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.
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
Text Analytics in Enterprise Search - Daniel Linglucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
Text analytics is a large and interesting subject, covering a wide range of topics. In the world of enterprise search however, the usual application of text analytics rarely ranges beyond extracting semi-structured information from the source data. As some of the more advanced concepts in text analytics, such as automatic text categorization, can be easily leveraged to bring a search installation from a search tool to a tool for discovery.
An Advanced IR System of Relational Keyword Search Techniquepaperpublications3
Abstract: Now these days keyword search to relational data set becomes an area of research within the data base and Information Retrieval. There is no standard process of information retrieval, which will clearly show the accurate result also it shows keyword search with ranking. Execution time is retrieving of data is more in existing system. We propose a system for increasing performance of relational keyword search systems. In the proposed system we combine schema-based and graph-based approaches and propose a Relational Keyword Search System to overcome the mentioned disadvantages of existing systems and manage the information and user access the information very efficiently. Keyword Search with the ranking requires very low execution time. Execution time of retrieving information and file length during Information retrieval can be display using chart.Keywords: Keyword Search, Datasets, Information Retrieval Query Workloads, Schema-based Systems, Graph-based Systems, ranking, relational databases.
Title: An Advanced IR System of Relational Keyword Search Technique
Author: Dhananjay A. Gholap, Gumaste S. V
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
Text Analytics in Enterprise Search - Daniel Linglucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
Text analytics is a large and interesting subject, covering a wide range of topics. In the world of enterprise search however, the usual application of text analytics rarely ranges beyond extracting semi-structured information from the source data. As some of the more advanced concepts in text analytics, such as automatic text categorization, can be easily leveraged to bring a search installation from a search tool to a tool for discovery.
An Advanced IR System of Relational Keyword Search Techniquepaperpublications3
Abstract: Now these days keyword search to relational data set becomes an area of research within the data base and Information Retrieval. There is no standard process of information retrieval, which will clearly show the accurate result also it shows keyword search with ranking. Execution time is retrieving of data is more in existing system. We propose a system for increasing performance of relational keyword search systems. In the proposed system we combine schema-based and graph-based approaches and propose a Relational Keyword Search System to overcome the mentioned disadvantages of existing systems and manage the information and user access the information very efficiently. Keyword Search with the ranking requires very low execution time. Execution time of retrieving information and file length during Information retrieval can be display using chart.Keywords: Keyword Search, Datasets, Information Retrieval Query Workloads, Schema-based Systems, Graph-based Systems, ranking, relational databases.
Title: An Advanced IR System of Relational Keyword Search Technique
Author: Dhananjay A. Gholap, Gumaste S. V
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
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.
Semantic Data Normalization For Efficient Clinical Trial ResearchOntotext
Clinical trials, both public and proprietary, hold a huge amount of valuable information. Acquiring knowledge from that information in a cost and time efficient manner is a major industry pain point.
Although information from clinical trials is stored in structured or semi-structured form, it is rarely coded with medical terminologies, which creates a significant level of ambiguity and increases the effort for data preparation for analytical purposes.
Tag recommendation is very useful system in detecting the type of messages like GMAIL has divided its inbox in three tabs PRIMARY, SOCIAL, PROMOTIONS and there are some other labels like SPAM, Important etc. It can be used in other categories like Social Bookmarking, Search Engines etc.
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.
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.
Semantic Data Normalization For Efficient Clinical Trial ResearchOntotext
Clinical trials, both public and proprietary, hold a huge amount of valuable information. Acquiring knowledge from that information in a cost and time efficient manner is a major industry pain point.
Although information from clinical trials is stored in structured or semi-structured form, it is rarely coded with medical terminologies, which creates a significant level of ambiguity and increases the effort for data preparation for analytical purposes.
Tag recommendation is very useful system in detecting the type of messages like GMAIL has divided its inbox in three tabs PRIMARY, SOCIAL, PROMOTIONS and there are some other labels like SPAM, Important etc. It can be used in other categories like Social Bookmarking, Search Engines etc.
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.
Discovering User's Topics of Interest in Recommender SystemsGabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling.
Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We present some of Smart Canvas features powered by its recommender system, such as:
- Highlight relevant content, explaining to the users which of his topics of interest have generated each recommendation.
- Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests.
- Recommends people with similar interests, explaining which topics brings them together.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Presented at DocTrain East 2007 by Joe Gelb, Suite Solutions -- Designing, building and maintaining a coherent information architecture is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when your content is based on a modular or topic-based model such as DITA and SCORM or if you are migrating to such a model.
But where to start? Terms such as taxonomy, semantics, and ontology can be intimidating, and recognized standards like RDF, OWL, Topic Maps (XTM) and SKOS seem so abstract. This pragmatic workshop will provide an overview of the standards and concepts, and a chance to use them hands-on to turn the abstract into tangible skills. We will demonstrate how a well-designed information architecture facilitates reuse and how the information model is integrally connected to conditional and multi-purpose publishing.
We will introduce an innovative, comprehensive methodology for information modeling and content development called SOTA Solution Oriented Topic Architecture. SOTA does not aim to be yet another new standard, but rather a concrete methodology backed up with open-source and accessible tools for using existing standards. We will demonstrate ֖and practice—hands-on—how this powerful methodology can help you organize and express information, determine which content actually needs to be created or updated, and build documentation and training deliverables from your content based on the rules you define.
This workshop is essential for successfully implementing topic models like DITA and SCORM, multi-purpose conditional publishing, and successfully facilitating content reuse.
Sustainability Investment Research Using Cognitive AnalyticsCambridge Semantics
In this webinar Anthony J. Sarkis, Chief Strategy Officer at Parabole, and Steve Sarsfield, VP Product at Cambridge Semantics, explore how portfolio managers are using the recently developed Parabole/ AnzoGraph DB integration as their underlying infrastructure for conducting ML and cognitive analytics at scale to exploit data to identify potential risks and new opportunities.
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented "An Overview of Taxonomies and AI" on January 30th, 2024, in the inaugural webinar of the Artificial Intelligence webinar series: The promise and the perils,” hosted by the Knowledge & Information Management Group of CILIP, the library and information association of the UK. In her presentation, Heather explained, with examples, how both generative AI and other AI technologies support taxonomy development and use and how taxonomies can support AI applications.
Explore the presentation to learn:
Why both top-down and bottom-up methods are needed in taxonomy creation
What AI methods are used for auto-tagging and auto-classification with taxonomies
How AI methods can extract candidate terms for taxonomy creation
How generative AI can be used for certain bottom-up taxonomy development tasks
How AI can be used to analyze a taxonomy against a corpus of documents
How generative AI can be used in queries to analyze a taxonomy
What AI applications taxonomies can support
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Overview
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.
5. Test prototype
We developed a prototype called Data Mining Tool (DMT) for
testing analytics model.
As test data, we use tech and political news (about 2k + 1k RSS
Feeds delivering 10k news daily).
6. DMT workflow
1. Import Documents to Index
2. Extract meta-data for each Document (NLP: keywords labels, terms etc.)
3. Extract Chains using Cluster Analysis
4. Assign Weights to Topics
5. Build Trends using ranking by Current Weight and Weight Dynamics
6. Build Segments (related topics, nested topics)
7. Visualize Data (Trends Statistics, Segments Hierarchy)
8. Explore Data (Flexible Search UI: Trends, Documents, Segments, Keywords
etc.)
9. Use API to communicate with the system
21. Glossary
Term – sequence of characters for training NLP application
(represents Named Entity).
Trend - unique keywords chain with weight.
Topic – abstract ‘cluster’ of relations between particular keywords
that occur in Trend.
Segment - group of similar Trends, intersected by search results.
Segmentation – relations between topics from different segments,
based on subtopic dynamics. Represents 'new knowledge'.
Thread - sequence of keywords extracted from given sentence.
Label - an attribute of Term that defines its properties.
23. Team
We are a team with more than 3 years experience of Data Mining
research and projects.
We are interested in making sense of big data and experimenting with
Machine Learning Techniques. We build Semantic Networks and NLP
projects based on open-source projects as well as our own.
Oleksandr Shamrai - PHP software engineer, responsive for core
algorithms implementation and performance, team development tools
and rules
Pavel Yakovlev - Business analyst and QA, has passion for data mining:
cluster analysis and recommendation solutions
Max Leonov - Python software engineer, responsible for NLP (Natural
Languages Processing) applications modeling, development, testing and
deployment process