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TikTok launches ‘TikTok Q&A,’ a new feature for creators to engage with viewers’ questions

March 7, 2021 No Comments

Earlier this year, TikTok was spotted testing a new Q&A feature that would allow creators to more directly respond to their audience’s questions using either text or video. Today, the company has announced the feature is now available to all users globally. With the release of TikTok Q&A, as the feature is officially called, creators will be able to designate their comments as Q&A questions, respond to questions with either text comments or video replies and add a Q&A profile link to their bios, among other things. The feature also works with live videos.

TikTok Q&A grew out of a way that creators were already using the video platform to interact with viewers. Often, after posting a video, viewers would have follow-up questions about the content. Creators would then either respond to those questions in the comments section or, if the response was more involved, they might post a second video instead.

The Q&A feature essentially formalizes this process by making it easier for creators — particularly those with a lot of fans — to identify and answer the most interesting questions.

Image Credits: TikTok

To use Q&A, viewers will first designate their comment as a Q&A question using a new commenting option. To do so, they’ll tap the Q&A icon to the right side of the text entry field in comments. This will also label their comment with the icon and text that says “Asked by” followed by the username of the person asking the question. This makes it easier for creators to see when scanning through a long list of comments on their video.

The feature will also feed the question into the creator’s new Q&A page where all questions and answers are aggregated. Users can browse this page to see all the earlier questions and answers that have already been posted or add a new question of their own.

Creators will respond to a Q&A question with either text or video replies, just as they did before — so there isn’t much new to learn here, in terms of process.

They can also add Q&A comments as stickers in their responses where the new video will link back to the original, where the question was first asked, similar to how they’re using comment stickers today.

The feature will also be available in TikTok LIVE, making it easier for creators to see the incoming questions in the stream’s chat from a separate panel.

Image Credits: TikTok

As a part of this launch, a Q&A profile link can be added to creators’ Profile bios, which directs users to the Q&A page where everything is organized.

During tests, the feature was only made available to creators with public accounts that had more than 10,000 followers and who opted in. Today, TikTok says it’s available to all users with Creator Accounts.

To enable the feature on your own profile, you’ll go to the privacy page under Settings, then select “Creator,” tap “Q&A” and then “Turn on Q&A.” (If users don’t already have a Creator account, they can enable it for themselves under settings.)

The feature is rolling out to users worldwide in the latest version of the TikTok app now, the company says.

@tiktokYou can now ask and answer any questions on LIVE with the new Q&A feature. Check it out now!♬ original sound – TikTok


Early Stage is the premier “how-to” event for startup entrepreneurs and investors. You’ll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. We’ll cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in — there’s ample time included for audience questions and discussion.


Social – TechCrunch


TikTok launches ‘TikTok Q&A,’ a new feature for creators to engage with viewers’ questions

March 5, 2021 No Comments

Earlier this year, TikTok was spotted testing a new Q&A feature that would allow creators to more directly respond to their audience’s questions using either text or video. Today, the company has announced the feature is now available to all users globally. With the release of TikTok Q&A, as the feature is officially called, creators will be able to designate their comments as Q&A questions, respond to questions with either text comments or video replies and add a Q&A profile link to their bios, among other things. The feature also works with live videos.

TikTok Q&A grew out of a way that creators were already using the video platform to interact with viewers. Often, after posting a video, viewers would have follow-up questions about the content. Creators would then either respond to those questions in the comments section or, if the response was more involved, they might post a second video instead.

The Q&A feature essentially formalizes this process by making it easier for creators — particularly those with a lot of fans — to identify and answer the most interesting questions.

Image Credits: TikTok

To use Q&A, viewers will first designate their comment as a Q&A question using a new commenting option. To do so, they’ll tap the Q&A icon to the right side of the text entry field in comments. This will also label their comment with the icon and text that says “Asked by” followed by the username of the person asking the question. This makes it easier for creators to see when scanning through a long list of comments on their video.

The feature will also feed the question into the creator’s new Q&A page where all questions and answers are aggregated. Users can browse this page to see all the earlier questions and answers that have already been posted or add a new question of their own.

Creators will respond to a Q&A question with either text or video replies, just as they did before — so there isn’t much new to learn here, in terms of process.

They can also add Q&A comments as stickers in their responses where the new video will link back to the original, where the question was first asked, similar to how they’re using comment stickers today.

The feature will also be available in TikTok LIVE, making it easier for creators to see the incoming questions in the stream’s chat from a separate panel.

Image Credits: TikTok

As a part of this launch, a Q&A profile link can be added to creators’ Profile bios, which directs users to the Q&A page where everything is organized.

During tests, the feature was only made available to creators with public accounts that had more than 10,000 followers and who opted in. Today, TikTok says it’s available to all users with Creator Accounts.

To enable the feature on your own profile, you’ll go to the privacy page under Settings, then select “Creator,” tap “Q&A” and then “Turn on Q&A.” (If users don’t already have a Creator account, they can enable it for themselves under settings.)

The feature is rolling out to users worldwide in the latest version of the TikTok app now, the company says.

@tiktokYou can now ask and answer any questions on LIVE with the new Q&A feature. Check it out now!♬ original sound – TikTok

Mobile – TechCrunch


5 questions every IT team should be able to answer

December 15, 2020 No Comments

Now more than ever, IT teams play a vital role in keeping their businesses running smoothly and securely. With all of the assets and data that are now broadly distributed, a CEO depends on their IT team to ensure employees remain connected and productive and that sensitive data remains protected.

CEOs often visualize and measure things in terms of dollars and cents, and in the face of continuing uncertainty, IT — along with most other parts of the business — is facing intense scrutiny and tightening of budgets. So, it is more important than ever to be able to demonstrate that they’ve made sound technology investments and have the agility needed to operate successfully in the face of continued uncertainty.

For a CEO to properly understand risk exposure and make the right investments, IT departments have to be able to confidently communicate what types of data are on any given device at any given time.

Here are five questions that IT teams should be ready to answer when their CEO comes calling:

What have we spent our money on?

Or, more specifically, exactly how many assets do we have? And, do we know where they are? While these seem like basic questions, they can be shockingly difficult to answer … much more difficult than people realize. The last several months in the wake of the COVID-19 outbreak have been the proof point.

With the mass exodus of machines leaving the building and disconnecting from the corporate network, many IT leaders found themselves guessing just how many devices had been released into the wild and gone home with employees.

One CIO we spoke to estimated they had “somewhere between 30,000 and 50,000 devices” that went home with employees, meaning there could have been up to 20,000 that were completely unaccounted for. The complexity was further compounded as old devices were pulled out of desk drawers and storage closets to get something into the hands of employees who were not equipped to work remotely. Companies had endpoints connecting to corporate network and systems that they hadn’t seen for years — meaning they were out-of-date from a security perspective as well.

This level of uncertainty is obviously unsustainable and introduces a tremendous amount of security risk. Every endpoint that goes unaccounted for not only means wasted spend but also increased vulnerability, greater potential for breach or compliance violation, and more. In order to mitigate these risks, there needs to be a permanent connection to every device that can tell you exactly how many assets you have deployed at any given time — whether they are in the building or out in the wild.

Are our devices and data protected?

Device and data security go hand in hand; without the ability to see every device that is deployed across an organization, it becomes next to impossible to know what data is living on those devices. When employees know they are leaving the building and going to be off network, they tend to engage in “data hoarding.”


Enterprise – TechCrunch


3 questions to ask before adopting microservice architecture

December 8, 2020 No Comments

As a product manager, I’m a true believer that you can solve any problem with the right product and process, even one as gnarly as the multiheaded hydra that is microservice overhead.

Working for Vertex Ventures US this summer was my chance to put this to the test. After interviewing 30+ industry experts from a diverse set of companies — Facebook, Fannie Mae, Confluent, Salesforce and more — and hosting a webinar with the co-founders of PagerDuty, LaunchDarkly and OpsLevel, we were able to answer three main questions:

  1. How do teams adopt microservices?
  2. What are the main challenges organizations face?
  3. Which strategies, processes and tools do companies use to overcome these challenges?

How do teams adopt microservices?

Out of dozens of companies we spoke with, only two had not yet started their journey to microservices, but both were actively considering it. Industry trends mirror this as well. In an O’Reilly survey of 1500+ respondents, more than 75% had started to adopt microservices.

It’s rare for companies to start building with microservices from the ground up. Of the companies we spoke with, only one had done so. Some startups, such as LaunchDarkly, plan to build their infrastructure using microservices, but turned to a monolith once they realized the high cost of overhead.

“We were spending more time effectively building and operating a system for distributed systems versus actually building our own services so we pulled back hard,” said John Kodumal, CTO and co-founder of LaunchDarkly.

“As an example, the things we were trying to do in mesosphere, they were impossible,” he said. “We couldn’t do any logging. Zero downtime deploys were impossible. There were so many bugs in the infrastructure and we were spending so much time debugging the basic things that we weren’t building our own service.”

As a result, it’s more common for companies to start with a monolith and move to microservices to scale their infrastructure with their organization. Once a company reaches ~30 developers, most begin decentralizing control by moving to a microservice architecture.

Teams may take different routes to arrive at a microservice architecture, but they tend to face a common set of challenges once they get there.

Large companies with established monoliths are keen to move to microservices, but costs are high and the transition can take years. Atlassian’s platform infrastructure is in microservices, but legacy monoliths in Jira and Confluence persist despite ongoing decomposition efforts. Large companies often get stuck in this transition. However, a combination of strong, top-down strategy combined with bottoms-up dev team support can help companies, such as Freddie Mac, make substantial progress.

Some startups, like Instacart, first shifted to a modular monolith that allows the code to reside in a single repository while beginning the process of distributing ownership of discrete code functions to relevant teams. This enables them to mitigate the overhead associated with a microservice architecture by balancing the visibility of having a centralized repository and release pipeline with the flexibility of discrete ownership over portions of the codebase.

What challenges do teams face?

Teams may take different routes to arrive at a microservice architecture, but they tend to face a common set of challenges once they get there. John Laban, CEO and co-founder of OpsLevel, which helps teams build and manage microservices told us that “with a distributed or microservices based architecture your teams benefit from being able to move independently from each other, but there are some gotchas to look out for.”

Indeed, the linked O’Reilly chart shows how the top 10 challenges organizations face when adopting microservices are shared by 25%+ of respondents. While we discussed some of the adoption blockers above, feedback from our interviews highlighted issues around managing complexity.

The lack of a coherent definition for a service can cause teams to generate unnecessary overhead by creating too many similar services or spreading related services across different groups. One company we spoke with went down the path of decomposing their monolith and took it too far. Their service definitions were too narrow, and by the time decomposition was complete, they were left with 4,000+ microservices to manage. They then had to backtrack and consolidate down to a more manageable number.

Defining too many services creates unnecessary organizational and technical silos while increasing complexity and overhead. Logging and monitoring must be present on each service, but with ownership spread across different teams, a lack of standardized tooling can create observability headaches. It’s challenging for teams to get a single-pane-of-glass view with too many different interacting systems and services that span the entire architecture.


Enterprise – TechCrunch


When Surprisingly Good Performance Leads To Tough Questions About The Value Of PPC

May 19, 2020 No Comments

Many brands are seeing strong year over year growth and in some cases with a conservative PPC strategy. Why is this and what does it mean for future strategies?

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Ten questions for 2020 presidential candidate John Delaney

September 14, 2019 No Comments

In November 2020, America will go to the polls to vote in perhaps the most consequential election in a generation. The winner will lead the country amid great social, economic and ecological unrest. The 2020 election will be a referendum on both the current White House and the direction of the country at large.

Nearly 20 years into the young century, technology has become a pervasive element in all of our lives, and will continue to only grow more important. Whoever takes the oath of office in January 2021 will have to answer some difficult questions, raging from an impending climate disaster to concerns about job loss at the hands of robotics and automation.

Many of these questions are overlooked in day to day coverage of candidates and during debates. In order to better address the issues, TechCrunch staff has compiled a 10-part questionnaire across a wide range of tech-centric topics. The questions have been sent to national candidates, regardless of party. We will be publishing the answers as we receive them. Candidates are not required to answer all 10 in order for us to publish, but we will be noting which answers have been left blank.

First up is former Congressman John Delaney. Prior to being elected to Maryland’s 6th Congressional District, Delaney co-founded and led healthcare loan service Health Care Financial Partners (HCFP) and  commercial lender CapitalSource. He was elected to Congress in 2013, beating out a 10-term Republican incumbent. Rumored to be running against Maryland governor Larry Hogan for a 2018 bid, Delaney instead announced plans to run for president in 2020.

1. Which initiatives will you prioritize to limit humankind’s impact on climate and avoid potential climate catastrophe?

My $ 4 trillion Climate Plan will enable us to reach the goal of net zero emissions by 2050, which the IPCC says is the necessary target to avoid the worst effects of climate change. The centerpiece of my plan is a carbon-fee-and-dividend that will put a price on carbon emissions and return the money to the American people through a dividend. My plan also includes increased federal funding for renewable energy research, advanced nuclear technologies, direct air capture, a new Climate Corps program, and the construction of the Carbon Throughway, which would transport captured carbon from all over the country to the Permian Basin for reuse and permanent sequestration.

2. What is your plan to increase black and Latinx startup founders’ access to funding?

As a former entrepreneur who started two companies that went on to be publicly traded, I am a firm believer in the importance of entrepreneurship. To ensure people from all backgrounds have the support they need to start a new business, I will create nonprofit banks to serve economically distressed communities, launch a new SBIC program to help provide access to capital to minority entrepreneurs, and create a grant program to fund business incubators and accelerators at HBCUs. Additionally, I pledge to appoint an Entrepreneurship Czar who will be responsible for promoting entrepreneurship-friendly policies at all levels of government and encouraging entrepreneurship in rural and urban communities that have been left behind by venture capital investment.

3. Why do you think low-income students are underrepresented in STEM fields and how do you think the government can help fix that problem?

I think a major part of the problem is that schools serving low-income communities don’t have the resources they need to provide a quality STEM education to every student. To fix that, I have an education plan that will increase investment in STEM education and use Title I funding to eliminate the $ 23 billion annual funding gap between predominantly white and predominantly black school districts. To encourage students to continue their education after they graduate from high school and ensure every student learns the skills they need, my plan also provides two years of free in-state tuition and fees at a public university, community college, or technical school to everyone who completes one year of my mandatory national service program.

4. Do you plan on backing and rolling out paper-only ballots or paper-verified election machines? With many stakeholders in the private sector and the government, how do you aim to coordinate and achieve that?

Making sure that our elections are secure is vital, and I think using voting machines that create a voter-verified paper record could improve security and increase voters’ confidence in the integrity of our elections. To address other facets of the election security issue, I have proposed creating a Department of Cybersecurity to help protect our election systems, and while in Congress I introduced election security legislation to ensure that election vendors are solely owned and controlled by American citizens.

5. What, if any, federal regulation should be enacted for autonomous vehicles?

I was proud to be the founder of the Congressional Artificial Intelligence Caucus, a bipartisan group of lawmakers dedicated to understanding the impacts of advances in AI technology and educating other legislators so they have the knowledge they need to enact policies that ensure these innovations benefit Americans. We need to use the legislative process to have a real conversation involving experts and other stakeholders in order to develop a comprehensive set of regulations regarding autonomous vehicles, which should include standards that address data collection practices and other privacy issues as well as more fundamental questions about public safety.

6. How do you plan to achieve and maintain U.S. superiority in space, both in government programs and private industry?

Space exploration is tremendously important to me as a former Congressman from Maryland, the home of NASA’s Goddard Space Flight Center, major space research centers at the University of Maryland, and many companies that develop crucial aerospace technologies. As president, I will support the NASA budget and will continue to encourage innovation in the private sector.

7. Increased capital in startups founded by American entrepreneurs is a net positive, but should the U.S. allow its businesses to be part-owned by foreign governments, particularly the government of Saudi Arabia?

I am concerned that joint ventures between U.S. businesses and foreign governments, including state-owned enterprises, could facilitate the theft of intellectual property, potentially allowing foreign governments to benefit from taxpayer-funded research. We need to put in place greater protections that defend American innovation from theft.

8. Will U.S.-China technology decoupling harm or benefit U.S. innovation and why?

In general, I am in favor of international technology cooperation but in the case of China, it engages in predatory economic behavior and disregards international rules. Intellectual property theft has become a big problem for American businesses as China allows its companies to steal IP through joint ventures. In theory, U.S.-China collaboration could advance technology and innovation but without proper IP and economic protections, U.S.-China joint ventures and partnerships can be detrimental to the U.S.

9. How large a threat does automation represent to American jobs? Do you have a plan to help train low-skilled workers and otherwise offset job loss?

Automation could lead to the disruption of up to 54 million American jobs if we aren’t prepared and we don’t have the right policies. To help American workers transition to the high-tech, high-skill future economy, I am calling for a national AI strategy that will support public/private AI partnerships, develop a social contract with the communities that are negatively impacted by technology and globalization, and create updated education and job training programs that will help students and those currently in the workforce learn the skills they need.

To help provide jobs to displaced workers and drive economic growth in communities that suffer negative effects from automation, I have proposed a $ 2 trillion infrastructure plan that would create an infrastructure bank to facilitate state and local government investment, increase the Highway Trust Fund, create a Climate Infrastructure Fund, and create five new matching funds to support water infrastructure, school infrastructure, deferred maintenance projects, rural broadband, and infrastructure projects in disadvantaged communities in urban and rural areas. In addition, my proposed national service program will create new opportunities that allow young adults to learn new skills and gain valuable work experience. For example, my proposal includes a new national infrastructure apprenticeship program that will award a professional certificate proving mastery of particular skill sets for those who complete the program.

10. What steps will you take to restore net neutrality and assure internet users that their traffic and data are safe from manipulation by broadband providers?

I support the Save Net Neutrality Act to restore net neutrality, and I will appoint FCC commissioners who are committed to maintaining a fair and open internet. Additionally, I would work with Congress to update our digital privacy laws and regulations to protect consumers, especially children, from their data being collected without consent.


Enterprise – TechCrunch


How would Google Answer Vague Questions in Queries?

July 19, 2019 No Comments

“How Long is Harry Potter?” is asked in a diagram from a Google Patent. The answer to this vague question is unlikely to do with a length related to the fictional character but may have something to do with one of the best selling books or movies featuring Harry Potter.

When questions are asked as queries at Google, sometimes they aren’t asked clearly, with enough preciseness to make an answer easy to provide. How does Google Answer vague questions?

Question answering seems to be a common topic in Google Patents recently. I wrote about one not long ago in the post, How Google May Handle Question Answering when Facts are Missing

So this post is also on question answering but involves issues involving the questions rather than the answers. And particularly vague questions.

Early in the description for a recently granted Google Patent, we see this line, which is the focus of the patent:

Some queries may indicate that the user is searching for a particular fact to answer a question reflected in the query.

I’ve written a few posts about Google working on answering questions, and it is good seeing more information about that topic being published in a new patent. As I have noted, this one focuses upon when questions asking for facts may be vague:

When a question-and-answer (Q&A) system receives a query, such as in the search context, the system must interpret the query, determine whether to respond and if so, select one or more answers with which to respond. Not all queries may be received in the form of a question, and some queries might be vague or ambiguous.

The patent provides an example query for “Washington’s age.”

Washington’s Age could be referring to:

  • President George Washington
  • Actor Denzel Washington
  • The state of Washington
  • Washington D.C.

For the Q&A system to work correctly, it would have to decide which the searcher who typed that into a search box the query was likely interested in finding the age of one of the Washingtons. Trying that query, Google decided that I was interested in George Washington:

Answering vague questions

The problem that this patent is intended to resolve is captured in this line from the summary of the patent:

The techniques described in this paper describe systems and methods for determining whether to respond to a query with one or more factual answers, including how to rank multiple candidate topics and answers in a way that indicates the most likely interpretation(s) of a query.

How would Google potentially resolve vague questions problem?

It would likely start by trying to identify one or more candidate topics from a query. It may try to generate, for each candidate topic, a candidate topic-answer pair that includes both the candidate topic and an answer to the query for the candidate topic.

It would obtain search results based on the query, which references an annotated resource, which would be is a resource that, based on automated evaluation of the content of the resource, is associated with an annotation that identifies one or more likely topics associated with the resource. For each candidate topic-answer pair,

There would be a Determination of a score for the candidate topic-answer pair based on:

(i) The candidate topic appearing in the annotations of the resources referenced by one or more of the search results
(ii) The query answer appearing in annotations of the resources referenced by the search results, or in the resources referenced by the search results.

A decision would also be made on whether to respond to the query, with one or more answers from the candidate topic-answer pairs, based on the scores for each.

Topic-Answer Scores

topic-answer scores

The patent tells us about some optional features as well.

  1. The scores for the candidate topic-answer pairs would have to meet a predetermined threshold
  2. This process may decide to not respond to the query with any of the candidate topic answer pairs
  3. One or More of the highest-scoring topic-answer pairs might be shown
  4. An topic-answer might be selected from one of a number of interconnected nodes of a graph
  5. The Score for the topic-answer pair may also be based upon a respective query relevance score of the search results that include annotations in which the candidate topic occurs
  6. The score to the topic-answer pair may also be based upon a confidence measure associated with each of one or more annotations in which the candidate topic in a respective candidate topic-answer pair occurs, which could indicate the likelihood that the answer is correct for that question

Knowledge Graph Connection to Vague Questions?

vague answers answered with Knowledge base

This question-answering system can include a knowledge repository which includes a number of topics, each of which includes attributes and associated values for those attributes.

It may use a mapping module to identify one or more candidate topics from the topics in the knowledge repository, which may be determined to relate to a possible subject of the query.

An answer generator may generate for each candidate topic, a candidate topic-answer pair that includes:

(i) The candidate topic, and
(ii) An answer to the query for the candidate topic, wherein the answer for each candidate topic is identified from information in the knowledge repository.

A search engine may return search results based on the query, which can reference an annotated resource. A resource that, based on automated evaluation of the content of the resource, is associated with an annotation that identifies one or more likely topics associated with the resource.

A score may be generated for each candidate topic-answer pair based on:

(i) An occurrence of the candidate topic in the annotations of the resources referenced by one or more of the search results
(ii) An occurrence of the answer in annotations of the resources referenced by the one or more search results, or in the resources referenced by the one or more search results. A front-end system at the one or more computing devices can determine whether to respond to the query with one or more answers from the candidate topic-answer pairs, based on the scores.

The additional features above for topic-answers appears to be repeated in this knowledge repository approach:

  1. The answering system may decide to respond or not to the query based on a comparison of one or more of the scores to a predetermined threshold
  2. Each of the number of topics that in the knowledge repository can be represented by a node in a graph of interconnected nodes
  3. The returned search results can be associated with a respective query relevance score and the score can be determined by the scoring module for each candidate topic-answer pair based on the query relevance scores of one or more of the search results that reference an annotated resource in which the candidate topic occurs
  4. For one or more of the candidate topic-answer pairs, the score can be further based on a confidence measure associated with each of one or more annotations in which the candidate topic in a respective candidate topic-answer pair occurs, or each of one or more annotations in which the answer in a respective candidate topic-answer pair occurs

Advantages of this Vague Questions Approach

  1. Candidate responses to a query can be scored so that a Q&A system or method can decide whether to respond to the query.
  2. If the query does not ask a question or if none of the candidate answers are sufficiently relevant to the query, then no response may be provided
  3. The techniques described here may interpret a vague or ambiguous query and provide a response that is most likely to be relevant to what a searcher was looking for when submitting the query.

This patent on answering vague questions is:

Determining question and answer alternatives
Inventors: David Smith, Engin Cinar Sahin, and George Andrei Mihaila
Assignee: Google Inc.
US Patent: 10,346,415
Granted: July 9, 2019
Filed: April 1, 2016

Abstract

A computer-implemented method can include identifying one or more candidate topics from a query. The method can generate, for each candidate topic, a candidate topic-answer pair that includes both the candidate topic and an answer to the query for the candidate topic. The method can obtain search results based on the query, wherein one or more of the search results references an annotated resource. For each candidate topic-answer pair, the method can determine a score for the candidate topic-answer pair for use in determining a response to the query, based on (i) an occurrence of the candidate topic in the annotations of the resources referenced by one or more of the search results, and (ii) an occurrence of the answer in annotations of the resources referenced by the one or more search results, or in the resources referenced by the one or more search results.

Vague Questions Takeaways

I am reminded of a 2005 Google Blog post called Just the Facts, Fast when this patent tells us that sometimes it is “most helpful to a user to respond directly with one of more facts that answer a question determined to be relevant to a query.”

The different factors that might be used to determine which answer to show if an answer is shown, includes a confidence level, which may be confidence that an answer to a question is correct. That reminds me of the association scores of attributes related to entities that I wrote about in Google Shows Us How It Uses Entity Extractions for Knowledge Graphs. That patent told us that those association scores for entity attributes might be generated over the corpus of web documents as Googlebot crawled pages extracting entity information, so those confidence levels might be built into the knowledge graph for attributes that may be topic-answers for a question answering query.

A webpage that is relevant for such a query, and that an answer might be taken from may be used as an annotation for a displayed answer in search results.


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Critical Questions to Ask When Marketing Across Channels

June 4, 2019 No Comments

In this webinar, you’ll learn all the questions you and your agency should be discussing when implementing integrated marketing campaigns. Save your seat today!

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How Google’s Knowledge Graph Updates Itself by Answering Questions

February 14, 2019 No Comments

How A Knowledge Graph Updates Itself

unsplash-logoElijah Hail

To those of us who are used to doing Search Engine Optimization (SEO), we’ve been looking at URLs filled with content, and links between that content, and how algorithms such as PageRank (based upon links pointed between pages) and information retrieval scores based upon the relevance of that content have been determining how well pages rank in search results in response to queries entered into search boxes by searchers. Web pages connected by links have been seen as information points connected by nodes. This was the first generation of SEO.

Chances are good that many of the methods that we have been using to do SEO will remain the same as new features appear in search, such as knowledge panels, rich results, featured snippets, structured snippets, search by photography, and expanded schema covering many more industries and features then it does at present.

Search has been going through a transformation. Back in 2012, Google introduced something it refers to as the knowledge graph, in which they told us that they would begin focusing upon indexing things instead of strings. By “strings,” they were referring to words that appear in queries, and in documents on the Web. By “things,” they were referring to named entities, or real and specific people, places, and things. When people searched at Google, the search engines would show Search Engine Results Pages (SERPs) filled with URLs to pages that contained the strings of letters that we were searching for. Google still does that, and is slowly changing to showing search results that are about people, places, and things.

Google started showing us in patents how they were introducing entity recognition to search, as I described in this post:
How Google May Perform Entity Recognition

They now show us knowledge panels in search results that tell us about the people, places, and things they recognize in the queries we perform. In addition to crawling webpages and indexing the words on those pages, Google is collecting facts about the people, places, and things it finds on those pages.

A Google Patent that was just granted in the past week tells us about how the Google knowledge graph updates itself when it collects information about entities, their properties and attributes and relationships involving them. This is part of the evolution of SEO that is taking place today – learning how Search is changing from being based upon search to being based upon knowledge.

What does the patent tell us about knowledge? This is one of the sections that details what a knowledge graph is like that Google might collect information about when it indexes pages these days:

Knowledge graph portion includes information related to the entity [George Washington], represented by [George Washington] node. [George Washington] node is connected to [U.S. President] entity type node by [Is A] edge with the semantic content [Is A], such that the 3-tuple defined by nodes and the edge contains the information “George Washington is a U.S. President.” Similarly, “Thomas Jefferson Is A U.S. President” is represented by the tuple of [Thomas Jefferson] node 310, [Is A] edge, and [U.S. President] node. Knowledge graph portion includes entity type nodes [Person], and [U.S. President] node. The person type is defined in part by the connections from [Person] node. For example, the type [Person] is defined as having the property [Date Of Birth] by node and edge, and is defined as having the property [Gender] by node 334 and edge 336. These relationships define in part a schema associated with the entity type [Person].

Note that SEO is no longer just about how often certain words appear on pages of the Web, what words appear in links to those pages, in page titles, and headings, alt text for images, and how often certain words may be repeated or related words may be used. Google is looking at the facts that are mentioned about entities, such as entity types like a “person,” and properties, such as “Date of Birth,” or “Gender.”

Note that quote also mentions the word “Schema” as in “These relationships define in part a schema associated with the entity type [Person].” As part of the transformation of SEO from Strings to Things, The major Search Engines joined forces to offer us information on how to use Schema for structured data on the Web to provide a machine readable way of sharing information with search engines about the entities that we write about, their properties, and relationships.

I’m writing about this patent because I am participating in a Webinar online about the Google Knowledge Graph and how it is being used, and updated. The Webinar is tomorrow at:
#SEOisAEO: How Google Uses The Knowledge Graph in its AE algorithm. I haven’t been referring to SEO as Answer Engine Optimization, or AEO and it’s unlikely that I will start, but see it as an evolution of SEO

I’m writing about this Google Patent, because it starts out with the following line which it titles “Background:”

This disclosure generally relates to updating information in a database. Data has previously been updated by, for example, user input.

This line points to the fact that this approach no longer needs to be updated by users, but instead involves how Google knowledge graphs update themselves.

Updating a Knowledge Graph

I attended a Semantic Technology and Business conference a couple of year ago, where the head of Yahoo’s knowledge base presented, and he was asked a number of questions in a question and answer session after he spoke. Someone asked him what happens when information from a knowledge graph changes and it involves very sensitive information, and needs to be updated?

His answer was that a knowledge graph would have to be updated manually to have new information placed within it.

That wasn’t a satisfactory answer because it would have been good to hear that the information from such a source could be easily updated, and it was a little difficult hearing that a search engine would need to be edited like a newspaper would be. This may have been the answer that the people from Yahoo believed was the proper answer, and I’ve been waiting for Google to answer a question like this to see what their answer would be. That made seeing a line like this one from this patent interesting:

In some implementations, a system identifies information that is missing from a collection of data. The system generates a question to provide to a question answering service based on the missing information, and uses the response from the question answering service to update the collection of data.

This would be a knowledge graph update, so that patent provides details using language that reflects that exactly:

In some implementations, a computer-implemented method is provided. The method includes identifying an entity reference in a knowledge graph, wherein the entity reference corresponds to an entity type. The method further includes identifying a missing data element associated with the entity reference. The method further includes generating a query based at least in part on the missing data element and the type of the entity reference. The method further includes providing the query to a query processing engine. The method further includes receiving information from the query processing engine in response to the query. The method further includes updating the knowledge graph based at least in part on the received information.

How does the search engine do this? The patent provides more information that fills in such details.

The approaches to achieve this would be to:

…Identifying a missing data element comprises comparing properties associated with the entity reference to a schema table associated with the entity type.

…Generating the query comprises generating a natural language query. This can involve selecting, from the knowledge graph, disambiguation query terms associated with the entity reference, wherein the terms comprise property values associated with the entity reference, or updating the knowledge graph by updating the data graph to include information in place of the missing data element.

…Identifying an element in a knowledge graph to be updated based at least in part on a query record. Operations further include generating a query based at least in part on the identified element. Operations further include providing the query to a query processing engine. Operations further include receiving information from the query processing engine in response to the query. Operations further include updating the knowledge graph based at least in part on the received information.

A knowledge graph updates itself in these ways:

(1) The knowledge Graph may be updated with one or more previously performed searches.
(2) The knowledge Graph may be updated with a natural language query, using disambiguation query terms associated with the entity reference, wherein the terms comprise property values associated with the entity reference.
(3) The knowledge Graph may use properties associated with the entity reference to include information updating missing data elements.

The patent that describes how Google’s knowledge graph updates themselves is:

Question answering to populate knowledge base
Inventors: Rahul Gupta, Shaohua Sun, John Blitzer, Dekang Lin, and Evgeniy Gabrilovich
Assignee: Google
US Patent: 10,108,700
Granted: October 23, 2018
Filed: March 15, 2013

Abstract

Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.

Nicolas Torzec tweeted me a link to a paper published on the Google AI Blog, which shares a number of authors with this patent. It was posted in 2014 (a year after the patent this post is about was filed.) The paper explains in more detail how a knowledge graph might become more complete. As the Abstract of the paper tells us:

We discuss how to aggregate candidate answers across multiple queries, ultimately returning probabilistic predictions for possible values for each attribute. Finally, we evaluate our system and show that it is able to extract a large number of facts with high confidence.

The paper is Knowledge Base Completion via Search-Based Question Answering Reading this paper in addition to the patent is recommended. It presents a much more nuanced look at some of the issues that the people working upon this problem came across, and some of the solutions that they found to address those. One of the problems that they use to illustrate how this system works involves identifying the parents of Frank Zappa (His Band was named “The Mothers of Invention” which made that task have some issues unique, as well.)

It does seem like it is a difficult task trying to update a knowledge graph using questions and answers like this, and is a problem that faces some challenges. It is interesting seeing what stage we are at in having problems like this addressed – so read this paper carefully along with the patent.

We have been seeing other approaches that look at a knowledge graph from other directions such as:

3 Ways Query Stream Ontologies Change Search – this is about Google looking at query stream information to identify data that it can extract from the Web to use to build ontologies. By looking at searchers queries, in effect it is crowdsourcing information about topics that may be helpful in building those ontologies.

Constructing Knowledge Bases with Context Clouds – This tells us about how Google could look at unstructured content that it might be able to use to build up knowledge bases. We see statements like this from the patent the post is about:

Extending the number of attributes known to a search engine may enable the search engine to answer more precisely queries that lie outside a “long tail,” of statistical query arrangements, extract a broader range of facts from the Web, and/or retrieve information related to semantic information of tables present on the Web.

We haven’t reached the point where updating or building a knowledge base can be automated, and updating some knowledge graph information about some sensitive topics that change may be necessary still, but we have some examples of approaches that are underway towards such updates a possibility.


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Leena AI builds HR chatbots to answer policy questions automatically

June 30, 2018 No Comments

Say you have a job with a large company and you want to know how much vacation time you have left, or how to add your new baby to your healthcare. This usually involves emailing or calling HR and waiting for an answer, or it could even involve crossing multiple systems to get what you need.

Leena AI, a member of the Y Combinator Summer 2018 class, wants to change that by building HR bots to answer questions for employees instantly.

The bots can be integrated into Slack or Workplace by Facebook and they are built and trained using information in policy documents and by pulling data from various back-end systems like Oracle and SAP.

Adit Jain, co-founder at Leena AI, says the company has its roots in another startup called Chatteron, which the founders started after they got out of college in India in 2015. That product helped people build their own chatbots. Jain says along the way, they discovered while doing their market research a particularly strong need in HR. They started Leena AI last year to address that specific requirement.

Jain says when building bots, the team learned through its experience with Chatteron that it’s better to concentrate on a single subject because the underlying machine learning model gets better the more it’s used. “Once you create a bot, for it to really add value and be [extremely] accurate, and for it to really go deep, it takes a lot of time and effort and that can only happen through verticalization,” Jain explained.

Photo: Leena AI

What’s more, as the founders have become more knowledgeable about the needs of HR, they have learned that 80 percent of the questions cover similar topics, like vacation, sick time and expense reporting. They have also seen companies using similar back-end systems, so they can now build standard integrators for common applications like SAP, Oracle and NetSuite.

Of course, even though people may ask similar questions, the company may have unique terminology or people may ask the question in an unusual way. Jain says that’s where the natural language processing (NLP) comes in. The system can learn these variations over time as they build a larger database of possible queries.

The company just launched in 2017 and already has a dozen paying customers. They hope to double that number in just 60 days. Jain believes being part of Y Combinator should help in that regard. The partners are helping the team refine its pitch and making introductions to companies that could make use of this tool.

Their ultimate goal is nothing less than to be ubiquitous, to help bridge multiple legacy systems to provide answers seamlessly for employees to all their questions. If they can achieve that, they should be a successful company.


Enterprise – TechCrunch


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