I have been developing, testing, and researching an innovative semantic
knowledge management system called ArtificialMemory
for more than 5 years.
ArtificialMemory is a personal and enterprise knowledge management system integrating wikis, personal information, blogs, and document management systems.
Here are (the
first) 5 of the important lessons learned. These lessons often contradict common
opinions on Semantic Web technology and usage stemming from the
dominant artificial intelligence and knowledge representation
1. Simple ontologies are as important as complex ones
The foundation of any Semantic Web application is at least one
ontology scheme. An ontology scheme can be defined more or less
would be a simple form of ontology, an OWL scheme would be a
kind allowing for some logical inference of new knowledge. A
/ rigid ontology definition forces or tempts one to more often
work on the
ontology scheme instead of inserting data thus preventing one
things done fast. In my experience, combining simple ontologies
thesauri with more complex ones such as RDFS or OWL is the way
to go. It
important to be able to just enter a keyword / tag to denote or
another word or object instance as well as to be able to create and
instantiate a well-defined object class.
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2. Full-text search is far less, entity search is far more
important in Semantic Webs than in Document Webs
When one starts storing one's knowledge in highly interrelated
information chunks like semantic webs, full text search becomes
obsolete. Knowledge refers to object instances.
information is related to object instances and thus can be
Searching most times merely consists of jumping to an instance
and identifying the relevant statements / information. What is
Semantic Webs is a sophisticated auto-completion (that is fast)
for instance names (relevant entities).
3. Adding information to Semantic Webs has to be as easy
Examining the current Semantic Web applications, one will find
tend to either make it very simple to add information by more or
less automatic data imports, or very difficult by forcing users
manually using a
multi-step process or complex meta-language (RDF, OWL). The
will soon create an information pool not reflecting the user's
thus adding irrelevant information that endangers use and usability of the
ontology data store (it's like getting a huge dictionary instead of the
vocabulary you already know and want to relate to).
approach slows down the process of adding information to such a
degree that less
statements and instances will be created and more information is
traditional documents invalidating the Semantic
Web. The information added to a (personal) knowledge store has
inserted consciously using a limited set of natural language
expressions creating structured information fast and easy.
4. Information in Semantic Webs has to be normalized
Adding information to a Semantic Web has to take place in a
controlled manner. Duplication could happen in at least three
statement / triple duplication, and text duplication. Entity
many times be avoided by instance name checks. Nonetheless, entity
duplication will occur. Therefore, being able to easily merge
great use. Triple duplication has to be avoided by consistency
are not part and parcel of standard Semantic Web ontology data
inference engine or SPARQL statements would have to be used].
duplication is one of the toughest parts: It does imply that the
to reuse natural language text chunks as can be found in value
properties as part of Semantic Webs.
In order to enable reusing
NL-text chunks, it
be possible to integrate them in an indefinite number of text
sequences simulating traditional documents. Thus documents become
normalized text entities. For example, if each section of a book
or each slide of a presentation becomes a singular entity, all sections /
be reused in other presentations or books. Changing a text
will change it in any book / presentation using it. Traditional
documents are just sequences of NL-text entities that can be
enriched entities in Semantic Webs.
5. Any Semantic Web has to be a personal, integral
That's something not yet widely understood. Any knowledge is
personal. Adding semantics to data is giving meaning to data. The
Semantic Web is
the same as the data web. Searching in other people's Semantic
result in data meaning nothing to you. What is meaningful to you
related to your (personal) knowledge (Semantic Web). Meaning is
knowledge, is personal. An ontology is personal, even if it
happens to be
many people. In this case, it's just several times personal. The
Semantic Web thus is personal in character, made of individual
is in the nature of Semantic Webs that they represent personal!
knowledge, and the knowledge of one person is integral by nature.
That is, in
principal, each and every one of us has only one category
representation (however different it may be) for, say, 'dog', and
whenever we say 'dog'
actually mean our concept of 'dog'.
What Semantic Webs can help
is to integrate personal utterances into a single personal
reflecting and extending an individual's memory / knowledge, and
to communicate, to relate to and learn from other people's
automatically match and select people by their knowledge, to
pass on and
distribute changes of knowledge, and so forth. A Web of interrelated
Semantic Web is a Web of Data. I guess we will proceed from a
depersonalized documents to personal Semantic Webs, and from
there to a
Web of personal data
that can be acted upon intelligently.
Lars Ludwig is a cognitive psychologist with a professional and scientific IT
background living in Germany.