Internet of Things: From Facts to Meanings


Contrary to what the name may suggest, the so-called Internet-of-Things (IoT) is less about objects than facts: the network knows nothing until notified by events about some changes, and even then, it’s not sure about who or what sent it. Not to mention what it means.

Those two levels, identities and meanings, coincide with two basic search mechanisms, one looking for identified items and the other for information content. While semantic web approaches are meant to deal with the latter, it cannot be isolated from the former; hence, the need to bring the problems (a web of things and meanings) and the solutions (search strategies) within an integrated perspective.

Down with the System Aristocrats

The so-called ‘internet second revolution’ can be summarised as the end of privileged netizenship: down with the aristocracy of systems with their absolute lid on internet residency – within the new web, everything should be entitled to have a voice.


As nothing can be assumed about the things behind events, netizens must be characterised with regard to their identification and communication capabilities:

  • Humans have inherent identities and can exchange symbolic and non symbolic data;
  • Systems don’t have inherent identities and can only exchange symbolic data;
  • Devices don’t have inherent identities and can only exchange non symbolic data; and
  • Animals have inherent identities and can only exchange non symbolic data.

The so-called ‘internet second revolution’ can be summarised as the end of privileged netizenship: down with the aristocracy of systems with their absolute lid on internet residency – within the new web,  everything should be entitled to have a voice.

Along that perspective, speaking about the ‘internet of things’ can be misleading because the primary transformation goes the other way: many systems initially embedded within appliances (e.g. cell phones) have made their coming out by adding symbolic user interfaces, mutating from devices into fully fledged systems.

Physical Integration: The Meaning of Things

With embedded systems colonising every nook and cranny of the world, the supposedly innate hierarchical governance of systems over objects is challenged as the latter calls for full internet citizenship. Those new requirements can be expressed in terms of architecture capabilities:

  • Anywhere (Where): objects must be localised independently of systems. That’s customary for physical objects (e.g. Geo-localisation), but may be more challenging for digital ones on they way across the net;
  • Anytime (When): behaviours must be synchronised over asynchronous communication channels. Existing mechanism used for actual processes (e.g. Network Time protocol) may have to be set against modal logic if it is used for their representation;
  • Anybody (Who): while business systems don’t like anonymity and rely on their interfaces to secure access, things of the internet are to be identified whatever their interface (e.g. RFID);
  • Anything (What): objects must be managed independently of their nature, symbolic or otherwise (e.g. 3D printed objects);
  • Anyhow (How): contrary to business systems, processes don’t have to follow predefined scripts and versatility and non determinism are the rules of the game.

Taking a sortie in a restaurant for example: the actual event is associated to a reservation, car(s) and phone(s) are active objects geo-localised at a fixed place and possibly linked to diners, great wines can be authenticated directly by smart-phone applications, phones are used for conversations and pictures, possibly for adding to reviews, friends in the neighbourhood can be automatically informed of the sortie and invited to join.


As this simple example illustrates, the internet of things brings together dumb objects, smart systems, and knowledgeable documents. Navigating such a tangle will require more than the Semantic Web initiative because its purpose points in the opposite direction, back to the origin of the internet, namely how to extract knowledge from data and information.

Moreover, while most of those ‘things’ fall under the governance of the traditional internet of systems, the primary factor of change comes from the exponential growth of smart physical things with systems of their own. When those systems are ’embedded’, the representations they use are encapsulated and cannot be accessed directly as symbolic ones. In other words, those agents are governed by hidden agendas inaccessible to search engines. That problem is illustrated acontrario (things are not services) by services oriented architectures whose primary responsibility is to support services discovery.

Semantic Integration: The Actuality of Meanings

The internet of things is supposed to provide a unified perspective on physical objects and symbolic representations, with the former taken as they come and instantly donned in some symbolic skin, and the latter boiled down to their documentary avatars (as Marshall McLuhan famously said, “the medium is the message”).

Unfortunately, this goal is also a challenge because if physical objects can be uniformly enlisted across the web, that’s not the case for symbolic surrogates which are specific to social entities and managed by their respective systems accordingly.

With the Internet of Systems, social entities with common endeavours agree on shared symbolic representations and exchange the corresponding surrogates as managed by their systems. The Internet of Things, for its part, is meant to put an additional layer of meanings supposedly independent of those managed at systems level. As far as meanings are concerned, the latter is flat, the former is hierarchised.

That goal raises two questions: (1) what belongs to the part governed by the internet of things; and (2) how is its flattened governance to be related to the structured one of the internet of systems.

Organisational vs Social Meanings

As epitomised by handshakes and contracts, symbolic representations are all about how social behaviours are sanctioned.


The first generations of those internet robots (aka bots) were limited to automated tasks, performed on the account of their parent systems, to which they were due to report. Such neat hierarchical governance is being called into question by bots fired and forgotten by their maker, free of reporting duties, their life wholly governed by social events. That’s the case with the internet of things, with significant consequences for searches.

In system-based networks, representations and meanings are defined and governed by clearly identified organisations, corporate or otherwise. That’s not necessarily the case for networks populated by software agents performing unsupervised tasks.

As noted above, the internet of things can consistently manage both system-defined identities and the new ones it introduces for things of its own. But, given a network job description, the same consolidation cannot be even considered for meanings: networks are supposed to be kept in complete ignorance of contents, and walls between addresses and knowledge management must tower well above the clouds. As a corollary, the overlapping of meanings is bound to grow with the expanse of things, and the increase will not be linear.


That brings some light on the so-called ‘virtual world’, one made of representations detached from identified roots in the actual world. And there should be no misunderstanding: ‘actual’ doesn’t refer to physical objects but to objects and behaviours sanctioned by social entities, as opposed to virtual, which includes the ones whose meaning cannot be neatly anchored to a social authority.

That makes searches in the web of things doubly challenging as they have to deal with both overlapping and shifting semantics.

Things and Symbolic Representations

Semantic searches (forms and pattern matching should be seen as a special case) can be initiated by any kind of textual input, key words or phrase. As searches, they should first be classified with regard to their purpose: finding some specific instance or collecting information about some topic.

Considering that finding referenced objects is basically an indexing problem, and that pattern matching is a discipline of its own, the focus is to be put on searches driven by words (as opposed to identifiers and forms). From that standpoint searches are best understood in the broader semantic context of extensions and intensions, the former being the actual set of objects and phenomena, the latter a selected set of features shared by these instances.


A search can therefore be seen as an iterative process going back and forth between descriptions and occurrences or, more formally, between intentions and extensions. Depending on the request, iterations are made of four steps:

  • Given a description (intension), find the corresponding set of instances (extension); e.g. ‘restaurants’ > a list of restaurants;
  • Given an instance (extension), find a description (intension); e.g. ‘Alberto’s Pizza’ > “pizzerias”;
  • Extend or generalise a description to obtain a better match to request and context; e.g. ‘pizzerias’ > ‘Italian restaurants’;
  • Trim or refine instances to obtain a better match to request and context; e.g. a list of restaurants > a list of restaurants in the Village.

Iterations are repeated until the outcome is deemed to satisfy the quality parameters.

The benefit of those distinctions is to introduce explicit decision points with regard to the reference models heeding the searches. Depending on purpose and context, such models could be:

  • Inclusive: can be applied to any kind of search;
  • Semantic: can only be applied to circumscribed domains of knowledge. That’s the aim of the semantic web initiative and the Web Ontology Language (OWL);
  • Organisational: can only be applied within specific institutional or business contexts. They could be available to all or through services with restricted access and use.

From Meanings to Things, and back

The stunning performances of modern search engines comes from a combination of brawn and brains, the brainy part for grammars and statistics, the brawny one for running heuristics on gigantic repositories of linguistic practices and web researches. Moreover, those performances improve ‘naturally’ with the accumulation of data pertaining to virtual events and behaviours.

Nonetheless, search engines have grey or even blind spots, and there may be a downside to the accumulation of social data, as it may increase the gap between social and corporate knowledge, and consequently the coherence of outcomes.

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That can be illustrated by a search about Amedeo Modigliani:

  • A inclusive search for ‘Modigliani’ will use heuristics to identify the artist (a). An organisational search for an homonym (e.g. a bank customer) would be dealt with at enterprise level, possibly through an Intranet (c);
  • A search for ‘Modigliani’s friends’ may look for the artist’s Facebook friends if kept at the inclusive level (a1), or switch to a semantic context better suited to the artist (a2). The same outcome would have been obtained with a semantic search (b);
  • Searches about auction prices may be redirected or initiated directly, possibly subject to authorisation (c).

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