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Or how to move from a metadata language to a culture of collective intelligence…

THE METADATA ISSUE

Metadata are the data that organize the data. Data are like books in a library and metadata are like the library card index and catalog: their function is to identify the books in order to store and find them better. Metadata are less about describing things exhaustively (it is not about making maps at the same scale as the territory…) than about providing reference points from which users can find what they are looking for, with the help of algorithms. All information systems and software applications organize information through metadata.

We can distinguish between…

material metadata, such as a file’s format, creation date, author, license, etc.
semantic metadata which deals with the content of a document or a set of data (what it is about) as well as its practical dimension (what the data is used for, by whom, in what circumstances, etc.).

The main focus here is on semantic metadata. A semantic metadata system can be as simple as a vocabulary. At a higher level of complexity it can be a hierarchical classification or taxonomy. At the most complex level, it is an « ontology », i.e. the modelling of a domain of knowledge or practice, which may contain several taxonomies with transverse relationships, including causal relationships and automatic reasoning capabilities.

Semantic metadata are an essential part of artificial intelligence devices:

they are used as skeletons for knowledge graphs – or knowledge bases – implemented by big techs (Google, Facebook, Amazon, Microsoft, Apple…) and more and more in large and medium-sized companies,
they are used – under the name of « labels » – to categorize the training datasets for deep learning models.

Because they structure contemporary knowledge, whose medium is digital, metadata systems represent a considerable stake at the scientific, cultural, political levels…

One of the goals of my company INTLEKT Metadata Inc. is to establish IEML (Information Economy MetaLanguage) as a standard for the expression of semantic metadata systems. What is the contemporary landscape in this area?

THE SEMANTIC METADATA LANDSCAPE TODAY

Standard Formats

The system of standard formats and « languages » proposed by the World Wide Web Consortium – W3C – (XML, RDF, OWL, SPARQL) to achieve the « Semantic Web » has been around since the late 20th century. It has not really caught on, and especially not in companies in general and big tech in particular, which use less cumbersome and less complex formats, such as « property graphs« . Moreover, manual or semi-manual categorization of data is often replaced by statistical approaches for automated indexing (NLP, deep learning…), which bypass the need to design metadata systems. The W3C system of standards deals with the *files formats and programs* handling semantic metadata but *not the semantics itself*, i.e. the categories, concepts, properties, events, relations, etc. that are always expressed in natural languages, with all the ambiguities, multiplicities and incompatibilities this implies.

Standard models

On top of this system of standard formats, there are standard models to deal with the actual semantic content of concepts and their relationships. For example schema.org for web sites, CIDOC-CRM for the cultural domain, etc. There are standard models for many domains, from finance to medicine. The problem is, there are often several competing models for a domain and the models themselves are hypercomplex, to the point that even the specialists of a model master only a small part of it. Again, these models are expressed in natural languages, with the problems that this implies… and most often in English only.

Specific metadata systems

Taxonomies, ontologies and other metadata systems implemented in real applications for organizing data sets are mostly partial uses of standard models and standard formats. Users submit – to varying degrees of success – to these layers of standards in the hope that their data and applications will become the happy subjects of a realm of semantic interoperability. But their hopes are disappointed. The ideal of the decentralized intelligent Web of the late 1990s has given way to search engine optimization (SEO) more or less aligned with Google’s (secret!) knowledge graph. We have to admit, almost a quarter of a century after its launch, that the W3C’s Semantic Web has not kept its promises.

Problems encountered

To achieve semantic interoperability, i.e. fluid communication between knowledge bases, information system managers submit to rigid models and formats. But because of the multitude of formats, models and their disparate applications, not to mention language differences, they do not achieve the expected gain. Moreover, producing a good metadata system is expensive, because it requires a multidisciplinary team including: a project manager, one or more specialists in the domain of use, a specialist in formal modelling of the type of taxonomy or ontology (cognitive engineering) who is able to find his way through the labyrinth of standard models, and finally a computer engineer specializing in semantic metadata formats. Some people combine several of these skills, but they are rare. Finally, a recent survey shows that W3C « linked data » tools (including RDF, OWL and SPARQL) are too complex for web developers and end-users.

HOW CAN IEML SOLVE PROBLEMS IN THE WORLD OF SEMANTIC METADATA?

IEML in a nutshell

IEML – patented by INTLEKT Metadata – is neither a taxonomy, nor a universal ontology, nor a model, nor a format: it is a *language* or *meta-ontology* composed of (1) a few thousand semantic primitives organized in paradigms and (2) a fully regular grammar.

Unique features of the IEML language

IEML is « agnostic » with respect to formats, natural languages and hierarchical relationships between concepts. IEML allows to build and share any concept, concept hierarchy or relationship between concepts. Therefore IEML does not produce a flattening of expressive possibilities. However, IEML does provide semantic interoperability, i.e. the ability to merge, exchange, recombine, connect and translate almost automatically metadata systems and the knowledge bases organized by these metadata. IEML thus reconciles maximum originality, complexity or cognitive simplicity on the one hand and interoperability or communication on the other, contrary to the contemporary situation where interoperability is « paid for » with a restriction of expressive possibilities.

Unique features of the IEML editor

Another advantage: unlike the main contemporary metadata editing tools (Smart Logic Semaphore, Pool Party, Synaptica, Top Braid Composer) the IEML editor designed by INTLEKT will be intuitive (visual interface based on tables and graphs) and collaborative. It is not designed for specialists in RDF and OWL (the standard formats), like the editors mentioned above, but for application domain experts. A method accompanying the tool will help specialists to formalize their domains in IEML. The software will automatically import and export the metadata in the standard formats chosen by the user. Thus, the IEML editor will reduce the complexity and cost of creating semantic metadata systems.

Market for metadata management and edition tools

It is easy to see that, as the amount of data produced continues to grow, along with the urge to extract usable knowledge from it, there is an increasing need to create and maintain good metadata systems. The market for semantic metadata system editing and management tools is now worth $2 billion and could reach (by a very conservative estimate) $16 billion by 2026. This projection aggregates:

data from the semantic industry itself (companies that create metadata systems for their customers),
semantic annotation tools for training datasets for machine learning used in particular by data scientists,
management of their internal metadata systems by the big tech.

INTLEKT GOALS FOR THE NEXT 5-10 YEARS

The Foundation

We want IEML to become an open-source standard for semantic metadata around 2025. The IEML standard should be supported, maintained and developed by a non-profit foundation. This foundation will moderate a community dedicated to the collaborative edition of IEML metadata systems and provide a public knowledge base of IEML-categorized data. The foundation will create a socio-technical ecosystem conducive to the growth of collective intelligence.

The private company

INTLEKT will continue to maintain the collaborative editing tool and to design custom semantic knowledge bases for solvable clients. We will also implement a marketplace – or exchange system – for IEML-indexed private data that will be based on the blockchain. The IEML-indexed knowledge bases will be interoperable on the parallel planes of data analysis, automatic reasoning, and neural models training.

However, before reaching this point, INTLEKT must demonstrate the effectiveness of IEML through several real-world use cases.

INTLEKT’S MARKET IN THE 2-5 YEAR HORIZON

Interviews with numerous potential customers have enabled us to define our market for the coming years. Let’s define the relevant areas by elimination and successive approximations.

Human affairs

IEML is not relevant for modeling purely mathematical, physical or biological objects. The exact sciences already have formal languages and recognized classifications. On the other hand, IEML is relevant for objects from the humanities and social sciences or for interactions between objects from the exact sciences and objects from the humanities, such as technology, health, the environment or urban phenomena.

Non-standard fields

For the time being, we will not exhaust ourselves in translating all existing metadata models into IEML: they are very numerous, sometimes contradictory, and rarely used in full. Many users of these models are content to select a small, useful subpart of them and will not invest their time and money in a new technology without necessity. For example, many SEO (Search Engine Optimization) companies extract a useful subset from schema.org‘s classes (sponsored by Google) and Wikidata‘s entities (because they are trusted by Google) and have no need for additional semantic technologies. Other examples: the gallery, museum, library or archive sectors have to submit to rigid professional standards with limited possibilities of innovation. In short, sectors that are content to use an existing standard model are not part of our short-term market. We will not fight losing battles. In the long term, however, we envision a collaborative platform where the voluntary translation of current standard models into IEML can take place.

Let’s also eliminate the e-commerce market for the moment. This sector does use category systems to identify broad domains (real estate, cars, appliances, toys, books, etc.), but the multitude of goods and services within these broad categories is captured by automatic natural language processing or machine learning systems, rather than by refined metadata systems. We do not believe that IEML will be adopted in the near term in online commerce.

This leaves non-standard domains – which do not have ready-made models – or multi-standard domains – which must build hybrid models or crossroads – and for which statistical approaches are useful… but not sufficient. Think for example of collaborative learning, public health, smart cities, e-democracy, smart contracts documentation, massively multiplayer online games, analysis of complex corpora from several disciplines in digital humanities, etc.

Modelling and visualization of complex systems

Within the non-standard domains, we have identified the following needs that are not met by the semantic technologies in use today:

The modelling of complex human systems, where several heterogeneous « logics » meet, i.e. groups obeying various types of rules. This includes data produced by processes of deliberation, argumentation, negotiation and techno-social interaction.
The modelling of causal systems, including circular and intertwined causalities.
The modelling of dynamic systems during which the objects or actants transform. These dynamics can be of various types: evolution, ontogeny, successive hybridizations, etc.
The interactive 2D or 3D visualization and exploration of semantic structures in huge corpora, preferably in a memorable form, i.e. easy to remember.

In the coming years, INTLEKT intends to model complex dynamic systems involving human participation in a causal manner and to provide access to a memorable sensory-motor exploration of these systems.

IEML being a language, everything that can be defined, described and explained in natural language can be modelled in a formal way in IEML, thus providing a qualitative framework for quantitative measurements and calculations. It will be possible to perform automatic reasoning from rules, prediction and decision support, but the main contribution of IEML will be an increased capacity for analysis, synthesis, mutual understanding and coordination in action of user communities. Semantic interoperability should be the by-product of a cognitive gain, not a costly obligation.

THE NEXT SIX MONTHS

The IEML language already exists. Its development has been funded with one million dollars in an academic setting. We also have a prototype of the editor. We now need to move to a professional version of the editor in order to meet the market needs identified in the previous section. For this we are seeking a « seed » private investment of about 250 K US$, which will be used mainly for the development of a collaborative editing platform with the appropriate interface. Welcome to investors.

Based on the Information Economy MetaLanguage (IEML), semantic computing brings together several perspectives: an improvement of artificial intelligence, a solution to the problem of semantic interoperability, an algebraic model of semantic linguistics: all this at the service of human collective intelligence.

Neuro-Symbolic Artificial Intelligence

Every animal has a nervous system. Neuronal computing, which is statistical in essence, is the common basis of all animal intelligence. Machine learning, and in particular deep learning, is a partial automation and externalization of this neural computing. By contrast, symbolic or logical computing distinguishes human intelligence from other forms of animal intelligence. Language and complex semantic representations are the most obvious manifestations of symbolic computing, which is of course supported by neural networks. After what has been labeled automatic reasoning, expert systems and semantic web, knowledge graphs are today the name of an automation and externalization of natural symbolic computing.

The point that I want to make here is that a progress in human intelligence – which is what we are looking for – does not necessarily come from an augmentation of neuronal computing power. It may be achieved by the invention and use of new symbolic systems. For example, compared to anterior irregular numbering systems like the roman one, the invention of the position numbering system with a zero improved markedly arithmetical calculations. This example suggests that, considering two identical neural networks, one of them may have a much more efficient processing than the other just because of an improved data labelling.

Semantic Interoperability

In this line of thought, it is only on the basis of an adequately coded symbolic AI that we will be able to effectively exploit our new machine learning capabilities. I am standing for a neuro-symbolic perspective on AI, but I think that we need an improvement of the symbolic part. Symbolic AI has been invented before the Internet, when the problem of semantic interoperability did not exist. Because we now have a global memory and because our communication systems are processed by algorithms, natural languages are not anymore the right tool for knowledge metadata. Natural languages are multiple, informal, ambiguous, and changing. To make things worst, cultures, trades and disciplines divide reality in different ways. Finally, the numerous metadata systems used to classify data – often inherited from the age of print – are incompatible. The reader may object that the problem of semantic interoperability is solved by the semantic web standard RDF (Resource Description Framework) and other similar standards. It is true that current standards solve the problem of interoperability at a technical – or software – level. However *semantic* interoperability is not about files standards but about categories and architectures of concepts.

Digital computers exist for less than a century. We still live in the prehistory of automatic computing. Today we enjoy universal coordinate systems for space and time, but no coordinate semantic system. Public health, demography and economy statistics, training and education resources, talent management, job market, the internet of things, smart cities and many other sectors rely on multiple incompatible classification systems and ontologies, inside and among themselves. To take a classical example, disaster management requires an intense emergency communication between different services, departments and organizations. But currently these institutions do not share the same metadata system, even inside the same country, the same state or the same administration.

Language Intelligence

The solution proposed by INTLEKT Metadata to the problem of semantic interoperability is not a universal ontology, not even one standard ontology by domain, which would be a drastic over-simplification and impoverishment of our collective intelligence. We want to promote interoperability and semantic computability while allowing diversity to flourish.

Our solution is rather based on a techno-scientific breakthrough: the invention of a univocal and computable semantic code called IEML (the Information Economy MetaLanguage) that has been specially designed to solve the problem of semantic interoperability, while improving the calculability of semantics. In one word, IEML semantics are optimally computable because they are a function of its syntax. IEML is a programmable language (akin to a computable Esperanto) able to translate any ontology or semantic metadata system and to connect all of their categories. So, if their metadata speak the same metalanguage, a great diversity of classifications and ontologies, reflecting the situations and pragmatic goals of different communities, will be able to connect and exchange concepts.

IEML has a compact dictionary (less than 3500 words) that is organized by subject-oriented paradigms and visualized as keyboards. IEML paradigms work as symmetrical, nested and interconnected « micro-ontologies ». This feature enables the weaving of semantic relations between IEML words by the means of functions. IEML grammar is completely regular and is embedded in the IEML editor. All IEML texts are produced by the same grammatical operations on the same small dictionary. In brief, a computer only needs a dictionary and a grammar to “understand” an IEML text, which is notoriously not the case for texts in natural languages. Indeed, IEML has the expressive power of a natural language and can therefore translate any language, which makes it an ideal pivot-language.

Collective Intelligence

IEML can not only improve inter-human communication, but also make inter-machine and human-machine communication more fluid to ensure a collective mastery of the Internet of things, intelligent cities, robots, autonomous vehicles, etc. Contemporary collective intelligence works in a stigmergic way. It is a massively distributed read-write process on our digital memories. By framing our information architecture, we structure our memory, we train our algorithms, we determine our thoughts and influence our actions. Collective intelligence requires metadata intelligence. Everyone should be able to structure her digital information in her own way and, at the same time, be able to exchange it with the utmost precision through the channels of a universal semantic postal service. IEML is the semantic metadata system adapted to the new situation, able to harness our global computing environment for the benefit of human collective intelligence.

A semantic knowledge base organized by an IEML metadata system would play simultaneously three roles:

an encyclopaedia of a practical domain: a knowledge graph, including causal models à la Judea Pearl;
a training set (or several training sets) for machine learning;
a decision support system in real time, able to save the users attention span, while telling them what they need to know.

Today, the encyclopedia part is the work of the knowledge graph people and their RDF triples. The training set and its labels is the job of the machine learning specialists. The data scientists, for their part, build business intelligence tools. These activities will converge and a new kind of semantic engineers will emerge. Furthermore, communities, institutions and companies of all sorts will be able to share their semantic knowledge in a virtual public database, or to exchange their private data on a new semantic market.

Outline of a Business Model for a Change in Civilization