Semantics In Kepler

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Intended audience

This document is intended for SEEK and Kepler developers. It is a DRAFT DESIGN DOCUMENT and does not reflect functionality as it currently exists in Kepler or SEEK. Comments and feedback are appreciated.

Use of Semantics in Kepler

We intend to further develop the use of semantic mediation technologies within the Kepler scientific workflow environment. Semantic tools will be used for both discovery and integration of actors and data within the Kepler system. This document outlines some of the semantic functions, ontology issues, and GUI features for each of the major areas of functionality:

Data and Actor Discovery
Data and Actor Classification and Organization
Data and Actor Transformation and Integration
Semantic Workflow Design

Our proposed Kepler semantics demonstration paper submitted to SSDBM 2005 also describes some of this material: see Incorporating Semantics in Scientific Workflow Authoring.

Data and Actor Discovery

Overview

We intend to use ontology-driven search capabilities to improve discovery capabilities for both data and actors that have been annotated with terms from formal ontologies. For both actors and data, there are two levels of annotation that we will make use of. The first level of annotation addresses the general topic of the data or function of the actor. We can call this style of annotation the "topical ontology annotation". The second level of annotation describes the semantic "signature" of the data or actor. (By signature we mean a data-structure definition, e.g., a database schema.) We can call this style of annotation the "signature ontology annotation". In the case of data, signature annotations describe the semantic type of each attribute within the data and (possibly) how the attributes relate to one another. For actors, signature annotations describe the semantic type of each input and output port and (possibly) how the ports relate to one another. The topical annotation is meant to describe generally what something represents or does, while the signature annotation describes how such semantic information is encoding (i.e., realized) by the data or actor. In some cases, the signature annotation may be used to "infer" the topical annotation.

Envisioned GUI

We envision several mechanisms for discovering relevant data and actors via their semantic annotations within Kepler. First, in the left-hand list of data and actors, we envision users being able to search for terms from an ontology and have all related results display in the list of results. For example, one might search for data about "biodiveristy" and see a data set that contains abundance of reptile species at a site. Or one might search for "SpeciesDistributionModels" and see the GARP model. In this scenario, we need to decide how the user chooses terms from the ontology for searching. Currently, we simply allow them to type in a text string that is then compared to the ontology terms, and if there is a match, those ontology terms are used in the search. We may want to allow users to be more precise, e.g., by providing an advanced search interface or through an additional interactive step that suggests possible ontology concept expressions given the original text string.

Second, we would like scientists to be able to discover semantically compatible actors and data while composing a workflow. This would involve two possible GUI mechanisms. First, there could be a toggle that allowed the user to only display semantically compatible actors and data in the left-hand pane. When toggled, the system would impose a constraint in which each potential actor is screened against the current workflow to see if it could be added in any semantically compatible way based on the semantic type of its I/O signature. When the workflow canvas is blank, all actors and data would appear in the list, but as actors and data are added to the canvas, this imposes constraints that reduces the number of actors that are displayed. Second, the user should be able to select any combination of one or more actors that is currently on the canvas and right click to "Show compatible actors". This effectively launches a semantic query against the semantic I/O signature of the selected actors and displays compatible actors in the left-hand pane. In addition, a user may want to further restrict their search by showing both semantically and structurally compatible actors.

Ontology issues

There is some question as to what exactly is required in terms of ontologies for the topical ontology annotations and the signature ontology annotations. There may be one overall ontology, in which both style of annotations draw terms from, or it may be convenient to have separate ontologies for each style. For example, the ontology for topical annotations may be inherently more general, and would represent one or more classification axes that allowed users to label the actors and data with relevant terms. For example, an actor that calculates the Shannon-Weiner index might be labeled as a "BiodiversityIndex" and a model that generates spatially explicit maps of species distributions might be labeled a "SpatialSpeciesDistributionModel" (not that that would be an explicit term, but that would be the meaning of the annotation). The signature ontology would specifically be used to label the semantic types of data that are contained within a data set or that flow between two or more actors. Thus, this ontology would contain terms that are concretely tied to real-world measurements as represented by data. For example, the ontological annotation for a particular column in a biodiversity data set might be "PsychotriaLimonensisArealDensity" (it would also have a structural type describing units, etc). A particular "BiodiversityIndex" actor might take as input data with the type "SpeciesArealDensity", and the ontology would allow us to deduce that the Psychotria limonensis data is semantically compatible with the actor's input requirement.

Data and Actor Classification and Organization

Overview

When composing workflows scientists can create new composite actors that wrap up other actors into a functional unit that performs a typical task. They can also create new atomic actors that are introduced into Kepler using the KSW file format

that is being designed. We want scientists to be able to label these new actors with the proper terms from the topical and signature ontologies so that they can be saved and then discovered later using the semantic techniques outlined above. When annotating these new actors, the scientist may have a need for new terms in the ontology to properly classify their new actor. These could be topical terms or new semantic data types that are not yet represented. In addition, the ontology may not capture any particular scientists views and knowledge of the domain area accurately, so they may wish to reorganize the ontology to better reflect their semantic worldview. In both of these cases the Kepler GUI needs to accomodate modification of the ontologies.

Data that are created by executing a workflow will be saved locally and possibly remotely and will need to be semantically labeled as well to maximize its utility. We hope that part of the semantic annotation may be propagated through the workflow automatically, but in the absence of this advanced feature the scientist would need to be able to annotate the derived data with its appropriate semantic type. This operation is closely related to annotating the ports of a new actor.

Envisioned GUI

How exactly this would work is an open topic. In the current Kepler GUI the topical ontology is represented as a tree control on the left side of the window. Each category shows up in the tree in all of its parent categories, and actors that have been annotated show up i the tree node for each ontology temr that applies. Thus, actors may show up in one, two, or more parts of the tree. To annotate a new actor, we envision being able to drag a composite actor from the canvas and drop it onto the ontology category in the tree. It could then be dragged again to add it to another category as well. This operation adds the new actor to the subsumption hierarchy at the appropriate place(s). If the ontology ends up representing more than just subsumption, we will need an GUI ability to create these additional relationships. One of our concerns is that the folder view that currently is shown in the tree conveys a much more informal representation of the ontology than is appropriate. So, we have considered changing the icons from folders to another 'container' icon that might reduce some implications of a folder. Strictly speaking, one physical item can not be in more than one folder at a time, so the folder metaphor isn't really appropriate for our case. The tree itself isn't really accurate as the ontology is a graph, so we could and should consider whether we want to use a more appropriate representation of the ontology in the left pane. Of course, switching to a graph representation could be very confusing and so any benefits in accuracy of the visualization would need to be weighed against the cost of using a less familiar and more complicated representation.

An alternative approach might be to right click on an actor and be shown a dilaog for classifying the actor. This dialog would both show the ontology and show the actor and its ports and allow the components of the actor to be annotated with the terms from the ontology.

Another alternative proposed by B. Ludaescher is to allow the user to enter Sparrow expressions in a text box for describing actor annotations. This is simpler to implement but has significant usability questions, partly because it assumes that the user has detailed knowledge of the terms and relationships between terms inthe ontology.

In many ways, annotating the semantic tpye of the ports (the actor's I/O signature) involves a different set of operations in the GUI than annotating what the actor is 'about' in terms of its function and algorithm.

Data and Actor Transformation and Integration

Overview

Whether or not two actors or data sets are semantically compatible is an important property that we want to expose to users. For any given workflow, we want them to be able to check the semantic correctness of the workflow (semantic type checking). This allows them to see the parts of the workflow that might satisfy structural constraints (output and input both are array of floating point numbers) but that are semantically meaningless (e.g., the output is an array of rainfall values in Oregon, and the model takes as input an array of carbon soil concentrations).

A related aspect is being able to construct a transformation path to get between two compatible actors if they can't be linked. The semantic type checker might be able to determine that two actors are incompatible for direct linking but might be compatibel with some series of transformations that might be applied to the data stream. For a trivial example, if a data set contains species counts for individual species over a particular area and an actor requires as input the number of species present in an area, a simple transformation can be applied (filter where count > 0) to allow the data to be used. Thus, the data can be used after a simple transformation. Of course, realistic transformations would probably require multiple steps, so the challenge here is to generate the appropiate composite workflow that takes the semantic type of the original data set as input and outputs the sematic type of the original actor.

An equivalent process can be applied when aggregating/integrating data such that multiple semantic and structural types incoming can result in one outgoing structural and semantic type.

Envisioned GUI

Again, this is an open question, but a few thoughts have been proposed.

For semantic type checking, it has been proposed that a buttonbe placed on the Kepler UI that can invoke the type checking algorithm, which would then color code components based on their compatibility. For example, in a workflow A - B - C - D, the link from B to C might be semantically invalid and it could be highlighted. If the operation is not computatinally complex (ie, time consuming), the type check operation could be run in the background and the results highlighted on the canvas dynamically as changes are made to the workflow (akin to the red squiggly line in word processors indicating a spelling error). This would eliminate the need for a separate button and give the user immediate feedback about their workflow composition.

For the transformations, we have discussed putting two actors on screen that are semantically compatible through a transformation and selecting a context menu item 'Generate transformation step'. This algorithm would generate a composite actor with the appropriate semantic types and create the workflow needed to handle the semantic transformation. In doing this, the semantic mediation system would need to know the correspondence between various types of semantic incompatibilities and how particular actors can be used in resolving those incompatibilities. For simple cases this seems tractable (e.g., convert density to occurence), but for more complex cases its an open question as to whether it is tractable -- this would essentially be an automated program generator.

Semantic Workflow Design

Overview

Semantic workflow design takes the semantic type checking and transformation discussion to a greater level of sophistication. Here we envision a system where a user can create workflows as an 'abstract model' which is then specified at successively more concrete levels. For example, a user might want a simple workflow that takes 'SpeciesDistribution' data, connects it to a 'SpeciesDistributionPredictionModel', which creates a 'PredictedSpeciesDistributionMap'. This high-level, abstract workflow is not in itself executable, but it constrains the world of possibilites. The user must then drill down to provide more concrete (although still possibly not executable) specifications for each of the abstract components. At some level of concreteness the semantic mediation system should be able to recognize specific executable actors that could be used to realize the abstract model. These could be suggested to the workflow designer or automatically inserted in cases where the use is unambiguously correct.

The same underlying capabilities that are used in the data transformation and integration would be used to deduce the compatibility between abstract components and to suggest concrete realizations of the abstract components.

This model of a semantically-driven, abstract modeling approach is closely aligned to Villa's Integrated Modeling system, and we should carefully examine that system for compatibilities and synergies.

Envisioned GUI

The GUI in Kepler is already quite amenable to this approach in that it supports hierarchy in the models now. Right now composite actors are a form of abstraction that could be the basis for abstract models. The GUI changes needed would involve being able to drag abstract components from the ontology onto the workflow and visually indicate that these portions of the workflow are abstract and need to be further specified. All of the semantic type checking and tranformation tools described above could be utilized. The semantic type checker should be able to determine if the model has been specified to a sufficiently concrete level to be executed, and that would in turn trigger a visual indication on the canvas or in the GUI that the model was ready to be executed. For example, the director icon (if such a thing were present) might change shape or color, or the 'Run' button in the toolbar might switch from deactivated to actived with corresponding visual changes.

The challenge here is largely in constraining the abstract components to terms from the ontology that would represent 'useful' components and managing the multiple levels of abstract hierarchy that might be present.

Comments on the Draft

Please add any comments to the draft that you think would improve the exposition and we will incorporate the ideas into the design.

Extract from email by Sergui Krivov

Coming back to the issues of "folder tree" versus some other interface to ontologies- for deciding this issue it is important to consider what information from ontology is relevant during this annotation process. Let's go down to matrixes and imagine the process:

User has to select a concept X to annotate a port of an actor A. For that he/she has to look at ready made ontology which is already in Kepler and make a selection. What he/she has to know about a concept in order to select or not select it? What "variables" related to concept are relevant?

Is position of concept in class hierarchy important?
Is possible multiple inheritance important?
Do roles (properties and properties restriction) come into picture? Does user need to know them to make a selection?
Does user need to see the set of instances of a given concept?
Does user have to see the other actors (besides actor A)during selection process?
Does user have to see the semantic type of other actors?

Answers to questions 5 and 6 would dictate if concept selector work as dialog or as a pane. The answer to questions 1-4 would dictate the type of ontology browser/selector. If only #1 has positive answer then tree control is OK. While advocating to use something as GrOWL in capacity of "concept selection dialog" (concept selection panel or buffer) I presumed that answers to the questions 1, 2, 3 are positive. If they are not positive then my suggestion is void. If they are positive then what could be another viable alternative to GrOWL as concept selector? (May be it is hypertext based Sparrow rendering of assertions pertained to certain concept where user could click on a concept and get all Sparrow statement related to it assembled in one place. Perhaps it would be easy to make such dynamic generator of sparrow statement based on Jena or GrOWL model. I will certainly include one into GrOWL in a near future.)

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This particular version was published on 02-Mar-2005 21:15:51 PST by SDSC.bowers.