Semantics of Query Construction User Interfaces of ... - Semantic Scholar

Semantics of Query Construction User Interfaces of Web-based Search Engines for Digital Libraries Michael Fuchs, Lieming Huang, Claudia Niederée, Matthias Hemmje, Erich Neuhold Fraunhofer IPSI, Dolivostrasse 15, 64293 Darmstadt {fuchs, lhuang, niederee, hemmje, neuhold}@ipsi.fraunhofer.de

The effective and dynamic integration of digital library search and meta-search engines into the Semantic Web is a demanding as well as central task. We tackle this task by a flexible Semantic Web Application approach, which is based on the support of two additional types of models, semantic domain models and conceptual user interface models, as well as of flexible mappings between these models. Based on an analysis of query construction interfaces of DL search engines an query construction ontology has been developed as a basis for the creation of semantic domain models for DL search engine user interfaces following the Semantic Web Application approach. This ontology is discussed in this paper and applied in the construction of a semantic domain model for a simple example query construction user interface.

Introduction The revolution of the World Wide Web has set off the globalization of information access and publishing. Organizations, enterprises, and individuals are enabled to produce, publish, and update data on the Web resulting in an explosive growth of available information. As a consequence, it becomes more and more difficult for users to accurately find and completely retrieve information they need. Although there are thousands of general-purpose search-engines and search technologies improved considerably in recent years, most users still find it hard to retrieve effectively and efficiently what they are looking for. This holds especially true for scientific and technical publication domains. To improve the situation, domain specific digital libraries (DLs) as well as corresponding specific-purpose, i.e., publication-oriented search engines (SEs) have been introduced to the Web since its earliest days. However, although many such publication-oriented SEs of DLs are providing support for accessing published information through the web, they are still creating another challenge on the level of so called meta-search activities (see [Dreilinger, et al. 1996, Gauch, et al. 1996, Gravano, et al. 1997, Lawrence, el al. 1998, Hemmje, et al. 1996]) to users. These challenges exist especially for naïve users, i.e., for information seekers that are unexperienced in the use of the so-called query-construction user interfaces (QCUIs) of the different SEs of DLs. These are usually very divers with respect to their capability of supporting users’ in expressing their information needs into explicit information requests. Within our work on the adaptive meta-search engine SPOMSE [Huang, et al. 2000, Huang, et al. 2001a, Huang, et al. 2001b, Huang, et al. 2001c] several concrete examples of QCUIs of SE for DLs have been analysed and we have already demonstrated how an adaptive meta-search approach can help to ease the above mentioned challenges. Besides meta-search support, other innovative approaches in Web and Internet technology open promising new opportunities for content and service provision in the World Wide Web. The Semantic Web Activity, one of the

two most important current Web trends, introduces semantic content and functionality annotation that can be interpreted by software agents enabling manifold new applications and improved support for individual and cooperative tasks [Berners-Lee, et al. 2001]. Intelligent and flexible information retrieval support in Web-based SEs of DLs is one of these applications. However, the Semantic Web also imposes some challenges on the development of SEs for Digital Libraries (and on Web applications in general) that can be operated effectively in the Semantic Web context: − In the Semantic Web, web-based SE for DL applications move from a purely human user community towards a mixed user community consisting of humans as well as of software agents. This results into new requirements towards models for such Web applications’ user interfaces; The requirement of representing potential interaction with humans as well as with software agents is best met by a conceptual user interface model that describes the dialogs with the system on a conceptual level that can be dynamically translated into a (user) interface language adequate for the respective "user" (human or agent). − Automatic interpretation of content and functionality, one of the main building blocks of the Semantic Web, is based on interlinking local content and domain models with globally defined interpretation schemes like vocabularies and ontologies; Such interlinking has to be systematically integrated into web-based SEs for DLs operating in the Semantic Web. This paper presents a Semantic Web Application approach based on a semantic domain model and a conceptual user interface model that enables the implementation of SEs for DLs application in the Semantic Web. To increase practical relevance of the paper we also discuss a query construction ontology OSSPub that acts as a shared foundation for defining the semantic domain models of QCUIs of different SEs easing the interpretation by agents as well as the construction of meta-search functionality. Illustrating the approach, this ontology is used to define a semantic domain model for a simple example QCUI. The rest of the paper is structured as follows: Section 2 discusses related work. Section 3 summarizes the results of analyzing form-based QCUIs and discusses common elements identified in this process. Section 4 introduces the Semantic Web Application approach focusing on the new types of models required for the effective support of such applications: semantic domain models and conceptual user interface models. QCUIs of SEs for DLs are considered as an application of the Semantic Web Application approach in this paper. Section 5 introduces OSSPub, an ontology for query construction that provides the basis for the definition of semantic domain models for concrete QCUIs. Such a domain model is discussed in section 6 together with the conceptual user interface model and the mapping between these models for a simple example QCUI. We conclude the paper with a summary and some ideas for future work in the domain.

Related Work The Semantic Web Application approach introduced in this paper introduces two new models and flexible mappings between these models. It, thus, follows the model-based software development approach. Similar model-based frameworks are [Puerta 1997], [Puerta, et al. 1998], [Foley, et al. 1998], [Wiecha, et al. 1998], and [Eisenstein, et al. 2001]. Mobi-D [Puerta 1997] is an interactive environment where declarative models can be connected (see [Puerta, et al. 1999]). Mobi-D distinguishes two different kinds of models, the abstract and the concrete models. All combinations of mapping are possible between abstract and concrete models. Furthermore, in Mecano [Puerta, et al. 1998], a model-based user interface development environment is introduced, which provides a tool for creating domain models. Based on this model, high-level dialogs (e.g. workflow and navigation structure of windows) as well as low-level dialogs (one step within the high-level dialog e.g. form input field, button) can be generated and later customized. However in contrast to our approach the domain model has to be constructed manually and is not directly coupled to existing application data. SUIMS, another model-based user interface construction approach described in [Foley, et al. 1998] is based on the so-called ART Schemata where a user interface model is composed from objects, actions, parameter, attributes and their types, pre-

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conditions, and post-conditions as well as the corresponding relations between them. These schemata can be instantiated with different parameters resulting in the creation of different types of user interfaces. To build up the schemata, a highly skilled programmer is needed, in contrast to our approach where model construction and mapping definition is supported by a user-friendly application authoring tool suite. Our conceptual UI model is based on the XForms standard [Dubinko, et al. 2003]. Other formats for conceptual UI models have been proposed like e.g. the Ozone UI ontology in the Haystack framework [Quan, et al. 2003]. This UI model is based on RDF easing the coupling with the domain model, but lacking the UI specific support of XForms (events, predefined controls, validation of input, etc.). The WebML Application approach described in [Ceri, et al. 2001] uses in addition to the Domain Model (Entity Relationship like diagram) a so-called Site View model. This model consists of a composition model (UI units together with the mapping information to the corresponding Domain Model Object) and a navigation model (links between UI units). Both models are represented by WebML [Ceri, et al. 2000] .

Analysis of Query Construction User Interface Semantics In this section, we first of all provide a taxonomy of control components of QCUIs of SEs for DLs. In the next step we introduce constraints rules that can be used to describe various constraints among the QCUI controls of SEs for DLs. A Taxonomy of Query Construction User Interface Controls From working with and analyzing the DL-QCUIs in [Huang, et al. 2000, Huang, et al. 2001a, Huang, et al. 2001b, Huang, et al. 2001c], we learned that there are usually many discrepancies among the QCUIs of heterogeneous sources. The interactive query construction controls available in such QCUIs can be divided into an overall taxonomy of QCUI controls based on their role in the query construction dialogue (see Figure 1): (1) Classification Selection Controls (CSC): A classification selection control is a component of a QCUI that is used for limiting an information request to certain domains, subjects, categories, etc. For example, in many QCUI for publications, there is a choice control for users to limit their searches to certain types of publications (e.g., a specific journal). (2) Result Display Controls (RDC): A result display control is a component of a QCUI that is used to control the formats, size or sorting method of the query result. In many publication-oriented QCUIs, for example, a result sorting control enables sorting of the retrieved results by “author’s name”, “date” or “relevance”.

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Figure 1: A Taxonomy of Query Construction User Interface Controls (3) Query Input Controls (QIC): The building blocks of terms, term modifiers and logical operators of a QCUI constitute the set of query input control components of a QCUI, through which users express their information requests which are to be translated into formal queries to the underlying retrieval mechanism. In an adaptive, progressive QCUI, the number of these controls may change while users input and iteratively refine their queries. − Terms (T): A term as understood in the sense of information retrieval QCUI is the content keyed into an input box on the user interface, which is different from the usual meaning of “term” because a term can be a single keyword, multiple words, a phrase, or a Boolean expression. In some cases, the input term may support wildcards, truncation, stemming. It may be case-sensitive, and might drop stop-words, hyphens, diacritics and special characters. − Term Modifiers (M): A term modifier is used to limit the scope, the quality or the form of a term. Two types of term modifiers are distinguished, field modifiers and term qualifiers. Field modifiers specify the bibliographic fields the term is applied to (e.g. Title, Full-Text, Keywords, Abstract, Author, etc.). Term Qualifiers describe the way in which the term has to be interpreted (e.g. Exactly Like, Multiple Words, Using Stem Expansion, etc.). − Logical Operators (L): A logical operator is used to logically combine two terms to perform a search. For example, a logical operator can be AND, OR or NOT. Constraints Rules Due to various potential types of conflicts that may appear during the expression of a users information need into an explicit information request, any QCUI must seek to coordinate such conflicts for the purpose of both constructing a harmonious QCUI, and enabling a more accurate translation from the information request that was entered by users into the QCUI to the final formal query for the underlying retrieval mechanism of the target collections and their internal resources. Constraints rules are often employed to record the conflict information between the QCUI controls of a source or between different sources. For example, a term belonging to the ‘Date’ field cannot be modified by the ‘Sound like’ qualifier, while a term belonging to the ‘Author’ field can be modified by this qualifier but not by ‘Before’ or ‘After’. When, e.g., the SPOMSE meta-search engine dynamically constructs the QCUI, these constraints rules have been considered in order to eliminate various kinds of potential conflicts and to let users input their information requests more easily and accurately.

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Figure 2: Three examples of applying constraints rules to QCUI construction Figure 2 displays two screen shots of a search engine using constraints rules to coordinate interactions in its QCUI. In Figure 2(a), the ‘author’ field can only be modified by ‘Exactly like’, ‘Sound like’ and ‘Spelled like’ qualifiers. While in Figure 2(b), the ‘date’ field can only be modified by ‘Before’ and ‘After’ qualifiers and the search terms are two choice controls (one for month and another for year) instead of an input-box. Some examples of explicitly formalizing such constraint rules are listed in the following: 1. 2.

Fieldi.ENABLE()→Qualifieri.DISABLE(, , , ≠ , ≤, ≥) CategoryCSC.ENABLE() → JournalCSC.DISABLE( , , … …)

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Logical-Operator-Control 2.ENABLE() Disabling some items of a control does not mean the other items of this control will be enabled; it means that users can select one or more of these remaining items of the control or select nothing. Enabling an item means that this item has been selected and will be used to construct the formal query for the underlying retrieval mechanism from the explicit information request of the user. For example, in the above-mentioned first rule “Fieldi.ENABLE()→ Qualifieri.DISABLE(, , , ≠ , ≤, ≥ )”, if users select the item in the ith field modifier, according to the rule, seven items (e.g., , , etc) in the corresponding ith term qualifier are disabled. But users can select other items in this term qualifier, such as , , , and so on.

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In the second rule, when users select the item from the CategoryCSC, some items (these journals are not related to computer science) in the JournalCSC will be disabled. In the third rule, when users want to search publications from the journal < ACM Transactions on Information Systems >, the meta-search engine will automatically enable the item in the Search-EnginesCSC. In the fourth rule, if users select the NCSTRL search engine in the Search-EnginesCSC and set the first logical operator control to be , depending on the query capability of the NCSTRL search engine, the second logical operator control will be automatically set to be , too.

Semantic Web Applications Implementing QCUIs for SEs in the context of the Semantic Web raises the following problem: The “users” of the interface are no longer restricted to humans, but also include software agents that have to interpret the information exhibited by the QCUI as well as the type of dialog (input) expected from a user of an QCUI. If relying only on, e.g., an HTML-encoded web page, especially software agents cannot extract sufficient information to perform their operations as they will likely fail − to know how to enter into a dialog (i.e. an interaction) with the system which is, e.g., in the case of an HTMLencoding mainly due to the lack of logical structure in HTML and further hampered by HTML’s relaxed syntax rules. − to infer what the (input) fields in, e.g., an HTML-encoded Web page mean and which restrictions are available for qualifying the allowed input (Constraints, Types). Humans use their cognitive abilities as well as their experience with similar types of Web applications for overcoming the interpretation challenges of QCUIs. Semantic Web Application Models The above mentioned two problems can be overcome by introducing two new types of models for Web applications: These models are on the one hand so-called conceptual user interface models that systematically describe possible interactions with the system on a conceptual level and on the other hand so-called semantic domainmodels that describe application domains and relate model components to community-accepted ontologies and vocabularies. As we are discussing the semantic support of QCUIs in the remainder of this paper, we are assuming form-based user interfaces and corresponding conceptual models that support the query construction dialog and interaction with the user or agent in a well-structured and well-understood interaction paradigm. Semantic Domain Model While all Web applications embody some type of a domain model that is underlying the application this model is usually made explicit in a systematic way at design time only, e.g., when creating a UML class diagram during object-oriented analysis and design for a software system. When the system is implemented (and operational) the domain model is represented in a partly fragmented and partly duplicated way within the code of the application layer (e.g. Java classes) and the schema of the underlying relational database. Furthermore, the model is a local domain model relying on a conceptualization specific for the implementation of the application. In order to enable an extended use of the domain model in the Semantic Web context the domain model has to be made explicit and it has to be set into relationship to a global ontology that is also accessible by agents that want to interact with the system. With global ontology we mean an ontology that has been published and about which some agreement has been achieved within a user community, i.e., an ontology which represents some shared conceptualization (understanding) of the domain within the community [Gruber 1993][Borst 1997] . Referencing

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such an ontology (or several of them) eases the task of the agent to interpret the data exhibited within the Web application user interface, since he can relate them to his knowledge. Conceptual User Interface Model In Semantic Web applications an additional layer has to be introduced between the application layer and the actual user interface that manages a conceptual model of the user interface. The conceptual user interface model describes the (user) interface of the system on a conceptual level independent of the respective user interface agent. Such conceptual user interface models can be automatically transformed into specific user interfaces for different types of user agents (like e.g. for a Web browser, a PDA, or a mobile phone) by adequate processors introducing more flexibility with respect to the type of client that the user interface is finally rendered and displayed on and avoiding the bias towards one type of UI paradigm. However, the following advantage of introducing a conceptual user interface model is more important in the context of the Semantic Web: By communicating the conceptual structure of the interface to, e.g., software agents in the Semantic Web, these can use it to learn how to use the interface, e.g., where are fields to fill and elements to select from a list. However, this requires a shared understanding and a corresponding conceptualization that can be used to learn about the existence of potential functions of the interface and about the way to address them. This Semantic Web Application approach has been implemented as part of a flexible framework following the Meta-Design approach [Fischer, et al. 2000]. For further details about this framework see [Fuchs, et al. 2002][Fuchs, et al. 2003][Niederée, et al. 2002][Muscogiuri, et al. 2002].

An Ontology for Searching Scientific Publications (OSSPub) Each SE has its own domain model underlying the query formulation capabilities offered by its QCUI. These capabilities are influenced by the underlying SE, the type of information collection to be searched, and available metadata as well as by the intended application domain of the search engine and the type of targeted users. However, an inspection of different DL-QCUIs during our earlier work for the SPOMSE meta-search engine as mentioned above revealed that a common model underlying most form-based DL-QCUIs can be extracted. The most important building blocks of this model have been outlined in the above taxonomy of query construction user interface controls. These building blocks are now taken as a starting point for the definition of an ontology for the description of the query capabilities of search engines (OSSPub).

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Figure3: RDF Schema Representation of the OSSPub Ontology In the Semantic Web QCUI approach OSSPub is used as a reference point for the definition of individual semantic query construction capability domain models. One can also say it provides the meta-level for the description of these models.

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Figure 3 displays the core of this ontology represented as an RDF Schema graph composed from RDF classes and RDF properties. Note, that to ease reading, RDF classes in Figure 3 are colored, whereas RDF Properties are displayed in white (instead of connecting them explicitly with the RDF classes rdfs:Class and rdfs:Property). The ontology also contains some individuals like, e.g., the Logical Operator instances and and or. The ontology describes a vocabulary for the description of QCUIs. However, the focus is on the domain model underlying this process not on the actual control elements used in the user interface. A QueryInterface contains three different types of elements: a) The search space can be restricted by Classifications, b) the query interface enables the formulation of TermExpressions, and c) the display of the query result can be influenced (QueryResultControl). There are two types of classifications supported. CollectionClassifications enables the restriction to one or more specific collections or parts of them. ContentClassification is used for subject-based restriction of the searched information space. Of course available subjects depend upon the selected collection. This dependency is expressed by the constrainedBy property. Each term expressions can be either basic (BasicTermExpression) or composed (ComposedTermExpressions). A ComposedTermExpression combines two TermExpressions by a logical operator. A BasicTermExpression offers one or more potential TermDescription that can be used for the definition of such a term expression. Each TermDescription is a triple of three kinds of information: • The FieldModifier specifies the bibliographic field(s) the term is referring to (e.g. author or title). • The TermQualifier is used to refine the term that is used in the term expression by giving hints for its interpretation, e.g. “spells like” for a string value or “before” for a date; The query interface may offer a set of Term Qualifiers for each term description. Not all term qualifiers can be applied to a specific field modifier. They have to fit the type of content of the respective field. Therefore, adequate restriction can be formulated in the domain model using the property restrictedTo. • The TermValueRange restricts the range of values allowed for the term; this is done by referring to a vocabulary like ACM Computing Classification System and/or by a type expression using e.g. the types from XML Schema; Furthermore, it is possible to provide a default value for the term by using the property termValue. Two ways of influencing the result display are considered in OSSPub. On the one hand, the number of results displayed per page (PageNumber) or in total (TotalNumber) can be restricted. On the other hand, the order of the results can be controlled (OrderByControl) by selecting a field modifier, on which the ordering is to be based , and by deciding, if the ordering should be ascending or descending using the property orderDirection. The RDF graph just illustrates the core of a query capability ontology. For the SWAN-based Meta-search engine the ontology has been formulated using OWL classes and some cardinality constraints going beyond the information depicted in the RDF graph. However, we followed the ontology design principle of minimal ontological commitment [Gruber 1993] and, therefore, refrained from adding too many constraints to the ontology. This would restrict the reusability of the ontology for a wide variety of QCUIs. Additional constraints are formulated as part of the domain model for specific QCUIs. A domain model for an example SE that is based on this ontology is presented in the next section. Of course the specification of a concrete query defined with a QCUI uses mainly the same building blocks. That is why the concept QueryExpression has been inserted into the ontology and connected with the concepts of the ontology. Note, however, that more restrictive cardinality restrictions hold for the query expression than for the QueryInterface, which describes query formulation capabilities. In addition to OSSPub for QCUIs further ontologies/vocabularies can contribute to a consistent externalizations of information requests and query formalization in Semantic Web based QCUI for DLs. These are for example: • Vocabularies for Field modifiers, e.g., Dublin Core [DC 1999] and Marc [MARC2000]; • Ontology/Vocabulary of term qualifiers • Vocabularies/Ontologies for term values as e.g. Library of Congress Subject Headings (LCSH, [Lawrence, el al. 1998]), ACM Computing Classification System [Coulter, et al. 1998];

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All these vocabularies and ontologies together provide the basis ontology for describing the query construction capability of a concrete SE’s QCUI, as it is demonstrated for a simple example query interface in the next section.

Example Domain Model and Conceptual User Interface Starting from the concepts and terminology defined in OSSPub and the other query construction related vocabularies a semantic domain model of the query capabilities of an exemplar concrete QCUI as displayed in Figure 4 can be created. [Fuchs, et al. 2003a, 2003b, 2003c] shows a complete domain model for this QCUI depicted in Figure 4. The domain model explicitly refers to the concepts and properties of OSSPub and defines specific subclasses with additional constraints that hold for the domain model mainly using OWL property restrictions. An RDF instance that is based on this domain model is depicted in Figure 5. This instance can be considered as a template for queries that can be formulated using the example QCUI. Indeed, such instances are used as a basis for the mapping between conceptual user interface elements and domain model elements in the Semantic Web approach. They also act as query templates that are filled with values after user interaction.

Figure 4: Example QCUI of a sample search engine In more detail, the described interface enables the construction of up to two term expressions that are automatically combined by a logical or (since only this option is given in the domain model). The first term expression may either specify an author referring to dc:author or it may specify a date before (term qualifier sp:Before) or after (term qualifier sp:Before) which the publication was published. The adequate term qualifier can be selected by the user. As a second term expression keywords may be defined. The field is referring to the Dublin Core element subject and keywords are to be taken from the ACM Classification Schema (vocabulary for the term value range). The semantic domain model also provides typing information that can, for example, be used for client or server side validation of user input as well as for the (semi-)automatic selection of adequate user interface controls: authors and keywords are strings, whereas the date refers to the XML Schema data type date. For making references to other vocabularies (here: Dublin Core, OSSPub, ACM Classification Schema) explicit and machine-interpretable the mechanisms proposed by the Semantic Web technology are used (see [Fuchs, et. al 2003a, 2003b, 2003c]). This enables partial interpretation of the models by agents of the Semantic Web and, in our application, by the processors creating the search engine wrappers and the query translation procedures.

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Figure 5: RDF Representation of an Example Query Interface Following the Semantic Web oriented SE approach, the semantic domain model is only one of the proposed additional Semantic Web application models. The second model, the conceptual user interface model, is based on the XForms standard. Figure 6 illustrates three building blocks of the Semantic Web oriented approach for our example SE: − the conceptual user interface model in XForms notation (included in the HTML body element). Each of the XForms control elements specifies a component of the conceptual user interface (e.g. input and select1); − part of the semantic domain model that is exhibited as part of the user interface following the Semantic Web approach; the template that is based on the semantic domain model (see above) is included in the XForms instance element (located in the HTML head element) that is used to describe the data for the user interface or, when following the Model-View-Controller approach that underlies the XForms approach, the Model; the template refers to the published semantic domain model that can be consulted by software agents for interpretation; − the mappings between the components of the conceptual user interface and the semantic domain model template. In the XForms document the mappings are encoded as XPath expressions that refer to the domain model template included in the instance element (see above); these references are included into the respective XForms control elements.

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Figure 6: An example for a conceptual user interface model with domain objects

Conclusions and Future Research In this paper, we have presented a way for integrating QCUIs of SEs for DLs into the Semantic Web such that they exploit as well as serve this operation environment in an optimal way. The discussed Semantic Web Application approach eases the construction of meta-search engines by referring to a shared ontology (exploitation of Semantic Web technology) and also allows agents to access such search engines in a more systematic way. For this purpose an ontology (OSSPub) that describes query construction capabilities and is based on an analysis of QCUIs of DL search engines has been developed and applied for the construction of a concrete semantic domain model.

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We are well aware that the OSSPub ontology for describing query construction capabilities presented in this paper is just a starting point. Due to the claim of ontologies of representing a shared understanding an iterative community process of ontology publication, discussion, and refinement is required. We consider the ontology described in this paper as well as the community feedback to this publication as important contributions to this process. The evaluation, further development and refinement of the query construction capability ontology as well as of further related ontologies will strengthen the Semantic Web oriented approach and SEs for DLs developed upon it. Future developments in Semantic Web technologies like further evolution of standards and upcoming new ontologies for areas related with query construction and searching in scientific publications will be taken into account.

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