into, but it requires as much thought and effort around data structure, organizational principles, and search configuration as any portal or intranet.
This call will discuss building an effective search and information architecture strategy for SharePoint, including such topics as:
• Building a search & IA vision
• Requirements gathering & use cases
• Implementation strategy & approaches
• The future of SharePoint search
Enterprise search, once a hot topic, now faces new challenges with the adoption of hybrid and cloud environments. In SharePoint and Office 365, search isn’t equal, and before jumping on the Office 365 band wagon, organizations need to know as much as they can about potential challenges and pitfalls. Even in a SharePoint stand-alone environment, the consumption of content is forcing a renewed look at security, information lifecycle management, and non-compliance. Join us on Tuesday, October 28th, at 11:30am – 12:30pm EDT for this webinar packed with information on how to address search issues in SharePoint stand-alone, Office 365 hybrid, or cloud-only environments.
If you’re not sure how you would answer any of the below questions below, then this webinar is for you:.
• Does an enterprise search engine that provides the same transparency both on-premise and in the cloud matter to you? Does it matter to your users?
• With the huge push for Office 365, is enterprise search getting lost?
• Will the announcement of Delve push you over the edge in adopting Office 365? Why?
• What about managing content in the cloud? Do you have any reservations about security,or records, and what your users are doing in the cloud? (Bet you’ll be surprised.)
• What about migration? What goes where?
• How will you know what’s on your users’ OneDrives? Is DLP enough?
• Is your SharePoint search good enough? Have you calculated the ROI you can gain by improving it?
• How are you going to manage the ever increasing consumption of organizational content?
Presented by Mikael Wendelius (Findwise) & Jeff Fried (BA Insight) at Intranätverk 2016: Stockholm, 20 October.
Intranets and hybrid search – use search to bridge the “great divide” so your users find what they are looking for!
Jeff Fried and Mikael Wendelius show how hybrid search can drive a great intranet experience. They demonstrate this using SharePoint and Office 365, and illustrate the benefits and pitfalls with case studies.
Search Strategy for Enterprise SharePoint 2013 - Vancouver SharePoint SummitJoel Oleson
The Four Pillars of Search really help you focus your search planning. In this session we dig into the context, content, metadata and UX or user experience that really matter. We also dig into a variety of publicly accessible SharePoint 2013 real world search pages to demonstrate the value.
Similar to How to be successful with search in your organisation (20)
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16. Search:
User – Context – Content
Users:
Information needs, audience types, expertise,
tasks
[Who needs the Information]
Content:
Document types, objects, structure, attributes,
metadata
[How to describe the information]
Context:
Business models & goals, corporate culture,
resources
[How the information is used]
Users
ContextContent
17. Content Analysis
Locations of content
State and quality of content
Whether it will be migrated
Metadata
Organizing principles that can be reused
IS-ness and ABOUT-ness of content
Ownership of content
Content lifecycles
Relative value of content
39. Search Quality Metrics
Number of Searches
Number of successful
Searches
Number of Searches with
Zero results
Results with no click
Most popular Queries Results clicked the most
Who’s performing the
most searches
(users/departments/roles)
Correlated searches
Speed / latency Total distinct search terms
Total distinct words Trends and historical
data/charts
40. What if I cannot find a document?
The document does not exist.
The document does exist - but is not in the search index.
The document does exist, is indexed – but I don’t have permission to
read it.
42. Metrics
Get new staff to quickly start
using search to explore and
find resources.
How many new staff use
search in the first 2
months.
# of search
sessions/month for the
the new staff segment.
43. Proof of Concept (POC)
Initial requirements & conditions
Limited scope
• Functionality
• User Experience
• Content
• Users
• Etc.
Limited time frame
46. Governance
The set of
policies, roles, responsibilities and processes
that guide, direct and control
how an organization’s business divisions and IT teams
cooperate
to achieve business goals.
49. Search Governance Plan
Managing scopes of Search
Defining content sources
Defining and managing content types
Defining and managing metadata
Identifying promoted results
Using third-party tools or custom functionality to extend search
Search Analytics
Training end users