Conceptual Modeling of Open Information Systems

Conceptual Modeling of Open Information Systems Roman Lukyanenko Faculty of Business Administration Memorial University St. John‘s, Canada [email protected]

Jeffrey Parsons Faculty of Business Administration Memorial University St. John‘s, Canada [email protected]

ABSTRACT Traditionally, the research and practice of conceptual modeling assumed all relevant information about a domain could be discovered through users-analyst communication. The increasing reliance of organizations on externally produced information, such as online user-generated content, challenges this and other long-held assumptions underlying conceptual modeling. The information systems used in these environments are open information systems, in which at least some users permanently reside outside the original context of systems analysis, design, and implementation. In this setting, it is often impossible to predict how potential users may conceptualize a domain and employing traditional conceptual modeling approaches is highly constraining. This paper reviews core assumptions of conceptual modeling research and evaluates their applicability to open information systems. We illustrate key issues in the context of online citizen science, which relies on open information systems to collect contributions of ordinary people for scientific research. Specific conceptual modeling challenges are identified. To address these challenges, we use theoretical foundations in philosophy and psychology to develop conceptual modeling principles to guide open information systems development. We conclude by outlining implications for practice and suggest directions for future conceptual modeling research. Keywords Conceptual modeling, Open information systems, Open information systems development, Information quality, Social Media, Crowdsourcing, User-generated content, Citizen science

INTRODUCTION User-driven development is a fundamental tenet of conceptual modeling. Users (or stakeholders) provide system requirements and constraints, supply analysts with subject matter expertise, and evaluate and/or approve conceptual models (Dobing and Parsons 2006; Gemino and Wand 2004). Maintaining close contact with users and project stakeholders is a commonly prescribed guideline (Gould and Lewis 1985; Moody 2005; Mylopoulos 1998). The idea of conceptual modeling without engaging users to generate and verify requirements seems incongruent with the basic principles and goals of systems analysis. Traditionally, ―lack of user input‖ is considered among the ―leading reasons for project failures‖ (Gemino and Wand 2004, p. 248). Indeed, one of the roles of conceptual models is to facilitate communication between users and analysts (Gemino and Wand 2005; Mylopoulos 1998; Parsons and Cole 2005). The availability and reliability of user input underlies the process of conceptual modeling. Consider, for example, the on-going effort to ground conceptual modeling grammars in appropriate theoretical foundations to facilitate user-analyst interaction and enhance users‘ ability to comprehend and verify conceptual models (Bodart et al. 2001; Burton-Jones and Weber 1999; Burton-Jones and Meso 2006; Burton-Jones and Meso 2008; Figl and Derntl 2011; Gemino and Wand 2005; Recker et al. 2011). While much research investigates nuances of user-model or user-designer interactions, scant attention has been paid to situations in which user input is unavailable and/or unreliable. We believe this kind of setting is becoming common as decision-makers increasingly turn to information produced outside organizational boundaries. Such information may be created by suppliers, business partners, potential customers, or virtual passers-by (Hand 2010; Kauffman et al. 2010; Zwass 2010). These external information producers may or may not comply with, or even be aware of, the needs of information consumers. For example, many companies turn to social media and crowdsourcing platforms to harness user-generated content (i.e, various forms of digital information produced by

members of the general public, rather than designated professionals, see Daugherty et al. 2008). Companies, such as Procter & Gamble, Starbucks, Dell, and American Express, nurture user-generated content by creating specialized platforms for user participation (Gallaugher and Ransbotham 2010; Gangi et al. 2010; Piskorski 2011), in part to monitor what potential customers are saying (Barwise and Meehan 2010; Culnan et al. 2010). Many governments provide digital outlets for citizens to participate in political process, report civic issues, or help with emergency management (Johnson and Sieber 2012; Majchrzak and More 2011; Sieber 2006). Scientists also actively seek contributions from ordinary people, and build for this purpose novel information systems that satisfy scientific goals by exploiting the enthusiasm and local knowledge of lay observers (citizen scientists). Citizen scientists participate in a diverse range of online projects, such as folding proteins, finding interstellar dust, classifying galaxies, deciphering ancient scripts, identifying species, and mapping the planet (Fortson et al. 2011; Goodchild 2007; Hand 2010). Citizen science promises to reduce research costs and has led to significant discoveries (Lintott et al. 2009). Increasingly a new breed of open information systems is emerging, systems with at least some users who are permanently located outside the context in which the systems are created and maintained1. Such systems (e.g., a citizen science project) support purposes internal to an organization (e.g., a research laboratory) by drawing upon external providers of information. From the point of view of a sponsor organization, the external environment represents an open information environment: actors and objects in this setting may not be well-understood and are largely beyond organizational control. For example, a scientist may know little about who the next contributor is (e.g., what are his/her beliefs or

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Open information systems need not span organizational boundaries, nor necessarily draw on outside information sources. For example, an interorganizational information system supporting a supply chain with well-specified shared standards (e.g., Kauffman et al. 2010) would be arguably less ―open‖ than an intraorganizational system in a highly diverse corporate environment. Indeed, as we argue later, open conceptual modeling can be used to foster grass-roots innovation from within.

level of expertise), what he/she will observe, or how he/she will perceive, interpret and reason about the observed phenomena2. While opening systems to external users offers many potential benefits, it also poses fundamental conceptual modeling challenges. In contrast with more traditional systems where information creation is assumed to be predictable and under organizational control, in open information systems there are typically no constraints on who can contribute information and engaging broad and diverse audiences is often desirable. As a result, some requirements may originate from system owners or sponsors, but the actual information comes from distributed heterogeneous users. Many such users lack domain expertise (e.g., bird taxonomy or product knowledge) and have views or conceptualizations of the subject matter that are incongruent with the views of project sponsors and other users (Erickson et al. 2012; Lukyanenko et al. 2011). Unable to reach every potential contributor, analysts cannot construct an accurate and complete representation of modeled domains. In this context, many questions arise, including: (how) can conceptual models faithfully represent diverse views and accommodate varying levels of expertise? (how) do conceptual modeling grammars need to be modified to better support open information systems? It is widely contended that poorly executed analysis leads to unnecessary costs and project failures (Wand and Weber 2002). Thus, addressing these questions can have a significant impact on information systems development. In this paper we attempt to answer some of these questions and suggest further research questions. We first review the main tenets of existing conceptual modeling research and evaluate them in the context of citizen science applications – open information systems that collect contributions of

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Alternatively, an organization can attempt to circumvent this problem by dealing only with external sources deemed sufficiently similar with respect to views, conceptualizations and level of expertise. For example, scientists may choose to attract experts (e.g., nichesourcing, expertsourcing) or train contributors to raise their expertise and promote a uniform domain understanding (de Boer et al. 2012; Dickinson et al. 2010). We do not consider these cases here; our primary interest lies in conceptual modeling in situations where there are few constraints on user participation and expression.

ordinary people for scientific research. Specific conceptual modeling challenges are then identified. Next, we draw on theoretical foundations in philosophy and psychology to develop conceptual modeling principles for open information environments. We conclude by outlining implications for research and practice and suggest directions for future conceptual modeling research. TRADITIONAL APPROACH TO CONCEPTUAL MODELING Traditionally, research in systems analysis was conducted under the closed-world assumption (Liddle and Embley 2007). Formalized as ―anything that is not known to be true is treated as false‖, closed-world modeling assumes that relevant information about a domain is known in advance. Moreover, this information typically comes from end-users, who are also subject-matter experts. Below, we outline several consequences of this assumption that are problematic for open information systems development. Direct involvement of subject-matter experts who are typically end-users. Traditionally, analysts rely on users (or, more generally, stakeholders) for subject-matter expertise and system requirements. The information is typically elicited though direct contact with end-users or their representatives (e.g., supervisors, team leaders). Analysts are thus freed from having to become domain experts and are mostly proscribed from imposing their own view of the domain (Kotiadis and Robinson 2008, p. 952). Similarly, research on conceptual modeling grammars assumes user views as given, however derived or ―impoverished‖ they may be (e.g., Wand and Weber 1995, p. 206). At the same time, cognitive models and biases of users have been investigated with the objective of increasing veracity of users‘ assumptions about domains (Appan and Browne 2010; Browne and Ramesh 2002). In summary, users mediate between enterprise reality and the modeling done by analysts (e.g., Figure 1). Given the centrality of users to information systems development, analysts are encouraged to be directly engaged with users. Gould and Lewis (1985), for example, stipulate ―bringing the design team into direct contact with potential users, as opposed to hearing or reading about them through

human intermediaries or through an ‗examination of user profiles‘‖ (p. 301, original emphasis). Indeed, an important role of conceptual models is facilitating mutual understanding and supporting user-analyst communications (Wand and Weber 2002).

Enterprise reality

Conceptual Model

PERCEPTION

User

InfoAnalyst

Figure 1. Traditional conceptual modeling based on Kim and March (1995) Unified global schema. A final conceptual model typically represents a global, integrated view of a domain but often does not represent the view of any individual user (Parsons 2003). Close contact with users provides an opportunity to resolve conflicts in individual views (Pohl 1994), leading to an agreed-upon conceptualization of a domain. The global schema then serves as ―the basis for understanding by all users and applications‖ (Roussopoulos and Karagiannis 2009). Representation by abstraction. The fundamental approach to conveying domain semantics in a unified conceptual model is representation by abstraction (Mylopoulos 1998; Smith and Smith 1977). Abstraction enables analysts to deliberately ignore the many individual differences among phenomena and represent only relevant information, where users determine what relevant was. Abstraction is foundational to conceptual modeling grammars. For example, a typical script made using the popular entity-relationship (ER) grammar may depict classes (which are similar to kinds, entity types, categories), attributes of classes (or properties) and relationships between classes. Classes (e.g., student, tree, chair) abstract from differences among instances (e.g., a particular student, or a specific chair), instead capturing perceived equivalence of instances. Indeed, many conceptual modeling grammars

consider instances (objects) to be members of their classes (entity types): ―[o]ne principle of conceptual modeling is that domain objects are instances of entity types‖ (Olivé 2007, p. 383). Abstraction-based modeling is deemed critical to ―organize the information base and guide its use, making it easier to update or search it‖ (Mylopoulos and Borgida 2006, p. 35). Accuracy and Completeness. With user-driven modeling and representation by abstraction, it is possible to completely represent all relevant domain semantics: ―a conceptual schema is the definition of the general domain knowledge that the information system needs to perform its functions; therefore, the conceptual schema must include all the required knowledge‖ (Olivé 2007, p. 29, emphasis added). The idea of accurate and complete specifications for intended use has been assumed in conceptual modeling since its inception (e.g., Parnas 1972). While the abovementioned tenets of conceptual modeling influenced mainstream thinking, their limitations and potential negative consequences have also been recognized. Wand and Wang (1996) note inherent limitations in capturing unanticipated information. The notion of ―complete and correct set of requirements‖ that ―sweeps away the multiple perspectives and ambiguities of organizational life‖ has been criticized by interpretive researchers (Walsham 1993, p. 29). The challenges of view integration have been explored (Parsons and Wand 2000; Parsons 2003). Parsons and Wand (2000) examined the negative consequences of inherent classification (a major form of abstraction). Reaching remote users, especially on the Internet has also been noted as a modeling challenge. Indeed, Wand and Weber (2002) attribute renewed interest in conceptual modeling in part to ―the challenges faced in eliciting requirements when user cohorts are large, diffuse, and unknown‖ (p. 364). Despite concerns, traditional assumptions prevail in conceptual modeling research. End-users are assumed to be readily available and differences between users are purportedly minimized in view of

common goals, training, standards, and shared organizational culture. However, these assumptions no longer hold as organizations look outward to augment in-house information production. CHALLENGES OF MODELING OPEN INFORMATION SYSTEMS To illustrate the incongruence between modeling for traditional and open information systems, we use the domain of citizen science, in which scientists seek contributions of ordinary people for research purposes (Louv et al. 2012; Silvertown 2009). Online citizen science epitomize open information systems – while systems are developed primarily to serve the needs of scientists (the subject matter experts), the users or contributors in these projects (i.e., citizen scientists) are ordinary people, often lacking subject matter expertise and possessing diverse domain views (Coleman et al. 2009; Snäll et al. 2011). Citizen science supports the idea of harnessing human ingenuity – the ―wisdom of crowds‖ (Surowiecki 2005). Imposing a particular view upon content creators may focus (or bias) contributors on one particular goal (e.g., species identification, classification of galaxies), but fail to capture additional useful information citizen scientists may wish to communicate. Furthermore, many citizen science projects approach data collection in a hypothesis-free manner (e.g., www.eBird.org), making explicit provisions for ad hoc querying and increasing the potential for ―unanticipated benefits‖ from such data (Wiersma 2010). Here, we present a case of conceptual modeling based on our experience in developing a natural history citizen science project. The objective of the project was to map biodiversity of a region in North America (a territory of over 150,000 square miles) using citizens‘ sightings of plants and animals. Since the project was sanctioned by scientists, requirements determination was initiated by interviewing biologists. A major question arose regarding the kind of information to be captured and the ways to structure user input. Given that in biology many activities require identification of organisms at genus or species levels (e.g., Salix spp., American robin), a decision was made to focus on this classification level. This accorded with similar natural history citizen science projects (e.g., www.eBird.org,

www.iSpot.org.uk), where the major aspect of user involvement is positive identification (i.e., classification) of genera or species3. Considering the diversity of potential contributors, a mixed convention of biological nomenclature and general knowledge was chosen. Figure 2 shows a partial conceptual model depicting generalization-specialization relationships among classes in the biology domain. Animals -eukaryotic -heterotrophic -can move

Birds -can fly -has feathers -has wings

Birds of prey

Shorebirds

-curved beak -curved talons

-lives around water

Mammals -mammalian glands

Land Mammals Marine Mammals -move on land

-can swim

Polar bear Osprey -eats fish -diurnal

-eats fish -can swim -lives in North

Figure 2. Hypothetical conceptual model in citizen science While the conceptual model in Figure 2 captures a common domain view (e.g., Kaufman and Peterson 1999; Peterson and Peterson 2002), it may not represent or even be congruent with the views of many prospective citizen scientists. Different potential contributors may organize (or abstract) perceived reality using alternative criteria. For example, some may group birds by sound or appearance. Depending upon the criteria chosen, issues of reconciling different taxonomies may arise. For example, polar bears may be considered by some non-experts as land, rather than marine mammals. Similarly,

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In other citizen science domains users may classify galaxies and other cosmic bodies (e.g., www.galaxyzoo.org), in volunteer geographic projects users identify geographic forms (e.g., www.openstreetmap.org); in business domains users may be classifying consumer products (e.g., using Amazon Mechanical Turk platform).

different users may advocate different, but valid, conceptualizations of the instances represented by classes in Figure 2. For example, when observing or recalling the same instance of an osprey, some users may classify it as bird, predator, fish hawk, or osprey. Each classification may reflect varying individual experiences and domain expertise (e.g., non-expert contributors may not be familiar with osprey and be more comfortable with the more general concept of bird). Traditional approaches to modeling do not provide sufficient guidance to the problem of reconciling extreme diversity of user views. In the next section we use theoretical foundations in philosophy and psychology to develop principles of open conceptual modeling that address the emergent challenges of citizen science and other open information environments. PRINCIPLES OF OPEN CONCEPTUAL MODELING The analysis of traditional conceptual modeling applied to the domain of citizen science reveals the deficiency of abstraction-based approaches to domain representation. Abstraction-based conceptual models depict stylized (Kaldor 1961, p. 178) - generalized and simplified - representations of actual complex user experiences and beliefs. For example, some subject matter experts in biology may specify the class bird, assume all instances of this class possess attributes has feathers, has wings, and can fly, and contend that other attributes are irrelevant (see Figure 2). They may further stipulate constraints on the data types for attributes and prescribe particular relationships for all bird instances (e.g., generalization relationships as in Figure 2). The result is an abstract representation that is a cognitive artifact designed to organize a domain in a meaningful and useful way (Mylopoulos 1998). Psychologically, abstraction is a mental mechanism essential for humans to survive in a diverse and changing world (Harnad 2005). Traditional conceptual modeling that utilizes abstraction-based representation, however, implicitly assumed that models elicited from users during systems analysis will be reasonably similar to those held by users during actual system use. In other words, users occupying a particular role will be sufficiently similar to other users in the same role for it to be reasonable to model

a generic view, rather than having to elicit and specify individual conceptual models. Open information environments such as citizen science, however, forcefully demonstrate that each contributor sees reality differently, as prior experience, domain expertise, conceptualization, and ad hoc utility, may result in different abstractions of the same domain between contributors and for the same contributor over time (McCloskey and Glucksberg 1978; Murphy 2004; Smith 2005). A conceptual model representing a domain as perceived by some users may bias or exclude possibly valuable conceptualizations of other users. The incongruence between a model of reality embedded in information systems and the one natural for a user may preclude the user from effectively engaging and contributing and can negatively impact the quality (e.g., accuracy, completeness) of usercreated information (Lukyanenko and Parsons 2011; Parsons et al. 2011).4 We argue the solution to favoring one model of a domain over others lies in representations that are, to the extent possible, concrete. We use fundamental theories of philosophy and psychology to derive principles of open conceptual modeling driven by concrete rather than abstraction-based representations. Instance-based Ontological Core To ground concrete representation in theory we turn to ontology, a branch of philosophy that studies what exists in reality (March and Allen 2012; Wand et al. 1995). We use the general ontology of Bunge (1977) motivated by its established record in conceptual modeling research.

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Indeed, once the abovementioned citizen science project was implemented, the analysis of contributions suggested users sometimes made guesses when classifying species and, potentially, avoided contributing when unable to satisfy the constraints placed by the underlying abstraction-based conceptual model. These conclusions about poor information quality concurred with concerns in scientific community about low quality of citizen contributions (Flanagin and Metzger 2008; Lukyanenko et al. 2011; Rowland 2012; Snäll et al. 2011; Wiersma 2010).

According to Bunge (1977), the world consists of ―things,‖ which can also be referred to as instances, objects, or entities. Instance is an elementary or fundamental ontological construct. Every instance is unique in some way. The uniqueness of an instance is based on the distinctiveness of its properties. Properties are always attached to things and cannot exist without them: the materiality of properties directly derives from materiality of things. Properties can be intrinsic if they are inherent in things (e.g., height or mass) or mutual if they belong to more than one thing (e.g., date hired and salary are joint properties of a person and a company). Instance primacy has support in psychology: according to research in perception and attention, instance awareness (e.g., spatiotemporal permanence) is a fundamental mental mechanism. Organisms experience a continuous sensory input, but the final representation is typically more discrete, rather than continuous (Harnad 1990). Research in perception and attention shows that attention tends to be ―allocated to individual objects that are traced through time and space‖ (Carey 2009, p. 70; see also Scholl 2002). Humans then appear to perceive sensory fields (e.g., visual space) to be made of discriminable objects and an undifferentiated perceptual background (Carey 2009; Kahneman 1992). Much of (re)classification and (re)identification happens after some existence or spatiotemporal continuity is established. The privileged status of objects in information processing thereby attests to its salience in the ‗perceived world‘. This means that instances should also be ‗natural‘ to model, comprehend and verify when interacting with conceptual models. This leads to: Principle: Instance should be the primary construct of an open-world conceptual model; modeled independent of any other construct. Properties, Attributes, and Change According to Bunge (1977), people are unable to observe properties directly, and perceive them instead, as attributes. Several attributes can potentially refer to the same property. The existence of an attribute does not imply that a property exists (e.g., the attribute name does not refer to an underlying

property). While material things exist independent of an observer, individual observers may consider different attributes of things at different points in time. Indeed, attributes are basic abstractions of reality insofar as any attribute (e.g., color red, roughness of texture, height of a building) is a generalization formed by compressing diverse sensorimotor input (or memory) into a mentally stable coherent element5. Attributes are fundamental building blocks of representation to the extent that they can be used to identify instances and form higher-level abstractions (e.g., things with similar attributes can be grouped in classes). Consequently, we propose: Principle: Attributes can be attached to an instance to describe its properties. By using attributes, open conceptual models can represent change. Instances do not exist in isolation: based on Bunge (1977), instances interact with each other and these interactions produce mutual properties. Many types of interactions can occur between any two instances. For example, a supervisor can assign work to an employee or recommend a particular employee for promotion, and even report to the same employee at a later point in time. Each interaction is time-sensitive and may produce different mutual properties. Instances may also compose and emergent properties appear. For example, consciousness may be a key emergent property of individual neurons that make up human brain (Trefil 1998). Hence, complex systems are often distinguished by their emergent properties. Interactions between instances, including external (to an information system) agents, such as users, produce change. Following Bunge (1977) and consistent with prior research (e.g., Parsons 1996) we propose to model change in terms of change of attributes over time. Principle: Instances can interact with other instances and properties (mutual and emergent) arise from the interactions.

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When considering visual modality, with every input interruption or environment change, such as movement of eyes (saccades) or of the object of interest, the focal object (stationary, or moving) is sensed differently by the retina, but operational constancy and equivalence of attributes, such as shape, color, length, texture, size are seamlessly maintained.

Considering the above principles, open conceptual models strive to depict a particular instance and its attributes and interactions as perceived by a particular user at a given moment in time. For example, when observing a flock of birds, a user may note their similarities, but depict one or two birds in more detail. Indeed, each representation of the same instance may be unique (including representations by the same user at different times). The constancy of instances, however, connects different representations together. Higher-level Abstractions Despite flexibility and ontological fidelity, the proposed principles leave out a number of important aspects of human experience. Conceptual models of concrete instances and attributes (low level abstractions) are ineffective in capturing the full scope of a domain. Using specific instances and attributes allows depicting a sample of perceived reality, but can never provide a complete view. Moreover, instances and attributes alone may be unintuitive to use in communication. It is unnatural for users to refer to an instance x in terms of its attributes alone. It is more likely that users intuitively refer to x using some class (e.g., bird, osprey, shorebird, predator, object in the sky). Finally, higher-level abstractions (e.g., existing classes) known to an organism may work as filters through which humans consider attributes of instances in the world, biasing perception (Harnad 2005; Harnad 1990). Employing higher-level abstractions in a way that does not undermine domain representation and understanding can thus complement instance-based principles. We thus propose to use the class construct. We use cognition to establish the role of classes in open conceptual modeling. People (and other animals) use classes to group instances they deem equivalent in some way (see Fodor 1998; Murphy 2004; Smith and Medin 1981). This allows humans to abstract from differences among instances, thereby gaining cognitive economy and ability to infer unobserved properties of things (Parsons and Wand 2008; Rosch 1978). A collection of classes resulting from this process is a higher form of abstraction (Schyns et al. 1998). Classes are derived constructs: they do not have real-world

existence (Bunge 1977; Panaccio 2005). As was discussed earlier, any two observers may fail to share the same class definition (e.g., instances a class refers to and attributes it contains). In the absence of all users, however, predicting how a particular user may understand a given class is challenging: ―[c]lassifications that appear natural, eloquent, and homogeneous within a given human context appear forced and heterogeneous outside of that context‖ (Bowker and Star 2000, p. 131). While agreement among all users on every class in open information environments is infeasible, cognitive research points to a set of classes, termed basic-level categories, for which agreement among users should be particularly high. Typical basic-level categories are common words, such as bird, tree, fish, cup, chair, and house (see Table 1 for more examples from the natural history domain). These categories tend to be in the middle of taxonomies (e.g., a dog is taxonomically between a specific dog and an animal) and provide what is often considered an optimal combination of economy and inference (Corter and Gluck 1992; Rosch et al. 1976). By the economy principle, an organism tends to minimize storage and processing effort, thus favoring higher-level categories (e.g., animal) that account for more reality; however, the more encompassing a category is, the less useful it is for predicting particular features or behavior of its members. This causes organisms to seek more specific categories (e.g., German shepherd dog). In some sense, the basic level provides a trade-off between the two objectives (Corter and Gluck 1992). Table 1. Natural history basic-level categories considered in psychology research Categories Bird, dog Bear, rhino, pig, seal, bug, cat, turtle, crab, dog, fish, elephant, rabbit, horse, lizard, hippo, duck, snake, frog Horse, rhino, lizard, pig, hippo, bug, duck, turtle, snake, dog, frog, elephant Dog, duck, cat Mouse, fish, butterfly, bird, rabbit, beetle, dolphin, horse, dog, tree, monkey, chicken Apple, pear, orange, lime, coconut, pineapple, carrot, peas, corn, pepper, pumpkin, avocado, bird, dog Bird, dog, fish Horse, spider, chicken, fish, dog Cat, dog, horse, bird, bat Bush, tree, flower

Source Tanaka and Taylor (1991) Waxman and Klibanoff (2000) Klibanoff and Waxman (2000) Rhemtulla and Hall (2009) Op de Beeck and Wagemans (2001) Jolicoeur et al. (1984) Johnson and Mervis (1997) Mandler and Bauer (1988) Younger and Fearing (2000) Murphy and Wisniewski (1989)

Categories Cat, dog, horse, cow, apple, pear, daffodil, sunflower Bird, flower, tree

Source Bowers and Jones (2008) Barr and Caplan (1987)

Basic-level categories enjoy a privileged status in human information processing. Cognitive research offers strong empirical evidence of the universality and robustness of basic-level categories (Rosch et al. 1976). Thus, Jolicoeur et al. (1984) considers the basic-level to be the typical mental entry point - the first class people think of when they encounter an object. Children tend to learn basic-level categories ahead of other categories. Adults use them most frequently in daily speech. Basic-level categories encompass many features and account for a large variation of perceived reality, which appears to reflect perceptual discontinuities of reality. Two neighboring basic-level categories (i.e., categories that share the same parent in a hierarchy) have many relevant differences, while members of the same basic-level category have much in common (Rosch et al. 1976). Table 2 lists several widely held propositions about basic-level categories in cognitive literature. These propositions can also offer concrete guidance to analysts in discovering basic-level categories in a domain. Table 2. Principles of basic-level categorization Proposition Basic-level categories tend to be somewhere in the middle of taxonomic hierarchy Basic-level categories are the most natural labels for objects; adults use them more often in daily speech Objects in basic-level categories share common shapes Most inclusive level at which objects look alike and for which one mental image can reflect entire category; most inclusive level at which objects can be visualized Typically the first thing people think about when they encounter an object Within category similarity should be maximal, and between – minimal for a basic-level category Basic-level labels tend to be short and morphologically simple

Source from Reference Discipline Rosch et al. (1976); Tanaka and Taylor (1991); Corter and Gluck (1992) Rosch et al. (1976); Cruse (1977); Wisniewski and Murphy (1989) Rosch et al. (1976); Mervis and Rosch (1981); Tanaka and Taylor (1991) Rosch et al. (1976); Tanaka and Taylor (1991); Rhemtulla and Hall (2009) Jolicoeur et al. (1984); Murphy and Brownell (1985); Rosch et al. (1976) Rosch et al. (1976); Markman and Wisniewski (1997); Murphy and Brownell (1985) Dirven and Verspoor (2004); Tversky and Hemenway (1983); Markman and Wisniewski (1997); Murphy and Brownell (1985)

Basic-level categories can be used in open conceptual models to set instances in an intuitive context. Since using these categories is usually appropriate for users of varying levels of domain expertise, they can provide the benefits of abstraction-driven models, including communication, representation and scope, while to a degree avoiding the negative aspects of abstraction-based modeling. Instances in open conceptual models can be referenced using the basic category label and attributes. Thus, a basic-level category becomes an instance pointer: it helps to narrow down an instance of what kind out of many possible is being described using attributes. This may be critical as people (including experts) are notoriously poor at articulating attributes necessary and sufficient to establish memberships even for common classes (Medin 1998). More than one basic-level category can be attached to an instance: this is frequently the case in domains, such as geography, where they tend to form pairs (e.g., mountain - hill, see Mark et al. 1999). Principle: Basic-level categories can be attached to instances as labels used to represent the scope of a domain. DISCUSSION Open Conceptual Models as Tools of Representation and Communication Instance-based modeling enables representation of variable user input. In contrast to representation using higher-level abstractions, instances and attributes are not constrained by a stylized, generalized model (e.g., by forcing an instance to be a member of a class or to have predetermined interactions with other instances). For example, given an instance of an osprey, subject matter experts in biology may indicate attributes of interest (e.g., eats fish, white breast, black talons). Some of these attributes may be invisible and be inferred based on prior domain knowledge (e.g., eats fish). Non-expert users may contribute similar or different (presumably more observable) attributes of the same instance (e.g., white breast, sharp claws, brown wings).

Instance-based modeling enables meaningful communication between users with different levels of domain expertise. An instance becomes a reference for all users: both scientific experts and citizen scientists can reasonably comment on attributes supplied by others. Since attributes usually carry lowerlevel information than more complex abstract models (which include many attributes), conflict or misunderstanding regarding an attribute has less significant negative consequences for communication and representation. Furthermore, conflicting attributes supplied by different users for the same instance (e.g., white breast and grey breast) may convey useful information, as they inform stakeholders of inherent variability and fuzziness of information in a domain (and be an early warning sign of challenges and opportunities in data management). Basic-level categories enable a holistic view of a domain. Identifying basic-level categories can efficiently convey the scope of relevant information in a modeled domain (e.g., birds, nests and trees vs. stars and planets). This can inform stakeholders of the range of instances users are likely to report and how they are likely to think about them. Basic-level categories provide instances with an intuitive generalized context and permit users with different levels of expertise to carry out a meaningful dialog. In addition to common basic-level categories, communities of practice, and people in general develop their own specialized vocabulary and set of typical categories (e.g., species in biology, dog breeds for breeders). Some research considers equating these classes to the ―bona fide” basic-level categories (see Tanaka and Taylor 1991). While these classes may be unfamiliar to all users of open information systems, retaining them may be important for some focal user groups. These classes may be optionally attached to instances as secondary labels (perhaps, hidden from average observers). Introducing higher-level abstractions into open conceptual models can have a number of negative consequences, including impact on information quality and user engagement. This concern motivates the open modeling principle of demoting classes to labels, proscribing making a priori assumptions about instance attributes, and emphasizing basic-level categories. One additional measure that analysts can

take is to facilitate unlabeling during elicitation sessions with users, by which users are asked to try to abandon labels when describing instances, thereby ―recentering‖ perception (McKim 1980). Future research can propose and evaluate other approaches to increasing the link between representation and underlying reality. Open Conceptual Modeling and Open Information Systems Development Open conceptual modeling principles suggest a need for significant changes to the traditional systems development process. As shown in Figure 3, individual user views are no longer integrated by an analyst into a global schema (as in, for example, Figure 1), but rather attributes are reported directly by users in an analyst-moderated process (see Figure 3). Note the proliferation of double arrows in Figure 3: these signify a reciprocal impact of the model, users, analysts and the perceived reality. In particular, the double perception arrow captures concepts related to categorical perception, where existing mental models ―color‖ perceived reality; the double representation arrow stands for the impact of media on cognition (and perception). CONCEPTUAL MODEL

Reality User 1

Can fly Chirps

Bird 1 Red beak Grey head

User 2

Sits on

Long cones Tree, bush Has needles

User 3

FLEXIBLE DATA STORAGE AND USER INTERFACE

Analyst

Figure 3. Open conceptual modeling process Open conceptual modeling necessarily calls for flexible approaches to data storage and programming logic. Indeed, once the conceptual modeling phase is complete, the final product

(conceptual model) serves as guidance, more than a strict template to be transformed into a logical data model. This approach is consistent with instance-based database design (Parsons and Wand 2000). Instance-based approaches recognize the primacy of instances and attributes over classes and allow instances to be stored independent of classification. This enables seamless storage of heterogeneous information, and is a suitable data structure for open information systems. IMPLICATIONS FOR RESEARCH AND PRACTICE With the growing importance of external information sources and open information systems, a pressing question is how to carry out conceptual modeling in this environment. Predominantly ―closedworld‖ assumptions of traditional conceptual models are severely limited in handling the diversity and uncertainty of the new environment. One consequence of modeling open information environment using a closed-world modeling paradigm is decreased information quality, conceptualized as the ability to faithfully convey perceived reality by information producers. To address the emerging challenges we derived a set of conceptual modeling principles designed to guide open information systems development. We believe our conceptualizations carry substantial implications for research and practice. The developed principles of open conceptual modeling should facilitate organizational efforts to collect information outside organizational boundaries. This is a cost-effective and efficient model of information production, but realizing the fuller potential of external information creation remains difficult in part due to fundamental misalignment between prevailing approaches to modeling and the realities of open domains. The principles of open conceptual modeling should help organizations to connect with suppliers, business partners, potential customers or general public, as the commonly assumed prerequisite of a shared standard is now considerably relaxed. This makes the proposed principles also applicable to internal information production. Since universal attributions of meaning are not generally possible for all members of organizations (Checkland and Holwell 1998, p. 221), using the proposed principles enables capturing individual views, while maintaining focus on a common core of

information. Some potential benefits of open conceptual modeling used internally include grassroots sense making, increased organizational agility and member (employee) empowerment. We offer a theoretical rationale for evaluating existing and developing new conceptual modeling grammars and methods. With growing societal importance of social media, user-generated content and other innovative forms of information production, calls are increasing for developing more flexibility in conceptual modeling (Chen 2006; e.g., Liddle and Embley 2007). For example, a promising approach is ―schema during‖, in which users are free to alter the data model (Roussopoulos and Karagiannis 2009). Another suggestion is to consider folksonomies or collaboratively-created domain-specific ontologies (Braun et al. 2007; Sugumaran and Storey 2002). With the above principles we provide theoretical rationale and guidance for evaluating and improving constructs of existing grammars and directions for emerging technological solutions. The proposed principles contribute to the development of broader conceptual modeling research. The modeling community has largely adopted the notion of representation by abstraction (Mylopoulos 1998; Mylopoulos and Borgida 2006). We identified inherent limitations of abstraction-based conceptual models in representing reality, particularly in open information environments. We therefore introduced instance-based representation, arguably more appropriate for the emergent challenges and demands. At the same time, higher-level abstractions are deemed useful in providing a bird‘s eye view of a domain, especially using basic-level categories. Another challenge is the nature of higher-level abstractions, such as classes. Cognitive psychology recognizes four major models of classification: classical, prototype, exemplar and ―theory theory‖ approaches, all with multiple off-springs (see Murphy 2004). While debates continue, one conclusion is different models may be better suited to different situations (Hahn and Ramscar 2001). In contrast, conceptual and data modeling usually support one (typically, classical) model of classification. Here, we proposed a novel approach to modeling classes – representation of basic-level categories as

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