Hello. Hello, everyone. My name is Ana Ibáñez and I am going to give you
a brief introduction to the main concepts and the main contents of Unit 5
of the Chapter 5 of the coursebook on the Architecture of Words,
Applications of Meaning Studies, which is the book we use in the degree, in
the course Aplicaciones Semánticas de la Lengua Inglés, Adicionarios y
Ontologías from the Degree of English Studies of UNED. So today we are
going through the contents of Unit 5, which is entitled Meaning, Knowledge
and Ontologies. OK, so what are the objectives of this session? First, we
are going to see some differences between lexical products. We went through
that already in previous units. Now we are going to compare specifically
different lexical products to ontologies. So César. How are the
terminologies ontologies? Here there is a link. Of course, you can download
the PowerPoint. And then access the links, which now I will not go through.
There is no time and it's not possible with this Blackboard to go through
the links. But this is a link where it talks about the importance of
ontologies, for instance, for multilingual contexts or for multilingualism.
Although ontologies can be used in any area of life where we have to handle
knowledge. We are going to see also various definitions of ontologies. So
we are going to see how ontologies are approached from different
perspectives. And we are also going to go through the different types of
ontologies available nowadays, although they grow exponentially. So
probably next year there will be other ontologies that have appeared. So
for the introduction, as an introduction to this unit, we can just say
that, well, let's start with the introduction to the unit. So the language
means different things in different areas, for instance, in computer
programming or in semantics or in linguistics. It has a different meaning
than in everyday in an everyday context. The same happens with ontology.
Ontology can be approached from a pro from the world of artificial
intelligence for computer programming, or also it has a very big
philosophical background already Aristotle and the philosopher, the Greek
philosophers. In the ancient Greece, we're talking about ontology,
ontologies. We are more focused on computer programming nowadays on
artificial ontologies due to the. The fact that we are living in the
digital era and in computer programming, a language is considered a kind of
markup language. We have a lot of types of languages which have HTML, DML.
We saw a bit of this in past uni. So I recommend you go through it again
before continuing. And we also have in computer programming the concept of
ontology, which is kind of the main concepts that are handled in an area of
knowledge or in a database. So an ontology has to do. That's why it is. The
word is highlighted. It has to do with concepts and. It is just as a
reminder, a very simple idea that we are going to use to enter, to focus on
this unit is the fact that concepts are transmitted by words. As humans. So
we have to keep the what we have been going through in the previous units
different. We have to keep in mind the important difference that exists
between concepts and words, because this main difference is what will help
us distinguish also ontologies from terminologies or from lexical, lexical
or terminological approaches or ontological conceptual approaches. OK. And
we are going to work on it. So now through very different approaches and
definitions, some of them are complementary, others not so much. But in
general, they tend to be complementary. One definition is knowledge
networks. Ontologies are knowledge networks, so they are a collection of
concepts that structure information and allow a user to view it. In this
way, what is a structure? Structured information consists of organizing
hierarchies where we have super-concepts and sub-concepts. So we make use
of the idea of hyperonymy and hyponymy. A very interesting definition is
quoted here, completely quoted from Heflin, who is the editor of OWL. OWL
is a web ontology language, is one of the languages we use for the creation
of ontologies, the main one. And he says an ontology defines the terms used
to describe and represent an area of knowledge. Ontologists are used by
people, databases, applications that need to share domain information.
Ontologies include computer usable definitions of basic concepts in the
domain and the relationships among them. They encode knowledge in the
domain and also knowledge that spans domains. So in this way, they make
that knowledge reusable. So basically, what is an ontology? An ontology is
a way to handle knowledge, to structure knowledge into different domains,
to encode it, to organize it. To establish relationships in order for
knowledge to be retrievable, to be found, to be handled. We are going to go
a bit more through that. Another approach to ontologies is to consider them
databases or knowledge bases with different degrees of structure. Structure
can be simple taxonomies, metadata schemes such as the Dublin Core or Yahoo
hierarchy in the case of taxonomies, or they include also logical theories.
So, a taxonomy and an ontology would include structures in terms of
taxonomies, schemes, or logical relations. Another definition is a
logic-based language. A logic-based language means that it uses
meta-language, which we will go through later on. We will see some
examples. In this type of language, it is important to distinguish among
classes, properties, and relations. Heflin in 2004 also mentioned that
ontologies figure prominently in the semantic web, so for the semantic web,
which is the Web 3.0, the current web nowadays, ontologies are a very
important part of it. Because they represent knowledge, they help us access
the semantic of all documents. They help us structure knowledge, structure
meanings. They make use of metadata, which labels this type of meaning. And
so let's say that nowadays the semantic web needs ontologies to organize
all the amount of information that is circulating constantly. Another
definition is that the study, an ontology, the subject of an ontology is
categories of things that exist or may exist in some domain. So we are
going back again to the idea of category. The product of such a study
called an ontology is a catalog of the types of things that are assumed to
exist in a domain of interest D from the perspective of a person who uses a
language L for the purpose of talking about X. I like this definition very
much because it focuses on one type of ontology, which are domain-specific
ontologies. So you can create an ontology of anything you want. Like when
we were talking in Unit 4 about conceptual maps, an ontology is a kind of
conceptual map, but much more developed in the sense that it's a step
further because we don't have to see it visually as only the
representation, but it's also everything that goes after the
representation. So ontologies and concept maps are very much connected. We
can take a domain, for instance, imagine I like bikes, types of bikes, the
word of this would be our conceptual domain. I'm going to use a specific
language to categorize all the knowledge that exists in that domain
according to classes, relations, actions, instances, functions, etc. And
I'm going to use this new language to talk about this specific topic in
this domain, for instance, writing or types of engine, well, not engines,
of wheels, for instance. Guarino, for instance, talks about logical axioms,
so we have again the idea of logic, and we have designed to account for the
intended meaning of a vocabulary, so it's a way of giving a logical
language to meaning. And De Grotte, in 2013, already talked in a course in
her space. It's a specialized course in terminology and terminography of
ontologies. She was the lecturer of a course in 2013 about that in Ghent
University. She defined ontologies as an agreed conceptualization, agreed
is a conventionalized, it's a very important idea of a reality or of part
of reality, depending on whether an ontology is a domain-specific or
general ontology. And it is based on classes. Classes, relations,
functions, actions, instances. And ontology goes further than a thesaurus
because it can comprise, usually comprises a thesaurus, which in turn
comprises a taxonomy, so a hierarchy classification of a part of reality
using one relation type. Thank you. In ontologies, we have three or four
relation types. We have functions, different functions. These logical
actions include different types of relations in a taxonomy. We only have
one relation type, which is a class of a taxonomy has this type of relation
and ontology can have more types of relations. And here you have from
Stanford University. Why do I include this link? Because I would like you
to visit it. Stanford University is the one that has designed Protege.
Protege is a program to design ontologies. So this is the most used program
to create an ontology nowadays. And indeed, in their Web page they have
with a very simple HTML language, very simple Web pages. You have what you
have a web page. What I have copy, paste it here from this link. But much
more information about how to create an ontology, why we need an ontology,
the structure of an ontology. So I really recommend this link. Basically
now, I just wanted to mention that their definition, because we are now
focusing on this, of an ontology. They say that an ontology defines a
common voice. It defines a vocabulary for researchers who need to share
information in a domain. So it is, as they mentioned, a conventionalized or
an agreed language and structure. It is important that they mention that it
includes machine interpretable definitions. So that's why we need this
logic language or this metadata to make it understandable for machines. And
it includes these concepts, basic concepts and the relations. The reasons
to develop an ontology, to share common understanding, to reuse knowledge,
to make domain assumptions explicit, to, let's say, to agree on what we
consider from a part of the domain or not, to separate domain knowledge
from operational knowledge, to analyze domain knowledge. All these ideas
sound very abstract, but let's put a practical application to it. Let's
say, for instance, we are using it for translation. One of the areas where
ontology is used, apart from the main area, which is the semantic web,
translation. Machine translation is another area that really needs
ontologies. If we define a domain or the domain of a specific area of
knowledge, we are going to be able to create correspondences between
languages much more easily. And we are going to make it interpretable for a
machine. It can also be applied, for instance, to voice recognition
programs. This city, this Alexandra, or what? Is the name Alexandra? I
don't remember. Sorry. But, well, these robots or bots that reply to us,
they do it thanks to this type of ontologies that have been designed to
interpret, to obtain the data. Thank you. From the data we are providing,
they have given them specific definitions that can be interpreted by the
machines so they can recognize the information we are providing. And thanks
to this, they can also give a certain output. So that's, for instance,
another important application of ontologies. All in all, all definitions
include, if you have paid attention to it, classics. So the general things,
the relationships and the properties or attributes that these things have.
So the things that exist, the relationships that exist among these things
and the properties these things have. This is a link that I cannot click on
now, but as I said, I suggest you click on and investigate a bit. It's for
how to create your own ontology. And. It's the guide for using Protegi from
Stanford University. Now we are going to continue with point four, where we
are going to see the differences between databases, terminologies and
ontologies. We are going to go through a main through a very important
example, which is WordNet. Apart from Protegi, to create ontologies, a very
interesting lexical database of English lexical database is the set of It's
another different thing from an ontology, but it's interesting to see the
differences. OK, this is a database, but it is a lexical database. What
happens with the lexical database as WordNet? WordNet was created in
Princeton already a decade ago or almost two decades ago, but it keeps on
being developed and it is used for many scholars all around the world
because they have it freely available on the Internet. They have old
versions and the new version. The new version was released, I think,
already only a few months ago. What is based on? Well, WordNet is based on
lexical items, nouns, verbs, adjectives and adverbs and then function words
and they are grouped into sets of cognitive synonyms. So they play the
basic idea of these lexical databases, the idea of synsets. They express
different concepts. But first of all, they take lexical items and they
organize them into groups of synonyms according to the concept they
express. So these synsets, it's a specific term they use to refer to these,
are interlinked by means of conceptual, semantic and lexical relationships.
So why is this? Why is this lexical database different from WordNet? Which
is WordNet, sorry, different from Thesaurus? It is different because it
groups, Thesaurus groups based on the meaning. Both of them were grouped,
sorry, both of them grouped words together based on their meaning, but
there are important distinctions. Because in Ethesaurus, the word forms are
grouped according to their meaning. So, words that are found in close
proximity to one another are disambiguated. So, sorry, in WordNet, they are
not just word forms. They play with the idea of disambiguation because, as
I said, words that are found in close proximity to one another are
semantically disambiguated. So, the semantic relations among words are the
most important. In Ethesaurus, words are grouped together just following
one pattern, which is mutual. Meaning similarity. But this is the only
pattern, the relation that is used. Meaning similarity. In WordNet, it is
not only word forms that have similarities, but also there is a whole
semantic network created among them. I'm going to go a bit deeper into
this. As I said, the main relationship. The main relationship in WordNet is
synonymy. Words that denote the same concept are interchangeable in many
contexts, so they are grouped together into unordered sets, which are
syn-sets. And these words establish a number of conceptual relations, and
each scene set, which groups a number of words, contains a brief
definition, which is called gloss, and in most cases, one or more short
sentences that illustrate the use of scene sets for members. So, this scene
set is a group of concepts that are related, both all synonyms, there is a
common definition for them, and also a way of using them. Words can form
several distinct meanings, and they can become two different scene sets.
So, the form, together with the concept, is consistent. It constitutes a
pair, which is unique in the word line, let's say. In Ethesaurus, words are
together, but they don't go to the last step, which is disambiguation.
Because you can group, for instance, a conceptual, let's go to a specific
example in Ethesaurus. We can have, for instance, I don't know, hugging.
And then we have different words that denote the idea of hugging. This same
idea would also apply for WordNet, but apart from this, all the words. that
can be this and be created because they also have a different meaning will
be organized also in another different season where they have a specific
meaning for this insert okay this would be a bit the main difference here
is just the web then the screenshot of WordNet that you can see they have
publications there is here an area where you can download the versions it
is much easier with Windows with Mac they have a new version that today I
couldn't download so I have used always the older version for the new
version I think they they need to implement some other but probably with
within a few days soon. it will be also available and they give a lot of
information about their own project why it is useful etc so this would be a
lexical database then we go to terminologies terminologies are different
from lexical databases or from databases because they don't first of all
remember we were talking in previous units about the semaceological
approach and then onomaceological approach depending on whether we approach
the lexicon through first focusing on the term on the form or through first
focusing on the concept you In WordNet, this lexical database, we first
focus on the concept. And through this concept, where we create these
insets, we include the different terms. In a terminology, it is different
because we first look for the terms, we organize them, and then we see what
they mean. And we locate them in different specialized conceptual domains.
The difference between a dictionary and a terminology database, a
terminological database, as we saw before already, this is just reviewing
this, is that dictionaries have to do with general language, let's say. And
we have also, as we see here, synonyms that are usually scattered
throughout the dictionary. And polysemous words and homonyms are grouped
together. In dictionaries, in terminologies, they are restricted to
specific domains. And the terms that are homonyms are not only synonyms.
Synonyms will also be put together according to the domain they belong to.
But the most important difference is that terminologies have to do with
specific areas of language. So they are more for language for specific. For
instance, the terminology database for mechanics or a terminology of
translation, translation terminology. or medicine terminology. Lexicology
had to do with the study of words in general then, and the result of
lexicological studies will give us lexical databases or dictionaries,
whereas terminology has to do with the study of specialized language, and
the result will give us terminological databases. We saw also a few
databases, very famous databases and very widely used. The European
Commission has developed their own terminological databases. This is very
important for translation studies, as you know, especially in the European
Union when we have so many official languages for communication purposes,
for political relationships, for policy making, and so on and so on.
Terminologies then are more sense-based, that's why it is highlighted. The
terms they contain map an area of specialized knowledge, and as I said the
relationships between the concepts which the term represents are the main
organizing principle of the terminology. In dictionaries words are
organized first of all alphabetically. Of course relationships are
important, but the most important is the content. We saw this before, this
is just a review as I said. In dictionaries, each item, and in lexicology,
each item is observed individually. And for each item, we work with the
definition, then the spelling, the etymology, etc., etc. Whereas in
terminologies or in lexical database, we are studying units in groups. We
group together concepts according to their conceptual domain, their
meaning, their uses, etc. So it's not individual work, it's more group
work. We were studying... I was saying before, what are the applications of
ontologies? Well, Nirenburg and Raskin mentioned a few. There could be many
more. But as I said, machine translation is one of the biggest uses of
ontologies. Information extraction for the semantic web, for instance.
Question and answering, what I was saying about Siri or Alexandra, I don't
know if she's called Alexandra. Text summarization. Answering. General
human-computer dialogue systems are the combination of all of them. These
are... So this is all, in fact, the same types of applications that
computational dictionaries offer. So first of all, what is the difference
then between dictionaries and ontologies? We set the approach towards the
analysis of language and concepts. The approach is conceptual, not from the
word form, but from the concept. This is already different. Also, it is
different the type of language they use. They are more into the logical
languages, into the metadata, etc. Applications that are based on
ontological approach to meaning can all also have to undergo not only what
dictionaries do, computational dictionaries, but other stages, such as
tokenization, parsing. And if all these stages succeed, then we generate
the meaning of a text or what is called text meaning representation. So.
Here we have the input towards specialized processing. As I said,
ontologies go a step further or they are more abstract, obviously, because
they continue in their stages of development. And the fact that they use
logic language, these different organization of language accordingly. So,
we are going to the relations and the properties, etc. Hello, Maria. Thank
you. Some examples in machine translation, text meaning representation
needs to be translated into a natural language different from the one in
which the input was supplied. So the program that does this task is usually
called text generation. This is in machine translation. And in information
extraction, text meaning representations are used also by special rules as
sources of fillers of the user's query. The question answering processor
must first understand exactly what the user wants the system to do, then
find the necessary information, and then be able to generate a well-formed
answer. So we need this. Let's say that we need like a transitional
language that goes from the logic language we have created with all these
metadata relations properties to natural language, to our natural language.
All this is done through different systems, which is this tagging, this
parsing, this meta-language, this logical language that has different
codes. I'm going to give an example at the end. Okay. Shaly and Safferet
also, talking about ontologies and language, they say that ontologies are
useful to compare different languages. And why? Because for an ontology, in
order for an ontology to be reliable, let's say, and also consistent and
sustainable in time, we need it to be cross-cultural. And we need to rely
on concepts that are the same across human minds, cultures and languages.
We know that culture is very idiosyncratic. We are idiosyncratic. We have
different cultures and we need to go. They propose this and I agree with
this, but this doesn't mean that in reality there are domain-specific
ontologies, that there are not, because there are, or ontologies that are
very language-specific, not language-specific, but culture-specific. But
this proposal, I think it is interesting. To make ontologies usable and
reusable for cross-linguistically, and make them more language-independent.
How do we do this? Finding universal concepts in our languages, concepts
that are cross-cultural, let's say. For instance, the idea of mother, the
idea of father. I don't know if there are for this. We would need
anthropologists. We would need a social linguist. Psycholinguists, et
cetera, ethnologists that help us identify these universal concepts so that
we can create a language-independent ontology and a culture-independent
ontology. Talking about the concepts and ontological relationships. The
position a concept holds within a given ontology is determined by
ontological relations. This is just a review of what we just said. In an
ontology, we need concepts and we also need relations. And these relations
are organised in terms of taxonomies and metonymic relations. So, how we
organise concepts? What are these types of relations? A type of, class of,
pertaining to a certain class of, or part of another type of relation. And
sub-concepts or general concepts, more specific concepts. Like hyponymic
and hyperonymic relations. Apart from this, we also need, as we said
before, attributes to these main entities that for part constitute an
ontology. And an interesting authors that are mentioned in the book is
Dismetzinger and Gallese, who went as far as comparing an ontology to our
books. Their idea is very ambitious, because for them an ontology should
represent how we view the world. This is more... Let's say a theory, and in
practice, of course, this is a very ambitious achievement. It's very
ambitious, let's say. But I think it's a very interesting approach to
consider ontologies a way of interpreting the world and to even they found
empirical evidence that the brain models movements and action goals in
terms of these multimodal representations of organism, objects, and
relations. So their idea was to analyze how our brain works and then to
transfer this to ontologies. And if this can be achieved, it has been
achieved in some instances, this would be the best proposal. And the best
idea to create ontologies that are, as we said, cross-linguistic or
cross-cultural and sustainable in time. What is the most important
challenge for ontologies? Ambiguity. Ambiguity is what distinguishes
natural languages and ontologies. For instance, the mapping of one lexical
entry into two separate concepts. We have wall. We can have an inside wall
in English or an outside wall. This is a polysemous word in English, but in
Spanish, we already have two different terms for the same term that exists
in English, which is pared and tapia. So. What happens is that in an
ontology, these polysemous terms, such as world, will be part of a
different section, as I was saying, in the ontology or in the building of
the concepts. Because, as we said, ontologies are organized around domains
and concepts, not around terms, as could happen in a dictionary or in a
thesaurus. Finally, we could say that language is used by people's
ontologies and both as constructed for computers or as a philosophical
discipline, and they are made ad hoc. Let's say that, in conclusion, what
the book means by this, what Margarita Godet, the author of this chapter,
meant by this is that both ontologies, either from a computer or from a
computer, are made ad hoc. Or from a philosophical perspective, are made by
people, for the people, and according to the concepts that we have. But
this proposal of creating ontologies that are cross-linguistic and
cross-conceptual is, for me, a very interesting one, although this is a
subjective view. Because, in fact, if ontologies are based on concepts, and
the language is a conceptual phenomenon, and culture is a conceptual
phenomenon, it can be very challenging. Because this could be a very, let's
say, how could we say, it could reduce reality too much if we only focus on
universal concepts. But... In the end, what we have now a days in reality
is that there is a big emergence of ontologies of all kinds. Sometimes it
is very difficult even to catch up and to follow all these new ontologies
that are created. And I wonder whether sometimes as a linguist and as an
academic of this, I wonder whether it would be more economical to try to
focus first on all the things that we have in common conceptually to create
a very simple ontology that can be just usable and used by all speakers of
all languages. Maybe this is very ambitious also because here we are going
to talk about metalanguage. Even if we try to separate ontologies, from
language because we are they are supposed to be language independent. We
need the metalanguage. What happens with the metalanguage, which is it is a
bit as we say in Spanish, el pez que se muerde la cola. And as you have
seen in this degree, there are many types of metalanguages, but a
metalanguage makes use of language. In this case, for instance, we have the
example of the corel conceptual representation language, which is an
ontology created by language. By our chancellor, Ricardo Mairal and
Periñan Pascual and others in a big project they started already more than
a decade ago. Where they wanted to create the language of all concepts. But
of course, what do they use in their metalanguage? English words. So it is
language independent, but they use a lingua franca, which is English. The
ambitious idea of using a language that is not a language is really... I
don't know if it is possible, let's say. It is very interesting this
language. You can investigate a bit more about this. They are based on
thematic frames as FrameNet, which is a very interesting also lexical
database. For instance, answer. The concept of answering is represented
like this and it has a thematic frame. In this case, it could be X1 a
human, which is the theme, X2 answers to a reference, which is another
human. This would be X3. This would be the topic and this would be the
reference and the goal, the meaning postulate, etc. Well, this is the whole
thematic frame of answer. The answer would be the event and belongs to a
higher frame of the metaconcept communication, to which the metaconceptual
unit is assigned a prototypical thematic frame. So this is very ambitious,
but it worked in a way. So the thematic frame of communicative situations
has a metaconcept, which is communication. And this would be just this
metaconcept theme. This thematic frame derives from the general theme as
well as the meaning postulate. And this meaning posterior could be someone,
x1 says something, x2 to somebody, x3 related to equation x4 that x3 said
to x1. So imagine how complicated it can be. But if we manage to implement
this to a computer program and make it understandable for a computer, then
we succeed. But as you see, this metalanguage is not completely language
independent because it is making use of English. Is this possible to avoid?
It is hard, let's say. We can see also that they use logic symbols such as
plus, the hashtag, the x, the numbers, like as in logics or in generative
grammar or in functional grammar. So logic. Logic, of course, is very, very
necessary for this type of language. And I'm here just going to give you an
example of an ontology. This is just the notes we were taking in a meeting
because I have a PhD student who is developing an ontology for educational
resources in order to later on being able to be able to implement it to
search engines that would give advice and recommendations according to the
person's profile on different degrees. Or courses around the world. So we
were having a meeting with my PhD student and Omar who is working for. A.
For a project from the Internet, I will explain later, from the computer
science department with Salvador Ross, who has a very interesting project.
They are designing an ontology for poetry and he's the developer of this
ontology. So we had a meeting with Omar and my PhD student and he was
teaching us how to design an ontology. And here you can see a bit of a
sample because he gave us a really interesting masterclass on how we start
developing an ontology. First, we have to have an object. It is in Spanish.
The PhD is in Spanish. Objetivo de alto nivel. ¿Cuál es el objetivo?
Facilitar configuración de experiencias formativas. Here we include our
own objective, which is what my PhD student was doing, our PhD.
Concentración. Paso de uso. For instance, objetivos de alto nivel
relacionados. Uno, personalización del proceso de aprendizaje. Dos,
estudio y evaluación de los recursos de aprendizaje. ¿Qué necesito?
¿Qué preguntas de competencia formulo? Respecto de las personas. So this
is basically her main objectives in the thesis. In the thesis. With just
HTML. In the HTML language, what he does first is to establish relations.
You see? This is the first step. With O1, ZU1, ZU2, RT1, PC1. ¿Qué
necesitas? So, that's how it starts an ontology, creating concepts, domains
and relations and attributes. And this is I wanted to show you just as an
example. And finally, we're going to go through types of ontologies. We
have upper ontologies, here you have some examples which appear also in the
book and domain-specific ontologies with very specific concepts, such as
the one I just showed you that is being developed by our PhD. The
principles in all of them are language independence, but as I said, this is
quite of a challenge. They have to be well formed according to axioms and a
good axiomatic specification. They have to have a similarity principle and
opposition principle. For instance, a similarity principle means a child
must share the meaning of a parent, so they all have to share some
meanings, of course, and agreed meanings. A specificity principle would say
a child must differ from his parent in a distinctive way, and an opposition
principle is that the concept must be distinguishable from his siblings and
the distinction between each pair of siblings must be represented in order
for the disambiguation to take place. In Artificial Intelligence, they
focus on the computational reasoning about using ontological knowledge on
the computationally intelligent acquisition and revision of new knowledge.
This would be the use of ontologies. And the principles ontologists have to
have in artificial intelligence. So they have to include computational
reasoning. They have to be able to revise new knowledge and they have to be
computationally intelligent. That's why we need also computer developers
for the development of ontologies. And that's why one of these big projects
that is being carried out in Spain is being carried out in the computer
science department of UNED, not only in the linguistics department. In
fact, this project belongs to the computer department. So for an ontology,
a lot of computer engineers are required. Knowledge basis. Finally, a
knowledge base. Why is it different from an ontology based ontology?
Because they are very similar, but an ontology is a much broader term. It
can include the database, but it also, as we have seen by now, includes a
lot of much, much more types of information. Knowledge bases collect facts,
rules. They have inference procedures, how to make use of these rules, but
the organization, the language used, the taxonomies, the meronymic
relations, the metalanguage all this is going a step further than a
knowledge base finally these are some of the links you have also in the
book for language tools and here I have included some other links which are
really really recommended this one is just to see what is an ontology, what
an ontology is, you have three links about that and here there is a link to
a very interesting paper on an introduction to ontologies and ontology
engineering these are two PDFs that I recommend and here this is the
screenshot and the link to the department of UNE that is designing this
poetry ontology and it could it is quite interesting for you to just go
through it and see how it is being developed it is called as you see poetry
lab apps they have an application you have here information about the team
and this is part also of the the laboratory of digital humanities of UNE
this professor Salvador Ros belongs to this laboratory And this is one of
the projects they have. As you can see here, it is called Poetry
Standardization and Linked Open Data. So as you can see, this is also used
for open data, for linking open data ontologies. So this was all by now. If
you have any doubts, use the forum for Unit 5. What I would like to tell
you basically also is that this last chapter is quite abstract. It has been
written in a very technical language, so don't be afraid and don't be very
scared. I know it is very hard to understand. The only thing I could do is
to recommend you go through... ...through some of the links I gave you,
apart from just understanding that, of course, this is a very complex topic
that is part of specialized technologists, computer technologists, as I
say, together with linguists. So it is really something for postgraduates
or even PhDs. And at this level or in a degree, the main idea for me was to
transmit... ...to you the difference between an ontology, even if you, when
you go to an ontology, you don't really understand how it fully works, but
at least to be able for you to distinguish what an ontology would be
compared to a thesaurus, a dictionary, a terminological database. a
conceptual map. Ontologies are, let's say, the most complex developments of
nowadays of knowledge, of handling, for handling knowledge and they are
behind of, behind machine translation, the semantic web, data retrieval, as
you can see. There are many, many ways of using them and they are very,
very specialized and there are many, many, many ontologies all over the
world. There you see, I will try to share with you in the course an
article, not for you to read it fully because you would spend a whole
semester on it, but it's an article where some scientists are doing a
revision of all the, all the available ontologies that exist nowadays and
that have been done in the course of history and I can assure you there are
projects all over the world, very, very interesting projects, very complex.
So the idea is just to have a small picture of what is being done and to
try to make some sense of it to understand that ontologies are more based
on relations on their abstract concepts. On logics that they use, make use
of meta languages, that they are based on conceptual domains and that they
are an attempt to be able to organize knowledge. to structure knowledge and
to handle knowledge in the digital era where we are now, where we need to
especially develop tools to organize all this knowledge around. And that
was it. So thanks for watching by now.