This position paper presents an algorithm, which determines similarities between text documents. These text documents are indexed with keywords and further background knowledge-terms from an ontology.The representation of the documents and the evaluation of the algorithm are used to let an ontology learn. This is shown to be one way of improving the results of the algorithm by improving the background knowledge.
Consider a human being reading texts from domains,
which to a certain extent are familiar to him or her.
The reader is capable of the semantics of the text
documents. Even if the person is not an expert in any
of the domains described in the texts, a minimal
comment we expect him or her to state is, weather
two texts are similar or not. This kind of judgement
also includes text documents, which possess
similarities though containing a completely different
vocabulary or sharing just a few common terms.
Similarities are a part of the intellectual construction
of reality [
We apply the above situation to a computer instead
of a human reader. Our goal is to express similarities
of text documents detected by an algorithm. Hence a
semantic matching problem is to be solved. The
associations and heuristics recognizing similarities
beyond equalities of character strings have to be
modeled somehow, otherwise we are restricted to
plain full text retrieval [
We consider a corpus of short text documents to be
given. Any document D is attached with a vector
v(D) including a description of its contents. The
vector is a result of abstracting a text into
descriptorsthis can be done either by a knowledge worker or
keeping in mind the constraints from the business
application we referred to in the introduction- by an
automatic indexing [
To keep our discourse comprehensive we define an
ontology to be a set of terms and their relationships.
An example of building such an ontology in an
object-oriented fashion can be found in [
German foreign policy,
Gerhard exactly the concepts of a predefined ontology, connected by the ontological relations. The relations we perform with are typed semantic ones like ’is subconcept of’, ’is differential of’ or ’is associated with’. Example of an index vector: imagine a textdocument D describing the German chancellor Schröder visiting the U.S., where he meets President Clinton and argues with him about the chair of the IMF. The vectorial representation V(D) is: INDIVIDUAL KEYWORDS: Gerhard Schröder, Bill Clinton, German government, U.S. government, IMF, Caio Koch-Weser THEMATICAL BACKROUND-KNOWLEDGE: Germany, German government, SPD, international organizations, foreign policy INDIVIDUAL BACKGROUND-KNOWLEDGE: German government, U.S. government, international organizations, USA, Germany} The entries on THEMATICAL BACKROUNDKNOWLEDGE and INDIVIDUAL BACKGROUND-KNOWLEDGE depend on the modeling of the ontology, usually there are a more keywords listed. THEMATICAL BACKROUND-KNOWLEDGE refers to the key word from THEMES, INDIVIDUAL BACKGROUND-KNOWLEDGE belongs to the INDIVIDUAL KEYWORDS. Repetitions of keywords are possible, intended to strengthen the importance of a keyword.
In contrast to classical full text retrieval technology our method provides more structure. As was to be seen from the last paragraph we include background knowledge, which delivers more than synonyms. A first version of the matching algorithm deals with a type of overlap-measuring of the entries of a pair of vectors. We named the measure 'frequency' because of the way its functionality was implemented in the Smalltalk programming language.
Let us define a frequency measure of the similarity of two sets of words as the number of words appearing in both sets (whereby every repetition of a word is extra-counted) divided by the total of all words. An example: (sun, sun, rain) and (sun, sun, snow) have the frequency 4/6.
The output S(Q,P) of the matching algorithm is the similarity of a pair of documents. In fact it is a weighted sum of similarities S(a,f),...,S(b,i), where a,...,d are the collections of keywords (i.e. the vectorial entries) from the first index vector V(P) and f,...,i are the collections of keywords from the second vector V(Q). We assume the operation on the S(a,f),...,S(b,i) to be a linear one, which means, that a linear regression is able to estimate the participating weights t,u,v,w. An estimation is necessary, because we do not know anything about the contribution of each single similarity to the whole. We summarize S(Q,P)=tS(a,f)+uS(b,g)+vS(d,h)+wS(b,i) (1) with the t,u,v,w to be estimated.
How do we get these weights ? We have to take a collection of pairs like (P,Q), in our case we took a sample of size 50, and leave it up to a human to assign the respective similarities S(Q,P). The rest is to be done via a multi-linear regression, minimizing sums of squared errors analogous to the well-known linear regression approach.
Actually the following ideas are independent from guessing the weights t,u,v,w itself. Let us return to the environment, from which the regression was implemented. We already explained , that the indexing implying the vectors V(D) strongly depends on how far the ontology is developed. Thus the latter fact has also qualitative impact on the results of the matching algorithm. We focus on improving the algorithm by imroving the ontology.
First, a sub-optimal1 approach for judging an S(Q,P)
is taking as the value of similarity the percentage of
positive answers (given by testing persons) to the
question, if Q and P are similar. From now on we
apply a way of grouping keywords, which is inspired
by [
Two starting points of structuring the documents before extracting interesting clusters, a subjective and an objective one, shall finish our reasonings. A subjective pre-grouping follows from what the testing persons percept as similar: we only regard to 1. ’optimal’ settings would be in contrast to quantifying individual and subjective judgements clusters of keywords carrying a high average of interestingness in a collection C of similar documents. To find C, we must also cluster the documents. On the other hand an objective pre-grouping is introduced by defining C via the thematic entries and clustering with respect to the theme. By objectivity in this case we denote selecting a structure given by the themes from the ontology. Here, a theme might consist of several keywords.
The last step is to present the interesting collections of keywords resulting from either grouping to an ontology engineer and to let him or her decide, if he sees a reason why the ontology might be improved by filling in relations he or she associates with the interesting groups of keywords. Note that our approach deals with strictly supervised learning.
From our rather optimistic point of view there clearly exist ideas how to attain at least clues for maintaining an ontology by reuse of the output and evaluation of a matching algorithm. So the feedback of such an algorithm is a human contribution to machine learning- detecting related keywords, which do not have a relation in the ontology yet. Of course the algorithm using background knowledge has to proof its strength- not only in matching documents, but also in case of a growing ontology- is it still exact, when there are many different relations to a keyword ? What are ontologies to master the semantic matching of documents from a special domain properly ? Within further work would we like to confirm our idea about an interplay of automated retrieval and a human editor, for example by experimenting with a certain amount of new vocabulary, which could be classified to the ontology in our framework more easily.
Another way of improving the results is refining the indexing process by the introduction of an additional qualitative tagging of keywords in our vector representation. For example, if it is obvious, that special semantics of an entry is the only interpretation existing in a document, one cuts off background knowledge, which is not in the sense of the semantics, and gets a better preprocessing.
To end our brief discussion, we mention another field of research, namely the question of how we could derive hints, which point out redundant or even improper ontological. relations.