Disqus - Latest Comments for alltoute

Re: Twitter Lists in Action: NHL Builds a Social Network for Fans

alltoute — Mon, 02 Nov 2009 16:39:29 -0000

Great idea, my only concern is that popularity could easily make the content stream from that kind of list totally irrelevant (not related to hockey): 1) Most hockey fans are not only tweeting about hockey (I'm a hardcore hockey fan, but most of my tweets are not about hockey) 2) It's on request, so spammer, corpo accounts, marketing, etc. could be easily added. Managing such lists is no easy task. Wondering if there is also a limit on the number of user a list can be made of?

Re: Digital search vs. human search: Exploring a premise and citing an example

alltoute — Tue, 21 Apr 2009 13:14:27 -0000

I agree, but credibility of a user is not so different than the credibility of a web site for Google. It's more personal and thus closer to interests. It really depends on what kind of things you need to find and what kind of search you need to do: discovery, exploration, simple facts, website, opinions, etc. Social based search have a lot of potential, but I'm pretty sure it can’t do well alone for all kinds of search. A given user is maybe credible and shares similar interests with you, but he is maybe also a lot into lawn mowers and you maybe not :-) That's why I still believe that semantic content and interests representations are also part of the solutions.
About semantic representation, I think that it's an error to reduce the semantic web to the linked data. I completely agree that ambiguity will always remain. Semantic technologies such as semantic search can also “reach semantics” using analytics techniques like text mining. Classifications are necessary and helpful in various situations, but they have limitations because they are rigid and subjective and that's why context analysis remains a key differentiator. Once again, there is no magic solution. I'm a hybrid technologies believer, especially when we talk about text technologies.

Re: Digital search vs. human search: Exploring a premise and citing an example

alltoute — Thu, 16 Apr 2009 14:47:18 -0000

Sorry to join this interesting discussion late. I agree and partially disagree with several things in here :-) so I will try to focus on the latest comments only.

Speaking of what I call “social signatures”, I agree that a search engine on top of social network content works best when you take into account not only the pages, but also their readership, their general content consumption patterns, their profiles, their social graphs, etc. Nevertheless, I believe that one can reach much higher levels of accuracy/context if he could analyze what the content is about. That's where tagging, text mining and semantic search comes in (analysis of content aboutness). A combination of these 2 types of information (what content is about and what user is interested in) is key for a killer application for mulidimentional and global content access system.

A good example where the human factor can’t really help is at the moment of creation/ingestion of a new piece of content. How can the algorithm tell you that this piece of content is relevant to you, based on your interest or on the interest of your best social connections? How could it evaluate some sort of relevancy at this time using only your user profile? It knows what you like (based on your past connections/content) but it does not know what exactly the new piece of content is about.

On the other hand, if you can text-mine content to obtain a semantic representation (database-like structure of each piece of content) of it, you could match that semantic map with semantic maps of other documents and then leverage the similarities in content the same way as you would leverage similarities of semantic user profiles. For example if you already consumed content, that is similar semantically to this new piece, then this new piece is highly likely to be relevant to you.

Semantic representation of a given user group interests set is also pretty interesting. In terms of search, content discovery and navigation, semantic annotations are really helpful to slow down content stream, better handle noise and help create different view levels over and between content and people.

Again, a combination of these 2 approaches (semantic content analysis and user profile analysis) works best to build a more complete collective intelligence system. In this discussion we talk about its use as a search engine, while it could be the framework to do more than just meet basic search needs.

Re: Google & Twitter & Facebook, Oh my: Does the organization of content change?

alltoute — Tue, 07 Apr 2009 22:58:50 -0000

I think more and more news web sites are going in that directions, we also have to remember that semantic web is still in progression. I like "drive it's own usage" :-) I think it is one of the most interesting application that could be build on top of a semantic search platform.

Re: Google & Twitter & Facebook, Oh my: Does the organization of content change?

alltoute — Tue, 07 Apr 2009 10:15:37 -0000

Sorry for the long comment :-)
That kind of markup surely requires some help from automatic semantic annotations technologies (named entities extraction, automatic categorization, sentiment analysis, fact extraction -> text analytics in general). But I think the real catalyst to Web level adoption will be a question of SEO. If someday websites owners are faced with a major SEO problem because of bad (or absence of) semantic markup, then they will have "no choice" to join the parade. And when we talk about SEO, we talk about search engines. That's the main test for linked data: major search engines have to leverage semantic annotations. Yahoo! is already trying interesting things right now (Yahoo! Boss * Search Monkey) and it's not because Google is not aggressive on that field that they can't do it. Right now it makes more sense for a company to invest in semantic technologies at the enterprise level or inside a news web site for example because they have an immediate significative ROI. Talking about Google, the thing a lot of people forget is that semantic search can also be done without linked data. Semantic Search and Linked Data are 2 different things. Google is not completely against the Linked Data and Semantic Web idea. They seems to just believe that unstructured information and semantic annotations themself are not sufficient to resolve all the problems. And again, to be able to play on that level of disambiguation, text analytics and related technologies are the key. Context analysis will always be necessary and I think that's one of the Twitter disadvantage: context is in a lot of cases very poor or contained in a link. They will need to go on the Web and the Web level is a very serious technical challenge. It's also difficult to imagine how deep semantic annotations can be included in a Twitter post right now.