The PoolParty Semantic Classifier is a component of the Semantic Suite, which makes use of machine learning in combination with Knowledge Graphs.
We discuss the potential of the fusion of machine learning, neuronal networks, and knowledge graphs based on use cases and this concrete technology offering.
We introduce the term 'Semantic AI' that refers to the combined usage of various AI methods.
My talk at Brighton SEO in September 2017. I cover examples of how to use NLP APIs such as IBM Watson Natural Language Understanding and Microsoft Azure's Textual Analysis API. Covers specific examples of how to use and get familiar with these APIs, and where SEO agencies and practitioners should focus their efforts.
TransferWise is hiring in SEO: http://grnh.se/7fdiup1
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
As search evolves, so does optimization. Search results are less about phrases (combinations of words and letters) and more about topics (semantic meanings and entities). So a smart content marketer optimizes for “things, not strings.”
But what exactly does this mean for the writer? This presentation covers five specific actions we take as content marketers to make sure that your marketing is aligned with the future of SEO.
Learn how to:
Find clues into what topics are semantically linked to each other (Research)
Target topics, not just phrases, through writing (Semantic Search)
Incorporate natural language into your content (Voice Search)
Make visitors happy in ways that make Google happy (User Interaction Signals)
You're about to learn the step-by-step process for each of the specific actions that will future-proof your search engine rankings.
How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
My talk at Brighton SEO in September 2017. I cover examples of how to use NLP APIs such as IBM Watson Natural Language Understanding and Microsoft Azure's Textual Analysis API. Covers specific examples of how to use and get familiar with these APIs, and where SEO agencies and practitioners should focus their efforts.
TransferWise is hiring in SEO: http://grnh.se/7fdiup1
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
As search evolves, so does optimization. Search results are less about phrases (combinations of words and letters) and more about topics (semantic meanings and entities). So a smart content marketer optimizes for “things, not strings.”
But what exactly does this mean for the writer? This presentation covers five specific actions we take as content marketers to make sure that your marketing is aligned with the future of SEO.
Learn how to:
Find clues into what topics are semantically linked to each other (Research)
Target topics, not just phrases, through writing (Semantic Search)
Incorporate natural language into your content (Voice Search)
Make visitors happy in ways that make Google happy (User Interaction Signals)
You're about to learn the step-by-step process for each of the specific actions that will future-proof your search engine rankings.
How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
Slawski New Approaches for Structured Data:Evolution of Question Answering Bill Slawski
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.
Document Management in SharePoint without folders - Introduction to MetadataGregory Zelfond
Step-by-Step Guide to Document Management
in SharePoint. Part I – Introduction to Metadata
What’s wrong with Folders?
Intro to Metadata
Step-by-Step on how to setup SharePoint Metadata
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
This is my presentation for the Azure Advent Calendar initiative by Azure MVPs in which I explain how Azure Cognitive Search works and can perform optimal information findings from an existing data source (a website, in this case).
Lake Database Database Template Map Data in Azure Synapse AnalyticsErwin de Kreuk
Database templates in Synapse Analytics are blueprints which can be used by organizations to plan, architect and design solutions.
How can we use these Database Templates in a day-to-day business, in order to speed up to automate this process?
Map data tool can help us with that
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...Koray Tugberk GUBUR
Semantic Search Engines can understand human language to analyze the need behind a query. Instead of focusing, string, or word matching, a semantic search engine focuses on concepts, intents, and relations of named entities. Taxonomy, ontology, onomastics, semantic role labeling, relation detection, lexical semantics, entity extraction, recognition, resolution can be used by semantic search engines. In this PDF file, semantic search engines' evolution will be processed based on Google Search Engine's research papers, patents, and official announcements. From 1998 to 20021, search's and search engines' evolution, from strings to things, from phrases to entities will be told along with query processing, and parsing methodology changes.
As opposed to lexical search, semantic searching searches for meaning, not meaningless matches of the query words. Semantic search attempts to increase the relevancy of results by understanding searchers' intents and the context of terms in the searchable dataspace, whether online or within a closed system. The right semantic search content is a blend of natural language, focuses on the intent of the user, and considers other topics the user may be interested in.
Ontologies, XML, and other structured data sources can be used to retrieve knowledge using semantic search according to some authors. The use of such technologies provides a mechanism for creating formal expressions of domain knowledge that are highly expressive and may allow the user to express more detailed intent during query processing.
Ed will be reviewing the continued importance of displaying EAT throughout your website, whilst also discussing how the wider SEO community has looked at the acronym backwards – with Trust being the most important element.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
SharePoint as an Intranet Portal for BusinessRashminPopat2
SharePoint has been one of Microsoft's frontrunners in aiding digital business growth. Companies looking to install this platform as their intranet portal may want to know more about it before investing.
Crafting Expertise, Authority and Trust with Entity-Based Content Strategy - ...Jamie Indigo
At SMXL, I presented a talk about crafting effective, authoritative content by understanding entities. People, places, objects, and ideas have facets. Human users have unique perspectives and their language changes as their relationship to an entity changes. It's time we stop chasing keywords-- a byproduct of search intent-- in favor of strategic entity-based strategy.
This deck includes insights into how to access the data behind Google's knowledge graph, use external links to boost the search engine's understanding, and ways to become an authoritative and trusted source.
A Guide to Properly Migrating a CMS: The Rainbow EditionKristina Azarenko
In this talk, I will concentrate on a particular migration type, switching content management systems — as this specific type has its own challenges. I will show you what you need to know before moving to another CMS, what issues you will face, and the exact steps to overcome these issues.
Whilst passage indexing may seem like a small tweak to search ranking, it is potentially much more symptomatic of the beginning of a fundamental shift in the way that search engines understand unstructured content, determine relevance in natural language, and rank efficiently and effectively.
It could also be a means of assessing overall quality of content and a means of dynamic index pruning. We will look at the landscape, and also provide some takeaways for brands and business owners looking to improve quality in unstructured content overall in this fast changing landscape.
Migration Best Practices - Search Y 2019, ParisBastian Grimm
My talk from SEARCHY 2019 in Paris covering best practices on how to successfully navigate through the various types of migrations (protocol migrations, frontend migrations, website migration, cms migration, etc.) from an SEO perspective - mainly focussing on all things technical SEO.
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
Linked Data has become a broadly adopted approach for information management and data management not only by government organisations but also more and more by various industries.
Enterprise linked data tackles several challenges like the improvement of information retrieval tools or the integration of distributed data silos. Enterprises understand better and better why their information management should not be limited by organisational boundaries but should rather consider to integrate and link information from different spheres like the public internet, government organisations, professional information providers, customers and even suppliers.
On the other hand, enterprise IT architects still tend to pull down the shutters wherever possible. The continuation of the success of the Semantic Web doesn't seem to be limited by technical barriers anymore but rather by people's mindsets of intranets being strictly cut off from other information sources.
In this talk I will throw new light on the reasons why metadata is key for professional information management, and why W3C's semantic web standards are so important to reduce costs of data management through economies of scale. I will discuss from a multi-stakeholder perspective several use cases for the industrialization of semantic technologies and linked data.
Slawski New Approaches for Structured Data:Evolution of Question Answering Bill Slawski
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.
Document Management in SharePoint without folders - Introduction to MetadataGregory Zelfond
Step-by-Step Guide to Document Management
in SharePoint. Part I – Introduction to Metadata
What’s wrong with Folders?
Intro to Metadata
Step-by-Step on how to setup SharePoint Metadata
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
This is my presentation for the Azure Advent Calendar initiative by Azure MVPs in which I explain how Azure Cognitive Search works and can perform optimal information findings from an existing data source (a website, in this case).
Lake Database Database Template Map Data in Azure Synapse AnalyticsErwin de Kreuk
Database templates in Synapse Analytics are blueprints which can be used by organizations to plan, architect and design solutions.
How can we use these Database Templates in a day-to-day business, in order to speed up to automate this process?
Map data tool can help us with that
Semantic Search Engine: Semantic Search and Query Parsing with Phrases and En...Koray Tugberk GUBUR
Semantic Search Engines can understand human language to analyze the need behind a query. Instead of focusing, string, or word matching, a semantic search engine focuses on concepts, intents, and relations of named entities. Taxonomy, ontology, onomastics, semantic role labeling, relation detection, lexical semantics, entity extraction, recognition, resolution can be used by semantic search engines. In this PDF file, semantic search engines' evolution will be processed based on Google Search Engine's research papers, patents, and official announcements. From 1998 to 20021, search's and search engines' evolution, from strings to things, from phrases to entities will be told along with query processing, and parsing methodology changes.
As opposed to lexical search, semantic searching searches for meaning, not meaningless matches of the query words. Semantic search attempts to increase the relevancy of results by understanding searchers' intents and the context of terms in the searchable dataspace, whether online or within a closed system. The right semantic search content is a blend of natural language, focuses on the intent of the user, and considers other topics the user may be interested in.
Ontologies, XML, and other structured data sources can be used to retrieve knowledge using semantic search according to some authors. The use of such technologies provides a mechanism for creating formal expressions of domain knowledge that are highly expressive and may allow the user to express more detailed intent during query processing.
Ed will be reviewing the continued importance of displaying EAT throughout your website, whilst also discussing how the wider SEO community has looked at the acronym backwards – with Trust being the most important element.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
SharePoint as an Intranet Portal for BusinessRashminPopat2
SharePoint has been one of Microsoft's frontrunners in aiding digital business growth. Companies looking to install this platform as their intranet portal may want to know more about it before investing.
Crafting Expertise, Authority and Trust with Entity-Based Content Strategy - ...Jamie Indigo
At SMXL, I presented a talk about crafting effective, authoritative content by understanding entities. People, places, objects, and ideas have facets. Human users have unique perspectives and their language changes as their relationship to an entity changes. It's time we stop chasing keywords-- a byproduct of search intent-- in favor of strategic entity-based strategy.
This deck includes insights into how to access the data behind Google's knowledge graph, use external links to boost the search engine's understanding, and ways to become an authoritative and trusted source.
A Guide to Properly Migrating a CMS: The Rainbow EditionKristina Azarenko
In this talk, I will concentrate on a particular migration type, switching content management systems — as this specific type has its own challenges. I will show you what you need to know before moving to another CMS, what issues you will face, and the exact steps to overcome these issues.
Whilst passage indexing may seem like a small tweak to search ranking, it is potentially much more symptomatic of the beginning of a fundamental shift in the way that search engines understand unstructured content, determine relevance in natural language, and rank efficiently and effectively.
It could also be a means of assessing overall quality of content and a means of dynamic index pruning. We will look at the landscape, and also provide some takeaways for brands and business owners looking to improve quality in unstructured content overall in this fast changing landscape.
Migration Best Practices - Search Y 2019, ParisBastian Grimm
My talk from SEARCHY 2019 in Paris covering best practices on how to successfully navigate through the various types of migrations (protocol migrations, frontend migrations, website migration, cms migration, etc.) from an SEO perspective - mainly focussing on all things technical SEO.
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
Linked Data has become a broadly adopted approach for information management and data management not only by government organisations but also more and more by various industries.
Enterprise linked data tackles several challenges like the improvement of information retrieval tools or the integration of distributed data silos. Enterprises understand better and better why their information management should not be limited by organisational boundaries but should rather consider to integrate and link information from different spheres like the public internet, government organisations, professional information providers, customers and even suppliers.
On the other hand, enterprise IT architects still tend to pull down the shutters wherever possible. The continuation of the success of the Semantic Web doesn't seem to be limited by technical barriers anymore but rather by people's mindsets of intranets being strictly cut off from other information sources.
In this talk I will throw new light on the reasons why metadata is key for professional information management, and why W3C's semantic web standards are so important to reduce costs of data management through economies of scale. I will discuss from a multi-stakeholder perspective several use cases for the industrialization of semantic technologies and linked data.
Semantic Artificial Intelligence is the fusion of various types of AI, incl. symbolic AI, reasoning, and machine learning techniques like deep learning. At the same time, Semantic AI has a strong focus on data management and data governance. With the 'wedding' of various AI techniques new promises are made, but also fundamental approaches like 'Explainable AI (XAI)', knowledge graphs, or Linked Data are more strongly focused.
Leveraging Knowledge Graphs in your Enterprise Knowledge Management SystemSemantic Web Company
Knowledge graphs and graph-based data in general are becoming increasingly important for addressing various data management challenges in industries such as financial services, life sciences, healthcare or energy.
At the core of this challenge is the comprehensive management of graph-based data, ranging from taxonomy to ontology management to the administration of comprehensive data graphs along with a defined governance framework. Various data sources are integrated and linked (semi) automatically using NLP and machine learning algorithms. Tools for securing high data quality and consistency are an integral part of such a platform.
PoolParty 7.0 can now handle a full range of enterprise data management tasks. Based on agile data integration, machine learning and text mining, or ontology-based data analysis, applications are developed that allow knowledge workers, marketers, analysts or researchers a comprehensive and in-depth view of previously unlinked data assets.
At the heart of the new release is the PoolParty GraphEditor, which complements the Taxonomy, Thesaurus, and Ontology Manager components that have been around for some time. All in all, data engineers and subject matter experts can now administrate and analyze enterprise-wide and heterogeneous data stocks with comfortable means, or link them with the help of artificial intelligence.
Sara Nash and Urmi Majumder, Principal Consultants at Enterprise Knowledge, presented on April 19, 2023 at KM World in Washington D.C. on the topic of Scaling Knowledge Graph Architectures with AI.
In this presentation, Sara and Urmi defined a Knowledge Graph architecture and reviewed how AI can support the creation and growth of Knowledge Graphs. Drawing from their experience in designing enterprise Knowledge Graphs based on knowledge embedded in unstructured content, Sara and Urmi defined approaches for entity and relationship extraction depending on Enterprise AI maturity and highlighted other key considerations to incorporate AI capabilities into the development of a Knowledge Graph.
View presentation below in order to learn about how:
Assess entity and relationship extraction readiness according to EK’s Extraction Maturity Spectrum and Relationship Extraction Maturity Spectrum.
Utilize knowledge extraction from content to gather important insights into organizational data.
Extract knowledge with three approaches:
RedEx Rule, Auto-Classification Rule, Custom ML Model
Examine key factors such as how to leverage SMEs, iterate AI processes, define use cases, and invest in establishing robust AI models.
Benefiting from Semantic AI along the data life cycleMartin Kaltenböck
Slides of 1 hour session of Martin Kaltenböck (CFO and Managing Partner of Semantic Web Company / PoolParty Software Ltd) on 19 March 2019 in Boston, US at the Enterprise Data World 2019, with its title: Benefiting from Semantic AI along the data life cycle.
Sara Mae O’Brien Scott and Tatiana Baquero Cakici, Senior Consultants at Enterprise Knowledge (EK), presented “AI Fast Track to Search-Focused AI Solutions” at the Information Architecture Conference (IAC24) that took place on April 11, 2024 in Seattle, WA.
In their presentation, O’Brien-Scott and Cakici focused on what Enterprise AI is, why it is important, and what it takes to empower organizations to get started on a search-based AI journey and stay on track. The presentation explored the complexities of enterprise search challenges and how IA principles can be leveraged to provide AI solutions through the use of a semantic layer. O’Brien-Scott and Cakici showcased a case study where a taxonomy, an ontology, and a knowledge graph were used to structure content at a healthcare workforce solutions organization, providing personalized content recommendations and increasing content findability.
In this session, participants gained insights about the following:
Most common types of AI categories and use cases;
Recommended steps to design and implement taxonomies and ontologies, ensuring they evolve effectively and support the organization’s search objectives;
Taxonomy and ontology design considerations and best practices;
Real-world AI applications that illustrated the value of taxonomies, ontologies, and knowledge graphs; and
Tools, roles, and skills to design and implement AI-powered search solutions.
How to build your own Delve: combining machine learning, big data and SharePointJoris Poelmans
You are experiencing the benefits of machine learning everyday through product recommendations on Amazon & Bol.com, credit card fraud prevention, etc… So how can we leverage machine learning together with SharePoint and Yammer. We will first look into the fundamentals of machine learning and big data solutions and next we will explore how we can combine tools such as Windows Azure HDInsight, R, Azure Machine Learning to extend and support collaboration and content management scenarios within your organization.
How Enterprise Architecture & Knowledge Graph Technologies Can Scale Business...Semantic Web Company
Organising data, for most of us, means Excel spreadsheets and folders upon folders. Knowledge graph technology, however, organises data in ways similar to the brain – through context and relations. By connecting your data, you (and also machines) are able to gain context within your knowledge, helping you to make informed decisions based on all of the information you already have.
So, how can enterprises benefit from this and scale?
PwC Sr. Research Fellow for Emerging Tech, Alan Morrison, and Sebastian Gabler, Head of Sales of Semantic Web Company tackle the importance of Enterprise Knowledge Graphs and how these technologies scale business efficiency.
Learn about:
• Application-centric development to data-centric approaches
• How enterprise architects learn how to benefit from knowledge graphs: use cases
• Learn which use cases fit well to which type of graph, and which technologies are involved
• Understand how RDF helps with data integration.
• What is AI-assisted entity linking?
• Understand data virtualisation vs. materialisation
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
Deep Text Analytics - How to extract hidden information and aboutness from textSemantic Web Company
- Deep Text Analytics (DTA) is an application of Semantic AI
- DTA fuses methods and algorithms taken from language modeling, corpus linguistics, machine learning, knowledge representation and the semantic web result into Deep Text Analytics methods
- Main areas of use cases for DTA are Information retrieval, NLU, Question answering, and Recommender Systems
Unified views of business-critical information across all customer-facing processes and HR-related tasks are most relevant for decision makers.
In this talk we present a SharePoint extension that supports the automatic linking of unstructured content like Word documents with structured information from other databases, such as statistical data. As a result, decision makers have knowledge portals based on linked data at their fingertips.
While the importance of managed metadata and Term Store is clear to most SharePoint architects, the significance of a semantic layer outside of the content silos has not yet been explored systematically.
We will present a four-layered content architecture and will take a close look on some of the aspects of the semantic layer and its integration with SharePoint:
- Keeping Term Store and the semantic layer in sync
- Automatic tagging of SharePoint content
- Use of graph databases to store tags
- Entity-centric search & analytics applications
Metadata is most often stored per data source, and therefore it is meaningless outside of the silo. In this presentation, we will give a live demo of a SharePoint extension that makes use of an explicit semantic layer based on standards. This approach builds the basis to start linking data across the silos in a most agile way.
The resulting knowledge graph can start on a small scale, to develop continuously and to grow with the requirements. In this presentation we will give an example to illustrate how initially disconnected HR-related data (CVs in SharePoint; statistical data from labour market; skills and competencies taxonomies; salary spreadsheets) gets linked automatically, and is then made available through an extensive search & analytics application.
Slides based on a workshop held at SEMANTiCS 2018 in Vienna. Introduces a methodology for knowledge graph management based on Semantic Web standards, ranging from taxonomies over ontologies, mappings, graph and entity linking. Further topics covered: Semantic AI and machine learning, text mining, and semantic search.
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
Machines learn better with Semantics!
See how taxonomy management and the maintenance of knowledge graphs benefit from machine learning and corpus analysis, and how, in return, machine learning gets improved when using semantic knowledge models for further enrichment.
A quick introduction to taxonomies, and how they relate to ontologies and knowledge graph. See how they can serve as part of a semantic layer in your information architecture. Learn which use cases can be developed based on this.
PoolParty GraphSearch - The Fusion of Search, Recommendation and AnalyticsSemantic Web Company
See how Cognitive Search works when based on Semantic Knowledge Graphs.
We showcase the latest developments and new features of PoolParty GraphSearch:
- Navigate a semantic knowledge graph
- Ontology-based data access (OBDA)
- Search over various search spaces: Ontology-driven facets including hierarchies
- Sophisticated autocomplete including context information
- Custom views on entity-centric and document-centric search results
- Linked data: put various tagging services such as TRIT or PoolParty Extractor in series and benefit from comprehensive semantic enrichment
- Statistical charts to explain results from unified data repositories quickly
- Plug-in system for various recommendation and matchmaking algorithms
This talk discusses how companies can apply semantic technologies to build cognitive applications. It examines the role of semantic technologies within the larger Artificial Intelligence (AI) technology ecosystem, with the aim of raising awareness of different solution approaches.
To succeed in a digital and increasingly self-service-oriented business environment, companies can no longer rely solely on IT professionals. Solutions like the PoolParty Semantic Suite utilize domain experts and business users to shape the cognitive intelligence of knowledge-driven applications.
Cognitive solutions essentially mimic how the human brain works. The search for cognitive solutions has challenged computer scientists for more than six decades. The research has matured to the extent that it has moved out of the laboratory and is now being applied in a range of knowledge-intensive industries.
There is no such thing as a single, all-encompassing “AI technology.” Rather, the large global professional technology community and software vendors are continuously developing a broad set of methods and tools for natural language processing and advanced data analytics. They are creating a growing library of machine learning algorithms to enhance the automated learning capabilities of computer systems. These emerging technologies need to be customized or combined with complementary solutions as semantic knowledge graphs, depending on the use case.
A hybrid approach to cognitive computing, employing both the statistical and knowledge base models, will have a critical influence on the development of applications. Highly automated data processing based on sophisticated machine-learning algorithms must give end user the option to independently modify the functioning of smart applications in order to overcome the disadvantages associated with ‘black-box’ approaches.
This talk will give an overview over state-of-the-art smart applications, which are becoming a fusion of search, recommendation, and question-answer machines. We will cover specific use cases in focused knowledge domains, and we will discuss how this approach allows for AI-enabled use cases and application scenarios that are currently highly prioritized by corporate and digital business players.
In this engaging, 1-hour webinar (hosted by http://www.poolparty.biz and http://www.mekon.com), you will learn how to tailor information chunks to readers’ unique needs. We will talk about:
- Benefits and principles of granular structured content, and how to start preparing your own content for this new architecture.
- Best practices for linking structured content to standards-based taxonomies, and some pitfalls to avoid
- The underlying semantic architecture that you can work toward for a truly mature and scalable approach to linking content and data
- Key use cases that you can apply to your own organization
See how you can configure your linked data eco-system based on PoolParty's semantic middleware configurator. Benefit from Shadow Concept Extraction by making implicit knowledge visible. Combine knowledge graphs with machine learning and integrate semantics into your enterprise information systems.
Technical Deep Dive: Learn more about the most complete Semantic Middleware on the market. See how to integrate semantic services into your Enterprise Information Systems.
Taxonomies and Ontologies – The Yin and Yang of Knowledge ModellingSemantic Web Company
See how ontologies and taxonomies can play together to reach the ultimate goal, which is the cost-efficient creation and maintenance of an enterprise knowledge graph. The knowledge modelling methodology is supported by approaches taken from NLP, data science, and machine learning.
This talk addresses two questions: “How can the quality of taxonomies be defined?” and “How can it be measured?” See how quality criteria vary depending on how a taxonomy is applied, such as automatic content classification in ecommerce or a knowledge graph for data integration in enterprises. Distinguish between formal quality, structural properties, content coverage, and network topology. Investigate the advantages of standards-based and machine-processable SKOS taxonomies to be able to measure the quality of taxonomies automatically, as well as several tools and techniques for quality assessment.
Consistency is crucial to a good user experience. Designers go to great lengths to create and test consistent visual designs. The structural design of an information environment, which is of equal importance to a good user experience, is too often ignored. Blumauer presents a “four-layered content architecture” for making sense of any information environment by clearly distinguishing between the content, metadata, and semantic layers and the navigation logic. He discusses several use cases for a taxonomy-driven user experience such as personalization or dynamically created topic pages.
PoolParty Semantic Suite 5.5 has been released in August 2016. Further integrations like with Elasticsearch or Stardog strengthen PoolParty’s position as the leading semantic middleware at the cognitive computing market. Knowledge engineers and users benefit from an even more sophisticated combination of semantic computing and machine learning. The new features support context aware knowledge modelling and include an extended data quality management module.
Knowledge extraction: Extract terms, phrases and named entities from SharePoint and O365 documents with high accuracy
Auto classification: Streamline your workflows with PoolParty’s reliable auto classifier
Consistent tagging: Semi-automatic tagging based on your taxonomies provides consistent metadata
Enterprise-wide tagging: Benefit from linked data and connect your SharePoint to other repositories
Concept based search: autocomplete from taxonomy
Automatic use of synonyms: get more precise results
Configurable search refiners: faceted search based on taxonomy hierarchy
Include fact box for search term in search results: benefit from additional context information
This slidedeck is about PoolParty Semantic Suite (http://www.poolparty.biz/), especially about features included by releases 5.2 and 5.3.
See how taxonomy management based on SKOS can be extended with SKOS-XL, all based on W3C's Semantic Web standards. See how SKOS-XL can be combined with ontologies like FIBO.
PoolParty's built in reference corpus analysis based on powerful text mining helps to continuously extend taxonomies. Its built-in co-occurence analysis supports taxonomists with the identification of candidate concepts.
PoolParty Semantic Integrator can be used for deep data analytics tasks and semantic search. See how this can be integrated with various graph databases and search engines.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
PoolParty Semantic Classifier
1. Andreas Blumauer
CEO & Managing Partner
Semantic Web Company /
PoolParty Semantic Suite
PoolParty Semantic Classifier
Bringing Machine Learning, NLP
and Knowledge Graphs together
2. Introduction
Semantic Web Company (SWC)
▸ Founded in 2004
▸ Based in Vienna
▸ Privately held
▸ 40+ FTE
▸ Experts in NLP, Semantics and
Machine learning
▸ ~30% growth/year
▸ 2.5 Mio Euro funding for R&D
▸ SWC named to KMWorld’s
‘100 Companies That Matter in
Knowledge Management’ in
2016 and 2017
▸ www.semantic-web.com
2 PoolParty Semantic Suite
▸ First release in 2009
▸ Current version 6.2
▸ W3C standards compliant
▸ Over 200 installations
world-wide
▸ 50% of revenue is reinvested
into PoolParty development
▸ PoolParty on-premises or
used as a cloud service
▸ KMWorld listed PoolParty as
Trend-Setting Product 2015,
2016 and 2017
▸ www.poolparty.biz
3. Agenda
3
Semantic
AI
▸ Introduction to Semantic AI
▹ Machine Learning, NLP and
Knowledge Graphs
▹ Current status of Artificial Intelligence?
▹ What is Semantic AI?
▸ PoolParty Semantic Classifier
▹ How does it work?
▹ Benchmarks
▹ Integration Scenarios
▸ Use Cases
▹ Overview
▹ Business Case
▹ Example: Issue Classifier
5. ArtificiaI
Intelligence -
An overview
5
Artificial
Intelligence (AI)
Artificial Neural
Network (ANN)
Symbolic AI
(GOFAI*)
Sub-Symbolic AI Statistical AI
Knowledge graphs &
reasoning
Natural Language
Processing (NLP)
Machine Learning
* Good old-fashioned AI
Word Embedding
(Word2Vec)
Deep Learning
(DNN)
Natural Language
Understanding
Entity Recognition
& Linking
Knowledge
Extraction
Semantic enhanced
Text Classification
6. What makes
someone an
intelligent
being?
Assessment of
the current status
of Artificial
Intelligence
Level Example Typical Problems Questions
(6)
Create
Convert an "unhealthy" recipe
for apple pie to a "healthy"
recipe by replacing your choice
of ingredients.
- Create a new product or point of view
- Combine elements in a new pattern
- Propose alternative solutions
How would you improve …?
Can you formulate a theory for …?
Can you predict the outcome if …?
(5)
Evaluate
Which kinds of knowledge
models are best for machine
learning, and why?
- Judge the value of material (statement,
poem, research report) for a given
purpose
- Defend opinions
What is your opinion of …?
How would you prioritize …?
What would you use to support the
view …?
(4)
Analyse
How does a graph database
and a semantic knowledge
model work together?
- Recognise organizational principles
- Identify parts
- Understand relationships between
parts
How is ... related to ...?
What is the function of ...?
What conclusions can you draw ...?
(3)
Apply
How can taxonomies be used
to enhance machine learning?
- Apply facts, rules, principles, and
theories
- Use learned material in new and
concrete situations
Why is … significant?
How is … an example of …?
What elements would you use to
change …?
(2)
Understand
What is the difference between
an ontology and a taxonomy?
- Understand facts and ideas
- Classify objects and summarise text
- Grasp the meaning of material
What is the difference between …?
What is the main idea of …?
Which statements support …?
(1)
Remember
Who is the inventor of the
World Wide Web?
- Recognise facts, terms, and basic
concepts
- Recall of a wide range of material,
from specific facts to complete theories
Who is …?
Where is …?
Why did …?
6
Bloom’s Taxonomy: Classify cognitive processes
7. Remember
Knowledge Graphs
& Knowledge
Extraction
7
Perth
Australia
Perth is one of the
most isolated
major cities in the
world, with a
population of
2,022,044 living
in Greater Perth.
Australia is a
member of the
OECD, United
Nations, G20,
ANZUS, and the
World Trade
Organisation.
Country
City
is a
is a
is located in
Avoid illogical answers:
Support complex Q&A:
distance between
Which cities located in the
Commonwealth of Nations
have a population of more
than 2 mio. people?
Commonwealth
of Nations
International
Organisation
is part of
is a
8. Remember
Knowledge Graphs
& Knowledge
Extraction
Knowledge Graphs (KG) can cover
general knowledge (often also
called cross-domain or
encyclopedic knowledge), or
provide knowledge about special
domains such as biomedicine.
In most cases KGs are based on
Semantic Web standards, and have
been generated by a mixture of
automatic extraction from text or
structured data, and manual
curation work.
Examples:
▸ DBpedia
▸ Google Knowledge Graph
▸ YAGO
▸ OpenCyc
▸ Wikidata
8 Who is the inventor of the World Wide Web?
9. The Semantic
Web
A standards-based
graph of
knowledge graphs
9
Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
10. “Understand”
Google Featured
Snippets based on
Sentence
Compression
Algorithms
(based on DL)
To train Google’s artificial Q&A brain, the
company uses old news stories, where
machines start to see how headlines
serve as short summaries of the longer
articles that follow. But for now, the
company still needs its team of PhD
linguists.
Spanning about 100 PhD linguists across
the globe, the Pygmalion team produces
“the gold data,” while the news stories
are the “silver.” The silver data is still
useful, because there’s so much of it. But
the gold data is essential.
WIRED article
10 What is the difference between an ontology and a taxonomy?
11. “Create”
Example for
DL-based
“creativity” Aiva’s compositions still require human input with regards to
orchestration and musical production. In fact, Aiva’s creators envisage
a future where man and machine will collaborate to fulfill their
creative potential, rather than replace one another.
http://www.aiva.ai/
11 After having listened to a large
amount of music and learned its own
models of music theory, Aiva
composes its very own sheet music.
These partitions are then played by
professional artists on real
instruments in a recording studio,
achieving the best sound quality
possible.
12. What is
Semantic AI?
12
Artificial Intelligence
ANN
Symbolic AISub-Symbolic AI Statistical AI
KR & reasoning
NLP
Machine Learning
Word Embedding Deep Learning
Natural Language
Understanding
Entity Recognition &
Linking
Knowledge Extraction
Semantic enhanced
Text Classification
In Semantic AI, various methods from
Symbolic AI are combined with
machine learning methods, and/or
neuronal networks.
Examples:
● Semantic enrichment of
text corpora to enhance
word embeddings
● Extraction of semantic features
from text to improve ML-based
classification tasks
● Combine ML-based with
Graph-based entity extraction
● Knowledge Graphs as a Data
Model for Machine Learning
● ….
13. Knowledge
Graphs as a
Data Model for
Machine Learning
These transformations can result in loss of information and introduce bias. To solve this problem, we
require machine learning methods to consume knowledge in a data model more suited to represent
this heterogeneous knowledge. We argue that knowledge graphs are that data model.
Three examples for the benefits of using knowledge graphs:
▸ they allow for true end-to-end-learning,
▸ they simplify the integration of heterogeneous data sources and data harmonization,
▸ they provide a natural way to seamlessly integrate different forms of background knowledge.
Wilcke X, Bloem P, De Boer V. The Knowledge Graph as the Default Data Model for Machine Learning. Data Science. 2017 Oct 17;1-19. Available from, DOI:
10.3233/DS-170007
13 Traditionally, when faced with
heterogeneous knowledge in a
machine learning context, data
scientists preprocess the data
and engineer feature vectors so
they can be used as input for
learning algorithms (e.g., for
classification).
16. PoolParty
Semantic
Classifier
Feature highlights
▸ Classification
▹ Select from various machine learning
algorithms such as SVM, Deep Learning
and Naive Bayes for content classification.
▹ Benefit from a rich feature set such as
terms, concepts, shadow concepts which
gives you more flexibility when training
classifiers.
▸ User Experience
▹ A user-friendly interface enables
non-technical experts to perform
classification tasks and benefit from
machine learning.
▸ Scalability
▹ Large content repositories can be
classified on top of a Spark cluster.
▸ Easy integration
▹ New resources can be classified via the
PoolParty API.
▹ With the GraphSearch plugin system, the
Classifier can be easily integrated for
semantic applications.
▸ Transparency
▹ The Classifier works on the principles of
‘Explainable AI’.
16
17. Extraction,
Categorization,
Classification
What is the
difference?
▸ Classification
▹ Implemented based on a training corpus and uses the
labels of the training corpus as classes
▸ Extraction
▹ Finding terms and concepts in text, using scoring
mechanisms to give an indication of their importance
▸ Categorization
▹ Uses only concepts and categorizes text based on a
thesaurus
17
18. How it works
18 1. Determine classes (labels)
2. Identify training documents per class
3. Create classifier
a. Pick machine learning method
b. Choose correct parameters
c. Determine used features
d. Train model
4. Evaluate results (Cross validation/F1)
5. Goto 2. and try to find better classification
method
6. Make use of the Classifier API
19. Explainable AI
Classifiers based on ML algorithms such as Deep Learning perform better when training data is
semantically enhanced. Additional features are derived from a controlled vocabulary, which also
make the used features more transparent to the Data Scientist.
19
20. Benchmarking
the PoolParty
Semantic
Classifier
Improvement of
5.2% compared
to traditional
(term-based)
SVM
20
Features used Classifier F1 (5 folds) Variance
Terms LinearSVC 0.83175 0.0008
Concepts from REEGLE + Shadow Concepts LinearSVC 0.84451 0.0011
Concepts from REEGLE LinearSVC 0.84647 0.0009
Terms + Concepts from REEGLE + Shadow Concepts LinearSVC 0.87474 0.0009
Reegle thesaurus
A comprehensive SKOS taxonomy
for the clean energy sector
(http://data.reeep.org/thesaurus/guide)
● 3,420 concepts
● 7,280 labels (English version)
● 9,183 relations (broader/narrower + related)
Document Training Set
1,800 documents in 7 classes
Renewable Energy, District Heating Systems,
Cogeneration, Energy Efficiency, Energy (general),
Climate Protection, Rural Electrification
21. The Classifier as
a component of
PoolParty
Semantic Suite
Most complete
Semantic
Middleware on
the Global Market
21
Bain Capital is a venture capital
company based in Boston, MA.
Since inception it has invested in
hundreds of companies including AMC
Entertainment, Brookstone, and Burger
King. The company was co-founded by
Mitt Romney.
Taxonomy &
Ontology Server
Entity Extractor &
Semantic Classifier
Data Integration &
Data Linking
Unstructured
Data
Semi-
structured
Data
Structured
Data
Unified
Views
PoolParty
GraphSearch
Identify new
candidate concepts
to be included in a
controlled vocabulary
Controlled vocabularies as a basis for
highly precise knowledge extraction
and text classification
Entity Extractor informs
all incoming data
streams about its
semantics and links them
Schema mapping
based on ontologies
RDF
Graph Database
Factsheet
24. Examples for
use cases based
on PoolParty
Semantic
Classifier
▸ News classification
▹ Reduce manual effort of classifying inbound documents or news
▸ Recommender Services
▹ Identify appropriate agents in help desk systems
▹ Matchmaking between user(groups) and products/content
▸ Sentiment Analysis
▹ Improve customer retention management by precise sentiment analysis
▸ Enhanced Domain-specific Text Mining
▹ Complement rule-based systems for fraud detection
▹ Analyze judicial decisions
24
25. ▸ To understand
▹ Content aboutness in a defined
framework
▹ Data relationships and context within a
unified organizational model
▹ Connections across disparate datasets
▸ To increase precision
▹ Hierarchical or other mapped
relationships allow for recommending
similar content when exact matches not
found
▹ Granularity allows for more specific
recommendations
▹ Consistency across structure results more
precise analysis and predictions
Source: Suzanne Carroll, Data Science Product Director at XO Group
Why Data
Scientists need
Semantic
Models
25
26. Business Case
Based on an
improvement of
5.2%
26
Inbound
Documents
PoolParty
Semantic
Classifier
Experienced
Agent
● 100,000 documents (emails, tickets, etc.) per month
● 5 Euros extra costs per document when misrouted
● Cost savings per year:
○ 1,200.000 x €5.0 x 0.052 = € 312,000 per annum
28. One question
at the end
Will Artificial
Intelligence
make
Subject Matter
Experts
obsolete?
28 Imagine you want to
build an application
that helps to identify
patients and
treatments pairings.
Which will you prefer?
Applications solely based on machine learning, those ones which
are based on doctors' knowledge only, or a combination of both?
29. Learn more and
ask for a demo
based on your
own data!
https://www.poolparty.biz/semantic-classifier/
29