Design and Development of Tools for Learning Semantic Relations present in Natural Language Antonio Vaquero Sánchez
Francisco Álvarez Montero
Fernando Sáenz Pérez
Departamento de Ingeniería del Universidad Autónoma de Sinaloa Departamento de Ingeniería del Software e Inteligencia Artificial Ángel Flores y Riva Palacios, s/n, C.P. Software e Inteligencia Artificial Facultad de Informática 80000, Culiacán, Sinaloa, México Facultad de Informática Universidad Complutense de Madrid Universidad Complutense de Madrid
[email protected] C/Prof. José García Santesmases, C/Prof. José García Santesmases, s/n, E-28040, Madrid, Spain s/n, E-28040, Madrid, Spain
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ABSTRACT In our previous work, we used a methodology based on software engineering principles to develop tools for building and querying electronic dictionaries based on a DAG-shaped taxonomy, with language learning purposes. However, the tools do not enforce any kind of control over the use of semantic relations during the DAG-shaped taxonomy construction. In order to teach specific semantic relations (e.g. is_a, part_of, etc.) the tools must control the appropriate usage of relations, thus preventing users from making inappropriate and inconsistent modelling choices. We intend to do so by reusing the OntoClean method that is based on the assignment of meta-properties to concepts. Therefore, the tools will control the building of the DAG by assisting the user in choosing the appropriate meta-properties, so that between two concepts there can be a given semantic relation. This paper represents a first step towards that goal.
Keywords Dictionaries, Semantic Relations, Methodology, Conceptual Model, Tools
1. INTRODUCTION We have stated elsewhere [14, 15] that language is an important value, but is nonetheless, inside the classroom, a weak knowledge domain because of the technical challenge it poses to both teachers and students. It can be argued that this knowledge deficiency can be mitigated using paper based resources (e.g. dictionaries, thesauri, etc.) or their electronic counterparts. However, as [1] point out, these resources put severe restrictions on access to words, and their conceptual counterparts: meanings. Thus, the appropriate language learning environments must be provided in order to palliate this language deficiency [20]. Moreover, the interfaces of these environments should be designed to provide an intuitive, coherent and effective way for accessing the desired information contained in the database. We have developed several tools [14, 15] that overcome some of the deficiencies of most electronic dictionaries, by providing both a form-based and meaning-based access to the information [1, 21]. The tools are based on a decompositional theory of word meaning [10] that has two levels of representation: a conceptualsemantic level and a syntactic-semantic level. The former is represented by a conceptual taxonomy or ontology that expresses
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the meanings of words, and the latter by words grouped in synonym clusters where lexical-syntactic information can be recorded. Furthermore, the tools follow an imposed constructivist model [3], that is, they provide means for gathering, representing ("externalizing" in the wording of [16]), structuring and creating navigational objects [19]. By using a taxonomy to represent word meanings, and a constructivist approach for language learning, a user can understand that the meaning of a word is related to and depends on the meanings of other words. Other specific pedagogical goal that can be attained in this way is the fact that the lexical relations of polysemy and synonymy are relations among meanings not among words. Another advantage of such an approach for word meaning representation is the proved usefulness of diagrams as cognitive tools, to express relations and meanings directly [13]. In addition, the grouping of words in synonym clusters is also cognitively meaningful, as grouping makes clear the common features and differences between groups [13]. Our latest version of the tools [15] allows representing a directed acyclic graph (DAG) shaped conceptual taxonomy. Although we have stated that our DAG is based on a single implicit semantic relation, the precise nature of the relation is not clear. This phenomenon is noticed by [2] in many controlled vocabularies of the medical domain, by [14] in several important ontology-based lexical resources, and by [7] in the electronic versions of some important dictionaries. Moreover, more than one relation can be used to build a hierarchy [2]. Semantic and lexical relations are an important aspect of natural language processing, because users, as opposed to paper-based resources, people do not store words in alphabetic order, rather they do it by meaning and relations (links/associations between words/concepts) [1]. Therefore, if we are to develop tools for the language user in general that use a DAG-shaped taxonomy for meaning classification, navigation or exploration of concepts, instances, and resources within a subject domain [4], the intended meaning of the semantic relations among them must be controlled and clarified, by using the appropriate control and verification mechanisms as parts of the tools. It would be relatively easy to take our existent tools [14,15] and integrate validation mechanisms for relations, like OntoClean [17], in them. However, we have developed these tools following
a methodology based on database development and software engineering principles [11], and we intend to keep using it because of the acknowledged advantages of applying software engineering principles and methods to the development of information systems. Nevertheless, the methodology can only account for a single relation and does not foresee the integration of any control mechanism for relations. Hence, we aim to enhance the methodology so that it can account for any number of semantic relations and to integrate the ideas in [17] to control the usage of relations by the user. This paper represents a first step towards that goal.
such a dictionary, it has a limited set of semantic relations (mainly meronymy and hypernymy) which in turn are organized into an inheritance network as to allow for simple navigation [1]. Nevertheless, just as with MRDs, the information that these relations convey is garbled in some way. The semantic relations in WordNet do not always reflect its purpose, sometimes they behave like lexical relations and sometimes like ontological relations [6, 8]. Moreover, although WordNet tries to represent concepts (using synsets) it is hard to tell, by looking at its structure, the difference between a concept and the word that describes it. Figure 2 depicts part of the structure of WordNet.
The rest of the paper is organized as follows. In section 2, we state the importance of semantic relations and claim for the need for a resource where they are explicitly clear to the user. In section 3, we comment about the poorness of the interfaces for electronic dictionaries. In section 4, a description of the OntoClean method is done. In section 5, we point out the importance of the properties of semantic relations. In section 6, we describe our proposed and upgraded conceptual model. In section 7, we make some finals remarks about the conceptual model and the methodology we use. Finally, in section 8 some conclusions and future work are underlined.
2. Semantic Relations in Electronic Dictionaries In their modern electronic form, dictionaries have tremendous potential, provided they are built in a way that allows for their use not only by experts or machines, but also by ordinary language users [21]. One of the important features that is present in any dictionary (electronic or not) is the presence of semantic relations linking lexical items, and thus forming a semantic network [7]. Nevertheless, in the electronic versions of paper-based dictionaries (machine readable dictionaries or MRD) these relations are implicit and are difficult to detect by a language user, because the information contained in these dictionaries is garbled in someway [7]. This makes these resources near to useless for vocabulary learning, mainly because the rich associative semantic network that a dictionary can contain remains inaccessible to them, and because that same network is so flawed that even a state-of-the-art NLP application could never straighten it up [7]. Figure 1 (taken from [7]) is an example of the semantic information contained in a definition.
Figure 2. Semantic Relations in WordNet We claim that a resource, in order to be of any utility to a language learner needs to have a clear-cut structure, where all the subtle things that are normally implicit or clouded in most dictionaries are explicitly represented at their appropriate level. Moreover, from the MRDs and WordNet examples we can deduce, that if a learning tool is to constructively allow a learner to create a semantic network, this tool must enforce relations control mechanisms, so that the network is not created (as it happened with MRDs and WordNet) relying essentially on the intuition of the developer.
3. Interfaces for Electronic Dictionaries A very important aspect in developing an electronic dictionary is to provide the future user with a suitable interface. Nevertheless, although electronic dictionaries have become increasingly popular in recent years, current dictionary interfaces were not developed thinking in the ordinary language user [1, 21]. Thus, this raises the question: what is a dictionary good for, it one cannot access the data it contains?
Figure 1. Semantic Information in Definitions One way to palliate this is to have a dictionary that represents the structure of the conceptual lexicon. WordNet [9] is an example of
Apparently, moving from paper to an electronic form was not followed with the corresponding change to the facilities provided for the dictionary user. For instance, in [1] the authors report that most MRDs are basically electronic versions of their paper counterparts. Hence, their interfaces obey, most of the time, the conventions of their paper ancestors, and obviously, all this puts severe constraints on access.
The same applies to any other resource that could be regarded as an electronic dictionary (e.g. WordNet, Mikrokosmos, etc). For example, the tools available for WordNet just serve for comparing and ordering the elements in other synsets, and to enlarge the resource [5]. Thus, they can only be used by experts (lexicographers, knowledge engineers, etc.). Hence, evidence shows that developing interfaces for electronic dictionaries, so they can be used by language learners, is an underrated issue that clearly undermines the hopes of exploiting electronic dictionaries in general as learning tools.
4. The OntoClean Method In order to prevent language learners from making inappropriate and inconsistent modeling choices while using our tools to build dictionaries with a DAG-shaped taxonomy, we aim towards the integration of mechanisms to evaluate the taxonomy with respect to its main purpose: specifying a given vocabulary’s intended meaning. With that in mind, we intend to apply the ideas of the OntoClean method [17] to assure the consistency of the taxonomy, and to control the user’s usage of semantic relations. It can be argued that these ideas require the use of modal logic, but as [18] states, one does not require modal logic nor modal logic reasoning to apply these ideas to the development of ontology-based systems. OntoClean is based on notions such as: rigidity, identity, dependency and unity [17, 18]. These notions are called metaproperties, and are useful to detect false subsumption relations. They can have different values in different concepts (e.g. yes in person, no in student, etc.). To indicate their values, special symbols are used: + for yes, – for no and ~ to indicate that a concept (note that concepts are also referred as properties in [17, 18]) can stop having a meta-property at some point. Let us take a look now at these notions.
4.1 Rigidity This notion is defined based on the idea of essence. A property is essential to an individual if and only if necessarily holds for that individual. Thus, a property is rigid (+R) if and only if is necessarily essential to all its instances. A property is non-rigid (R) if and only if it is not essential to some of its instances, and anti-rigid (~R) if and only if it is not essential to all its instances. For example, the concept person is usually considered rigid, since every person is essentially such, while the concept student is not normally considered anti-rigid, since every student can possibly be a non-student a few years later.
not part of x. For example, a hole in a wall is constantly dependent on the wall. The hole cannot be present if the wall is not present. A property P is constantly dependent if and only if, for all its instances, there exists something they are constantly dependent on. For instance, the concept hole is constantly dependent because every instance of hole is constantly dependent.
4.4 Unity We can say that an individual is a whole if and only if it is made by a set of parts unified by a relation R. For example, the enterprise Iberia is a whole because it is composed by a set of people that are linked by the relation having the same president. A property P is said to carry unity (+U) if there is a common unifying relation R such that all the instances of P are wholes under R1. For example, the concept enterprise-with-president carries unity because every enterprise with president is made up people linked through the relation having the same president. A property carries anti-unity (~U) if all its instances can possibly be non-wholes. Properties that refer to amounts of matter, like gold, water, etc., are good examples of anti-unity.
4.5 A Small Example We mentioned above that the meta-properties are useful to detect the misuse of the subsumption relations. For instance, we have said above that student is anti-rigid (~R) and person is rigid (+R). Hence, according to the definition of rigidity, student could never subsume person. In fact, if we had this link, what would it happen if a person was not student any more? Nevertheless, the method requires that the assignment of metaproperties to concepts in an ontology be performed by hand. This analysis in all cases requires that the modeler be very clear about what each concept means. Hence, there is the need for some sort of automatic-support in order to help the user. In [17, 18] the authors present a question/answer (Q/A) system designed to assist a modeler in choosing the appropriate meta-properties for the concepts, by asking a series of questions. Figure 3 shows a sample output of such a system.
4.2 Identity A property carries an identity criterion (IC) (+I) if and only if all its instances can be (re)identified by means of a suitable “sameness” relation. A property supplies an identity criterion (+O) if and only if such criterion is not inherited by any subsuming property. For example, person is usually considered a supplier of an identity criterion (for example, the fingerprint), while student just inherits the identity criterion of person, without supplying any further identity criteria.
4.3 Dependency An individual x is constantly dependent on y if and only if, at any time, x cannot be present unless y is fully present, and y is
Figure 3. Sample Output of a Q/A System for the Concept Animal This is a simple example, but it serves to illustrate how the methodology works. More complete examples on how to apply the method can be found in [17].
5. Properties of Semantic Relations The OntoClean method comes in handy to decide if a given ontological relation can be established between two concepts. Nevertheless, it focuses on the nature of the properties involved in relations, rather than on the semantics of the relations themselves [17]. This means that the semantics of relations are taken from granted in this method. However, the meaning of each corresponding relational expression between two concepts must be established in an unambiguous way, in order to further assist the users in avoiding errors while building a vocabulary [12]. Hence, each relation must have a set of properties defining it, which will assure that the relation will be used in a uniform way across subject domains. Table 1 shows two of the most used and ubiquitous semantic relations and some of the properties they could have. Table 1. Semantic Relations and Some of their Properties Relations
Transitive
Symmetric
Reflexive
is_a
+
-
+
part_of
+
-
-
6. Designing a Monolingual Ontology-Based Dictionary We have designed our previous tools following a decompositional theory of word meaning [10] that has two levels of representation: a conceptual-semantic level and a syntactic-semantic level. The former is represented by a conceptual taxonomy or ontology that expresses the meanings of words, and the latter by words grouped in synonym clusters where lexical-syntactic information can be recorded. Hence, we have that a cluster of terms (from now on synset), and not each term individually, identifies each meaning represented as a concept in the taxonomy. Furthermore, synonymy holds for all the terms in this synset. Polysemy comes from the fact that a given term may be in different synsets (obviously related to different meanings). In sum, in our dictionaries the meaning of words is represented by concepts instead of terms. Synonymy is a set-oriented property of terms, and the set itself is related to a concept, instead of having every term describing a concept. Finally, in order to fulfill the intensional definition of concepts explained in [11], a definition is needed for each concept, and a non-empty synset is needed for such a concept.
6.1 Conceptual Model of the Dictionary Following these premises, we propose the E/R scheme (upgraded from [11]) shown in figure 4 as a result of the first stage design (conceptual modeling). However, because the constructors of E-R models, as well as most of the elements composing the E-R model presented in this article have already been explained in [11], here, we will focus only in the new elements that have been added or changed in order to upgrade the previous model.
Figure 4. Conceptual Model for a Monlingual Dictionary
6.1.1 Entity Sets The entity set Concepts denotes the meaning of words, and it has two attributes: ConceptID (artificial attribute intended only for entity identification which shall be explained later), and Definition, intended for the textual definition of the meaning. The entity set MetaProperties represents the set of meta-properties described in section 5, and it has one attribute: MetaProperty, intended for the textual name of each property (i.e., R, I, D, U). The entity set Relations represents the set of relations that can exist in an ontology, and it has one attribute: Relation denoting the textual name given to each relation (e.g., is_a, part_of, etc.). Finally, the entity set Properties represents the properties of relations pointed out in section 6, and it has one attribute, Property, that denotes each property.
6.1.2 Relationship Sets The relationship set HasMetaProperty is used to assign metaproperties to concepts, and it has one attribute, Value, which denotes that (as stated in section 5) for each concept a metaproperty can have different values (i.e., +, -, ~). The ternary relationship set R is used to represent that the concepts in an ontology are linked by at least one relation. The relationship set HasProperty is used to convey that relations could have attached a set of properties.
6.1.3 Constraints The ternary relationship R is many to many because a concept can have more than one parent (except the root node) and a concept can have several child concepts. Furthermore, the same concept can be linked to other concepts by more than one relation. The relationship HasMetaProperty is many to many, because concepts can have a set of meta-properties, where each property has a different value. For instance the concept “food” could have the following attributes and values: {+I +D –O –U –R}. Moreover, the same meta-property can have the same value in different concepts. For example, the concepts “fruit” and “apple”, they both could have “+R”. The relationship HasProperty is many to many, because each relationship can have one or more
properties attached, and each property can be attached to different relations. Note that there are less total participation constraints that one could expect for the following reasons (all of them derived from the incremental creation of a database in-stance). A concept does not have to have a related definition. Finally, the relationship set R has no total participation since a concept may have no parent (the root node), and a concept may have no children (leaf concepts). The final (lexical) database containing the dictionary should hold total participation for the former constraints, but we cannot impose them as such because of the authoring nature of dictionaries. Therefore, we are ought to provide consistency checking features to the dictionary authors. These features must inform the author about authoring constraints which are violated by the instance database. Such constraints which may be violated during the authoring are known as soft constraints, by contrast with the hard constraints that every database instance must hold at any time. All of the attributes are primary keys. This means that they have an existence constraint automatically attached. However, although we can think of the attribute Definition as a candidate key, it cannot be since it can have a null value. Therefore, an extra attribute is needed for identifying this entity set, which we call ConceptID. In the physical model, these attributes must have a type for identifiers (such as the sequences or autonumbers). From the discussion above, we should also impose soft existence constraints (for instance, there should be a definition for each concept) and hard uniqueness constraints (each definition must be different) for all of the non-primary key attributes.
Figure 5. An Instance of the Conceptual Model
6.2 Relationships among Metaproperties, Concepts and Relations Figure 5 depicts an instance of the E-R model we just described. The square boxes represent 3 entity sets (i.e. Concepts, Relations and MetaProperties), whereas the ovals represent 3 relationships (i.e. MetaProperties, R and HasProperty). We can see that the concepts “fruit” and “apple” have assigned one or more metaproperties, each one with a value. This in turn will determine if both concepts can be linked via a given relation that in turn could have one or more properties defining its behavior.
7. Some Final Remarks about the Conceptual Model and the Methodology A question that the reader might have is the following: what are the dictionaries that can be developed from the conceptual model and using the methodology? The answer is subject and domain specific dictionaries. In order to create an instance of the conceptual model, and along with it, its management tools, a finite and specific set of relations must be established “a priori” through an analysis of the domain (the subject or knowledge area), and each relation will have a specific set of properties and meta-properties. For instance, if we needed a dictionary with only the is_a relation, then we would have to determine which are its properties, as well as the necessary meta-properties that are needed to determine if between two concepts there could be an is_a relation. The same applies for any single relation that we want to represent in the resource. If we want a resource with two relations, then we will have to determine the properties and meta-properties for both of them, and so on. These decisions will then be reflected in the management tool, that is, the tool will not allow the user to create more relations than the ones previously established, and the meta-properties he could assign to concepts would be only those involved in these relations, and nothing else.
8. Conclusions and Future Work We have made a first step towards the enhancement of our methodology for the creation of tools to build and query electronic dictionaries based on DAG-shaped taxonomies. We have acknowledged the need for control and verification mechanisms in the creation and usage of semantic relations, and pointed out its importance in learning environments that use taxonomies to organize knowledge. Therefore, we have modified the previous conceptual model proposed by the methodology [11], so that it could account for any number of semantic relations (as long as they are binary). Furthermore, the principles of the OntoClean evaluation method have been integrated as part of the model. Hence, this model will allow us to create: a) a dictionary where the intended meaning of relations is clearly stated; and b) a management tool where the OntoClean method can be reflected in order to control the use of relations. This is our major contribution: the refinement and upgrade of a complete methodology to be used in the development of electronic dictionaries. Furthermore, the resultant tools will be useful to teach students how to properly use semantic relations to structure domains as taxonomies. As for the methodology, its major contribution is that it forces electronic dictionaries developers to do so in a step by step and systematic way, where all the modeling decisions are clearly stated and documented. It must also be said that the methodology will only allow the creation of management tools with a specific set relations: the relations needed to sufficiently represent a subject domain or the ones needed for a specific task. This questions a priori the reusability of the tools, nonetheless, our aim is not to achieve global or general reusability, but to develop a semantically clear (at the relation level), well-founded and wellstructured resource. In spite of this, the tools could be used to
develop a dictionary, for a subject or domain that only needs the relations controlled by the tools. Nevertheless, there are a few issues that we need to consider before going through the logical and physical design stages of the methodology, and thus develop the interface(s). First, how to implement the Q/A systems as part of tools, so that the notions described in section 4 are transparent to the user. Second, how to improve the friendliness of the interface by providing the user with facilities that allow him to quickly find the needed information (perhaps integrating the ideas of [1, 4, 21]). In spite of this, we have taken a first step towards those goals, by clearly stating and depicting the structure, scope and limitations of our future dictionary and its management tools.
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