To face the challenges of knowledge-intensive environments, we investigate a novel self-organising knowledge-oriented (SOKO) model, called Molecules of Knowledge (MoK for short). In MoK, knowledge atoms are generated by knowledge sources in shared spaces - compartments -, self-aggregate to shape knowledge molecules, and autonomously move toward knowledge consumers.
within their own compartment, especially to increase the probability of providing him/her with relevant knowledge items.
Atoms Atoms are the most primitive living pieces of knowledge within the model. A MoK atom is produced by a knowledge source, and conveys a piece of information spawning from the source itself. Hence, along with the content they store, atoms should also store some contextual information to refer to the content’s origin, and to preserve its original meaning.
As a result, a MoK atom is a triple of the form atom( src, val , attr )c where: src identifies unambiguously the source of knowledge; val is the actual piece of knowledge carried by the atom – any kind of content –; attr is essentially the content’s attribute, that is, the additional information that helps understanding it – possibly expressed according to some well-defined ontology or controlled vocabulary –; c is the current concentration of the atom, which is essentially the number of atoms of the kind in the compartment.
Molecules Molecules are spontaneous, stochastic, “environment-driven” aggregations of atoms, which in principle are meant to reify some semantic relationship between atoms, thus possibly adding new knowledge to the system—for instance self-aggregated news chunks could shape the conceptual “path” toward a novel interesting news story. Each molecule is simply interpreted as a set of atoms, that is, an unordered collection of somehow semantically-related atoms.
A MoK molecule has then a structure of the form molecule( Atoms )c where c is the current concentration of the molecule, and Atoms is the collection of all the atoms currently bonded together in the molecule.
Enzymes One of the key features of MoK is that it interprets prosumer’s knowledgerelated actions as positive feedbacks that increase the concentration of related atoms and molecules within the prosumer’s compartment, producing the positive feedback required to enable self-organisation of knowledge.
A MoK enzyme has then a structure of the form enzyme( Atoms )c where every enzyme – with its own concentration c – explicitly refers to a collection of Atoms that the catalyst’s actions have in any way pointed out as of interest for the catalyst him/herself.
Biochemical Reactions The behaviour of a MoK system is actually determined
by the last abstraction of the model: the biochemical reaction [
As a knowledge-oriented model, the main issue of MoK is determining the
semantic correlation between MoK atoms. So, to define a working MoK
system, the basic mok ( atom1 , atom2 ) function should be defined, which takes
two atoms atom1 , atom2 , and returns a “matching degree”—which could be a
boolean or a double value ∈ [
The aggregation reaction (AggR) bonds together atoms and molecules: molecule(Atoms1) + molecule(Atoms2) 7−→r agg molecule(Atoms1 S Atoms2) + Residual(Atoms1, Atoms2) where r agg is the AggR reaction rate, mok (atom1, atom2) holds for some atom1 ∈ Atoms1, atom2 ∈ Atoms2, and Residual (Atoms 1, Atoms2) is the multiset of atoms obtained as the multiset difference (Atoms 1 U Atoms2)\(Atoms1 S Atoms2)— that is, essentially, all the atoms of Atoms 1 and Atoms 2 that do not belong to the resulting molecule. In short, more complex molecules are formed by aggregation whenever some atoms in the reacting molecules (or in reacting atoms, handled here as one atom molecules) are semantically correlated (via the mok function).
Positive feedback is obtained by the reinforcement reaction (ReinfR), which consumes a single unit of enzyme to produce a unit of the relative atom/molecule: enzyme(Atoms1) + molecule(Atoms2)c 7−→r reinf molecule(Atoms2)c+1 where r reinf is the ReinfR reaction rate, enzyme(Atoms1) and molecule(Atoms2)c exist in the compartment, both with c 6= 0, and mok(atom1, atom2) holds for some atom1 ∈ Atoms1, atom2 ∈ Atoms2
Following the biochemical metaphor, molecules should fade as time passes, lowering their own concentration according to some well-defined decay-law. The temporal decay reaction (DecayR) is hence defined as follows:
molecule(Atoms)c 7−→r decay molecule(Atoms)c−1 Similarly, a biochemical-inspired knowledge management model should provide some spatial interaction pattern. MoK adopts diffusion as its data-migration mechanism, in which atoms and molecules can move only between neighbour compartments, resembling membrane crossing among cells. MoK diffusion reaction (DiffR) is then modelled as follows, assuming that σ identifies a biochemical compartment, and kkσ brackets molecules in a compartment σ: k Molecules1 S molecule1 kσi + k Molecules2 kσii 7−→r diffusion
k Molecules1 kσi + k Molecules2 S molecule1 kσii where σi and σii are neighbour compartments, r diffusion is the diffusion rate, and molecule1 moves from σi to σii as the result of the reaction. 2
While MoK is a general-purpose model for knowledge self-organisation, it can be tailored on specialised application scenarios, by refining the notions of atom and suitably defining the mok semantic correlation function. Since news management provide a prominent example of a knowledge-intensive environment, we chose news management as the first case study for MoK, introducing the MoK-News model for self-organisation of news. 2.1
IPTC1 develops and maintains technical standards for improved news management, such as NewsML2 and NITF3. 1 http://www.iptc.org/ 2 http://www.iptc.org/site/News_Exchange_Formats/NewsML-G2/ 3 http://www.iptc.org/site/News_Exchange_Formats/NITF/
The NewsML tagging language is a media-type orthogonal news sharing format standard aimed at conveying not only the core news content, but also the data that describe the content in an abstract way, namely the metadata. In order to ease syntactical and semantical interoperability, NewsML adopts XML as the first implementation language for its standards and for maintaining sets of Controlled Vocabularies (CVs), collectively branded as NewsCodes, to represent concepts describing and categorising news objects in a consistent manner—pretty much as domain-specific ontologies do.
The News Industry Text Format, too, adopts XML to enrich the content of news articles, supporting the identification and description of a number of news typical features, among which the most notable are: Who owns the copyright to the item, who may republish it, and who it’s about; What subjects, organisations, and events it covers; When it happened, was reported, issued, and revised; Where it was written, where the action took place, and where it may be released; Why it is newsworthy, based on the editor’s analysis of the metadata. NewsML in fact provides no support for any form of inline tagging to add information to the plain text, for instance with the purpose to ease the work of a text mining algorithm usable to automatically process the document. Thus, NITF and NewsML are complementary standards, hence they perfectly combine to shape quite a comprehensive and coherent framework to manage the whole news lifecycle: comprehensive, given that one cares about news overall structure, including metadata, whereas the other focusses on their internal meaning making it unambiguous; coherent, because they both exploit the same IPTC abstractions—in fact NITF, too, uses NewsCodes. 2.2
Since sources provide journalist with the required raw information already formatted according to the afore-mentioned IPTC standards, a simple-yet-effective mapping can be drawn. In fact, MoK atoms do actually have a clear counterpart in NewsML and NITF standards: tag. Tags – along with their “content” – can in fact be seen as the atoms that altogether compose the “news-substance” in the news management scenario. As a result, our MoK-coordinated news management system would contain < newsItem > atoms, < person > atoms, < subject > atoms, etc.—that is, virtually one kind of atom for each NewsML/NITF tag.
A MoK-News atom looks like atom( src, val , sem(tag, catalog) )c, where src ::= news source uri val ::= news content attr ::= sem(tag , catalog ) tag ::= NewsML tag | NITF tag catalog ::= NewsCode uri | ontology uri Here, the content of an atom is mostly given by the pair < val , tag >, where tag could be either a metadata tag drawn from NewsML or an inline description tag taken from NITF. The precise and unambiguous semantics of the new content (val) can be specified thanks to the catalog information, which could be grounded in either NewsML or NITF standards in the form of NewsCodes, or instead be referred to a custom ontology defined by the news worker. MoK-News molecules, enzymes, and biochemical reactions are then both syntactically and semantically affected by such domain-specific mapping of the MoK model. 3
To the best of our knowledge, althought the (bio-)chemical metaphor is widely used to achieve self-organisation and self-adaptation by emergence, no MoKlike approaches were studied that could bring self-* behaviours directly into data. On the other hand, in the news community much attention is paid to interoperability and semantic standardisation—but no paradigm shift has been tried to “see” data as active entities.
Nevertheless, some MoK-similar system actually exists although with
different aims. In [
Our future efforts will be on first devoted to provide an effective
implementation of the MoK model upon an existing coordination middleware enriched with
the “online biochemical simulator” behavior [