🕸️Web x.0: Is the Dot-Com Bubble 2.0 on its Way?

 

 The First Dot-Com Bubble

The dot-com bubble (aka Internet bubble) refers to the time period between 1995 and early spring of 2000, when the American economy underwent through a considerable boom, fact reflected in the increase in stock prices, especially the one associated with Internet-related assets [1]. The principal fuel for dot-com bubble seems to be the Internet and its huge potential for high-tech as well for non-high-tech companies. Data were communicated in real time at affordable prices, through web sites companies were having the potential of being known and of reaching potential customers all over the world, universities and companies could easier collaborate. It was the naissance of Internet phone, multicasting, e-commerce and e-auction portals, online banking and collaborative tools. The enormous potential was increasingly reflected in the news. 

Forbes and Wall Street Journal were encouraging people to invest in risky companies [4]. The public awareness was increasing [1], and the offer was huge. If previously a company needed to have had at least several profitable quarters before it went public, by 1999 the restrictions were relaxed considerably, it was enough to have a sketchy business plan, an Internet address and a few people who could speak the right jargon [1]. Numerous start-ups entered the marked over night, their entry being facilitated by the lower entry cost associated with the innovation [3], the sudden appearance of new niche markets and the demand coming from early adopters, eager to make most of the new technologies.

Many start-up companies like Google, Amazon or Netscape haven’t made a profit during the first years, however the high IPO value of their stocks allowed them to raise a substantial amount of money [4]. As the stocks of many companies were skying high, more and more money were pushed in the economy, many of the investments being driven by the mirage of getting rich over night. The investments in software, computer and communication equipment companies grow, IT becoming an important component of the US economy [2]. In fact, by October 1999, the stock value of the six biggest IT companies – Microsoft, Intel, IBM, Cisco, Lucent and Dell - was 20 percent of the US GDP [5]. Cities in US, trapped by the dream of becoming a new “Silicon Valley”, invested in their communication infrastructure and built network enabled offices to attract internet entrepreneurs [4]. Europe joined the rush as well, telecommunication companies investing in 3G licenses [4], companies were expanding to accommodate the increase demand coming from US.

Excessive IT investment, overconfidence and other factors made the bubble to burst in the spring of 2000. It was a turning point for the American economy as well for Europe, the dot-com stocks falling down, following the exit and bankruptcy of many dot-com companies. It was also the chance for other IT and non-IT companies to take over the assets of fallen companies. It was the time for acquisitions and mergers in order to survive the crisis. It was an opportunity for innovation to propagate and workforce to migrate from company to company. The investments in IT infrastructure continued moderately also after the burst, the economy revigorating itself slowly but steadily.

The Second Dot-Com Bubble?
 
Marc Andreeson, founder of Netscape, pointed out recently that “the ideas on the internet in the 1990s are all happening now” [7], and he seems to be right. The overevaluated IPOs of social networking companies like LinkedIn, Facebook or Groupon [6] seem to support the premises of a second dot-com bubble (aka dot-com bubble 2.0, to follow the trend, social networking bubble or social media bubble). Of course, social networks companies have a huge potential especially in what concerns the harnessing of collective intelligence and the diffusion of information, with application in multiple domains, but there is lot of work ahead until harnessing this potential. The boom of fancier and miniaturized electronic devices, plus the promises of Social Media, Web 2.0 and thinking further of the Semantic Web (aka Web 3,0), Big Data, Cloud Computing, Personal Cloud, Integrated Ecosystems, Enterprise App Stores and other technologies (e.g. in-memory computing, HTML5, Silverlight) seems to multiply exponentially the value of data, technology and networks. 

There are lots of opportunities for companies to appear over night, for small and average companies to grow, and for big companies to consolidate their position on the market. There is also lot of optimism in what concerns the future of the Web on one side, and the Web-centric organizations and business models on the other side, but is this optimism entitled? Could the boom of these technologies corroborated with the “this-time-is-different” syndrome and ignorance of history, facilitate the appearance of a second Internet bubble?  For sure that’s possible, but hopefully it won’t become reality, at least not in the near future. Hopefully the markets have learned something from the past…

Beyond Bubbles

Despite the negative effects the dot-com bubble had or might have at micro and macro level, I strongly believe that on the long term economies have the capacity to recover, and sometimes such bursts are necessary for the restructuring and re-leveling of values. Despite any future crisis, the Internet will continue its development fueled by the need for more capable technologies and, where technologies and needs are, investments more likely will follow.

References:
[1] G. Callahan, R. W. Garrison () Does Austrian Business Cycle Theory explain the Dot-Com Boom and Bust? [Online] Available from: http://mises.org/journals/qjae/pdf/qjae6_2_3.pdf (Accessed: 31.08.2012).
[2] Wikipedia (2012) Web Development. [Online] Available from: http://en.wikipedia.org/wiki/Web_development (Accessed: 31.08.2012).
[3] http://www.aeaweb.org/annual_mtg_papers/2007/0107_1300_0902.pdf
[4] Wikipedia (2012) Dot-com bubble. [Online] Available from: http://en.wikipedia.org/wiki/Dot-com_bubble (Accessed: 01.09.2012).
[5] M. Buttel (2010) 10 Years On: When the bubble burst. Financial Services Technologies. [Online] Available from: http://www.fsteurope.com/news/when-the-bubble-burst/ (Accessed: 01.09.2012).
[6] North American Value Investing. (2012). Are We in Another Dot Com Bubble? [Online] Available from: http://navinvesting.blogspot.de/2012/05/are-we-in-another-dot-com-bubble.html (Accessed: 02.01.2013)
[7] D. Soskin, (2012) Is the dotcom bubble 2.0 set to burst? [Online] Available from:  http://www.growingbusiness.co.uk/dot-com-bubble-2-0.html (Accessed: 02.01.2013)

Note: The current post was adapted after my assignment submission for the Internet History Technology and Security Coursera Course.

🔖Knowledge Representation: Utilizing Mind Maps as a Structure for Mining the Semantic Web

In the past 2 and half years I followed the Online Masters Programme of University of Liverpool, it was intriguing, fun, time consuming and quite an effort as energy, money and personal life, and I hope it will pay back in time, the sooner the better. The modules were quite entertaining, I learn lot of new stuff and two years passed fast and slower than expected, then the dissertation came and things got pretty tough as I wanted to make it useful for me, to learn something meaningful on which I can built in the future and not something that will rot in a corner of the brain. I was not sure what to choose and frankly not even what I supposed to do.

During the last modules I had the chance to read some material on Tony Buzan’s Mind Maps and it looked intriguing, I wish I had have read that stuff long time ago, but in the end better later than never. Why I found Mind Maps intriguing? First because they allow taking notes in a radiant fashion rather than using the old fashioned linear approach, by starting with a single idea (also referred as concept, subject, question) and built around it a whole Map using associations. In Mind Maps Key-Words are used to encapsulate a variety of meaning in smallest possible units, this step allowing some information filtering and processing, “obligating” the brain to actually integrate the new information in existing knowledge and represent already existing knowledge, identifying missing links, triggering other questions, etc. Thus on a piece of paper or in a electronic document, somebody can represent how concepts in a read material link to each other, making the subject clearer and I think easier to memorize and recall. Mind Maps can be also used to give life to own mental representations, as we all have created, voluntarily or involuntarily and map of the world we live in. Secondly, Mind Maps use symbols and graphical images, visual rhythms and patterns, colour and spatial awareness (dimension and gestalt), allowing people to take advantage of a broader set of cortical skills.

Given these characteristics, Mind Maps seems to be perfect tools for Knowledge Representation in particular and Knowledge Management in general. During the Web Applications module I tangentially learned about XTM (eXtensible Topic Maps) and ontologies for Knowledge Representation, though ontologies call for experts and come with many issues, while XTM is a standard for Knowledge Interchange and targets internal representation in computers. On the other side digital Mind Maps are more flexible than ontologies, target a broader range of users, have the potential of harnessing the Collective Intelligence, one of Web 2.0’s competences, by allowing users to map their knowledge or the knowledge existing on the Web in documents. This is how appeared the title of my Dissertation paper, “Utilizing Mind Maps as a Structure for Mining the Semantic Web”.

While diving in the subject, I found out that Mind Maps are just one of the Knowledge Maps used for various tasks, a search trough the literature revealing about 50 terms used to designate various types of Maps: argument maps, brace maps , bridge maps, bubble maps, causal maps, circle maps, cluster maps, cluster vee diagrams, clustering, cognitive maps, concept circle diagrams, concept maps, conceptual graphs, congregate maps, diagnostic maps, double bubble maps, dynamic cognitive maps, ecological maps, extended fuzzy cognitive maps, flow maps, frames, fuzzy cognitive maps, fuzzy relational maps, group maps, historical maps, idea maps, knowledge maps, mental maps, mind maps, multi-flow maps, neural cognitive maps, neutrosophic cognitive maps, node-link mappings, ontology, oval maps, probability fuzzy cognitive maps, rule-based fuzzy cognitive maps, semantic maps, semantic nets, semantic networks, shared maps, social maps, social mess maps, spider maps, strategy maps, taxonomy, text graphs, thinking maps, tree maps and virtual maps. Actually, the list might be much bigger, I expect I left out by mistake several terms, while on others I haven’t came across them until now.

From several considerations, I preferred to treat the subject from the perspective of Knowledge Maps, so maybe a better title for my paper would have been “Utilizing Knowledge Maps as a Structure for Mining the Semantic Web”. As I found out later this cost me a huge amount of time and effort, I longed for more I could chew in the dedicated amount of time for a Dissertation paper, not having the time to bring the paper to the desired final form, letting out some research material and ideas. Anyway, now it’s over, good or bad the paper is finished and waiting for the final results. With this blog I’m hoping to bring into light some of the ideas I couldn’t put in the paper, help me do to further research into the subject and hopefully get also some feedback.

I’m not sure yet whether I can put the paper in the public domain, therefore here is paper’s Table of Contents, with the mention that some of the topics (e.g. Fuzzy Cognitive Map) have only an informative character.

1. The Web

1.1 Introduction

1.2 Web 2.0

1.3 The Semantic Web

1.4 Semantic Web Problems

1.5 Beyond the Semantic Web

1.5.1 The Noosphere

1.5.2 Cognitive Machines

2. Philosophical Grounds

2.1 Introduction

2.2 From Meaning to Concept

2.3 Syntax, Semantics and Pragmatics

2.4 From data to wisdom

2.5 Types of Knowledge

2.6 Connectivism

2.6.1 Introduction

2.6.2 Chaos

2.6.3 Network

2.6.4 Complexity

2.6.5 Self-organization

2.7 Intelligence and Collective Intelligence

2.7.1 Intelligence

2.7.2 Collective Intelligence

2.7.3 Collective Web Intelligence

2.7.4 Web Technologies and Collective Intelligence

2.7.5 Offline Collective Intelligence

2.7.6. Collective Intelligence Forms

3. Knowledge Management

3.1 Introduction

3.2 Knowledge Representation

3.2.1 Introduction

3.2.2 Sub-conceptual level

3.2.3 Symbolic level

3.2.3.1 Generalities

3.2.3.2 The Frame Problem

3.2.3.4 The Symbol Grounding Problem

3.2.4 Conceptual level

3.2.5 Associationist level

3.2.6 Semantic level

3.3 From Mental Models to Knowledge Representation Structures

3.4 Historical Overview

3.5 Vocabularies

3.5.1 Controlled Vocabularies

3.5.1.1 Introduction

3.5.1.2 Indexing Schemes

3.5.1.3 Classification schemes

3.5.1.4 Thesauri

3.5.1.5 Taxonomies

3.5.2 Uncontrolled Vocabularies

3.5.2.1 Introduction

3.5.2.2 Folksonomies

3.6 Maps

3.6.1 Introduction

3.6.2 Semantic Nets

3.6.3 Frames

3.6.4 Mind Maps

3.6.5 Conceptual Graphs

3.6.6 Concept Maps

3.6.7 Neural Networks

3.6.8 Cognitive Maps

3.6.9 Fuzzy Cognitive Maps

3.6.9.1 Fuzzy Cognitive Maps

3.6.9.2 Rule-Based Fuzzy Cognitive Maps

3.6.9.3 Extended Fuzzy Cognitive Maps

3.6.9.4 Dynamic Cognitive Networks

3.6.9.5 Neural Cognitive Map

3.6.9.6 Neutrosophic Cognitive Maps

3.6.9.7 Probability Fuzzy Cognitive Maps

3.6.9.8 Fuzzy Relational Maps

3.6.10 Knowledge Maps

3.6.11 Topic Maps

3.6.12 Ontologies

3.6.12.1 Ontologies

3.6.12.2 Ontology Engineering

3.6.13 Other Knowledge Representation Structures

3.7 Analyzing Maps

3.7.1 Structural Comparison

3.7.2 Map Engineering

3.7.3 Mapping Tools

3.8 Harnessing Collective Intelligence for Knowledge Mapping

4. Data Mining the Semantic Web

4.1 Web Data Mining

4.2 Document Processing

4.3 The Case for Knowledge Representation Structures as Metadata

4.4. The Conceptual Knowledge Base

4.4.1 Introduction

4.4.2 Representational Elements of a Map

4.4.3 Operations with Maps

4.5 Mapping Descriptive Knowledge with Maps

4.5 Concepts in Documents’ classification

4.5.1 Introduction

4.5.2 Concept-Based Information Retrieval

4.5.3 Concept-Based Document Classification

5. Conclusions, Critics and Further Research

6 Appendix

6.1.Acronyms:

6.2 References: