Benoît Lemaire

Benoit Lemaire

Anne Guérin-Dugué

Thierry Baccino
email: thierry.baccino@univ-paris8.fr

Myriam Chanceaux
email: myriam.chanceaux@univ-provence.fr

Léa Pasqualotti
email: pasqualotti@lutin-userlab.fr

A cognitive computational model of eye movements investigating visual strategies on textual material

Keywords: Computational model, Eye movements, Visual strategy, Text

This article presents a computational model of the visual strategies involved in processing textual material. An experiment is presented in which participants performed different tasks on a multi-paragraph page (searching a target word, searching the most relevant paragraph according to a goal, memorizing paragraphs). The proposed model predicts eye movements based on 5 parameters. The weighting of parameters is determined for each task by means of a multidimensional comparison of participant and artificial scanpaths.

Introduction

Reading a text is a complex task which has been widely studied in cognitive science. Several models have been proposed to account for the peculiarities of human eye movements and especially the sequence of fixations and saccades that can be nowadays easily observed and recorded. For instance, EZ-Reader [START_REF] Reichle | The E-Z Reader model of eye-movement control in reading: Comparisons to other models[END_REF] proposes a detailed model of how low-level processes such as oculomotor control, attention, visual processing and word identification combine to produce a relevant scanpath. In addition to a theoretical framework, EZ-Reader offers a computational model which can be run on a specific text.

Those models are models of reading. A typical reading scanpath is a sequence of short forward saccades followed by a long backward saccade going to the beginning of the next line, then short forward saccades, etc. until the end of the text. Not all words are fixated and there can be short regressive saccades (up to 20% of all fixations) but the general shape looks like that. However, texts can be processed in different ways: when you are searching information on a web page, not all the words of all the lines are processed. Sometimes, a specific word tells you that the current sentence is probably not relevant and you jump a few lines. You can also quickly choose to abandon the current paragraph and move to another one.

Another way to process a text is to search for a particular word. The scanpath then looks even more different: only some words are fixated in a very fast browsing of the text.

However, if you read to learn the text, you will show short forward saccades as usual, but also a high proportion of regressive saccades, even moving to previous lines, in order to make sure that information is correctly stored in memory. [START_REF] Simola | Using hidden Markov model to uncover processing states from eye movements in information search tasks[END_REF] showed that different tasks on textual material produce different kind of scanpaths. [START_REF] Carver | Reading rate: A review of research and theory[END_REF] distinguished five kinds of processes (visual strategies), based on variations of reading rates:

• Scanning is performed at 600 words/min and is used when readers are looking for a particular word; • Skimming is used when readers need to get a quick overview of the content of the text (450 words/min.); • Rauding is normal reading (300 words/min.); • Learning is performed at 200 words/min. It is used when readers try to acquire knowledge from the text; • Memorizing is used when readers want to memorize the text, therefore constantly verifying that information have been memorized (138 words/min.). These processes differ in reading rates, but also in the length of saccades, fixation durations and number of regressions.

The aim of the present study was to design a cognitive computational model of eye movement that would account for all these strategies. The idea is to base this model on a very small number of parameters that can generate this variety of scanpaths, when appropriately tuned. The first purpose is to know the contribution of each of these variables in the production of the scanpath. For example, the spatial distance to the next fixation (saccade amplitude) is a key variable in rauding (words that are spatially close are much more likely to be selected than distant words) whereas it is not as important in scanning.

The second goal is to produce a general model of eye movements on texts which could easily adapt to high-level changes. For instance, a user may be looking for some information, first engaging in a skimming task, then switching to a learning process for a while, then moving to a scanning process because a specific word that occurred previously has to be reread in context. Our claim is that these processes are along a continuum. It is therefore interesting to model this behavior in a continuous way.

In order to build the model, we first gathered experimental data on different ways of processing a text.

Experiment Procedure

An experiment in which participants would generate various kinds of scanpaths was designed. Three tasks were defined:

• Searching for a particular word in the page. This task is likely to generate scanning scanpaths.

• Searching among a set of paragraphs the one which best matches a given goal. For instance, if the goal is "planet observation", the participant has to select the paragraph which is about that topic, although the paragraph may not contain those words: search has to be done based on semantics. In order to obtain rich scanpaths, several paragraphs may correspond to the goal; participants have to select the closest one. This task is likely to generate skimming scanpaths.

• Reading paragraphs in order to be able to answer comprehension questions afterwards. This task is likely to generate memorizing scanpaths.

Only 3 of the 5 processes defined by Carver were used, but, as we show later, the proposed model is not limited to them.

Materials

20 pages were generated in French. Each page was associated with a specific goal (for the skimming task).

Examples of goals were tribunal international (international tribunal), réhabilitation des logements (housing renovation), associations humanitaires (humanitarian associations), etc. One target word per page was defined for the scanning task. Seven paragraphs were produced for each page. In order to control the semantic relatedness of paragraphs to goals, Latent Semantic Analysis [START_REF] Landauer | Handbook of Latent Semantic Analysis[END_REF] was used, a method to compute semantic similarities between texts.

LSA was trained on a 24 million word French corpus composed of all articles published in the newspaper "Le Monde" in 1999. A 300 dimension space was generated from the corpus, by means of a singular value decomposition of the word x paragraph occurrence matrix (see. [START_REF] Martin | Mathematical foundations behind Latent Semantic Analysis[END_REF] for more details). Each word of the corpus being represented as a 300 dimension vector, new texts can also be represented as vector by means of a simple sum of their words. A cosine function was used to compute the similarity between vectors. The higher the cosine value, the more similar the two texts are.

From the seven paragraphs designed, two were highly related with the goal (cosine with the goal above .40), two were moderately related (cosine between .15 and .30) and three were unrelated (cosine below .10). In order to have a more realistic situation, an image was also included in the page as well as a banner. Figure 1 presents an example of a page. All paragraphs were organized into the page according to a layout that was randomly selected. There were eight versions of each page, in order to ensure that paragraphs are not processed in the same order.

Because the exact coordinates of words were needed for simulations, all pages were generated by a piece of software of our own which generates the image file and the word coordinates. The font was BitstreamVeraSans 12pt.

Participants

13 participants were recruited in the scanning condition, 8 in the memorizing condition, 34 in the skimming condition. All participants saw the 20 pages in random order. All scanpaths were recorded using a SR Research Eyelink 2 eyetracker. The images were presented on a 19 inch CRT monitor at a viewing distance of 50 cm.

Model

The main issue of the current model was to select which word to fixate next among all words in the paragraph, using a limited number of variables. That problem can be viewed as an iteration of two steps: weighting all words and selecting the best weighted one.

There are two ways for a variable to weight words: either by increasing the weight values of words likely to be fixated or by decreasing the weight values of words that will probably not be fixated. Some variables thus aim at selecting interesting words, other decrease the weight value of uninteresting words. In order to present the variables used, let us describe how the different processes operate. Each process will correspond to a specific combination of these variables1 .

Scanning

Scanning is the fastest strategy. The aim being to find a particular word (the target), it is likely that users tend to prefer words which match with that target. Since almost all words can only be viewed in peripheral vision, the weighting can only be done on similarity of shape by a kind of pattern-matching process. Shape similarity with the target is therefore the first variable. This variable will probably not be used by the other processes which do not rely on a target word. In addition, it is likely that the scanning process shows longer saccades compared to rauding. The hypothesis is that the closer the process is to classical reading, the shorter the saccades. Distance to the current fixation is therefore our second variable: words spatially close to the current fixation will be preferred. Scanning is a process which will probably not need a high weight to that variable, as opposed to rauding for example.

Skimming

Skimming differs from scanning in that it takes the content into account. However, not all words need to be fixated in order to keep a high processing speed. For the same reason as before, the decision to select a word or not can only be done under peripheral vision. Although the general shape of a word is certainly not related to its meaning, it is likely that users tend to prefer long words which are known to be more meaningful. Word length is our third variable. It is possible that others processes rely on that variable, but probably to a lesser extent than the skimming process.

Rauding

Rauding is normal reading. Almost all words have to be fixated. Therefore, the linear sequence of words becomes important in order to preserve the meaning of sentences. Saccades towards the next word tend to be the rule. These saccades are therefore mostly horizontal (including the long saccade going to the beginning of the next line). Saccade horizontality is therefore our fourth variable: it would give higher weights to words reachable with an horizontal saccade. Scanning would probably give a low weight to that variable because saccades may jump from one line to another. Instead, the number of intervening words between the previously fixated word and the current fixated word could have been used as a variable. That value would be close to 0 in rauding, larger than 0 but positive in skimming and sometimes negative in memorizing. However, that variable would not have captured the fact that in 2D fixating a distant word in the text may result in a short saccade.

Learning

Learning falls in-between rauding and memorizing. This process is slower than rauding because of longer fixation durations, and more regressive saccades. As in skimming, word length should play a role. However, almost all the words should be fixated, saccade horizontality should also been involved.

Memorizing

Memorizing is the slowest way of reading. Almost all words have to be fixated but, as opposed to rauding, they might be fixated more than once (rauding may also involve regressive saccades on the previous word but we are here talking about long regressive saccades). On the other hand, there is almost no fixation on previously fixated words in the other processes. Therefore, the fifth variable is called newness. This variable prefers words that have not been fixated previously. The memorizing process is therefore likely to give a low weight to that variable in order to select words that were seen before. Other processes will probably give higher weight to that variable.

To sum up, the model assigns a weight to all words of the text and moves to the one with the highest weight. Given the current fixation C, the weight w(Wi) of a word Wi depends on the following parameters. shapeSim(Wi): the visual similarity between Wi and a target word, if any. This similarity between words should not be based on the identity of letters (not processed in peripheral visual field) but rather on the identity of shapes. Therefore, each word corresponds to a string in which each letter is represented by a character denoting its class (b=lowercase ascender letter, g=lowercase descender letter, a=lowercase normal letter, A=uppercase letter). For instance, the word Psychology is represented as Aagababagg. Similarity of shapes is performed by computing the [START_REF] Levenshtein | Binary codes capable of correcting deletions, insertions and reversals[END_REF] distance between these strings. For instance, the distance between Psychology and Intrepidity (Aabaagababg) is 4 because four operations are needed to transform one string into the other (3 substitutions and 1 insertion). This distance is normalized for the longest string. Shape similarity is one minus that distance. dist(Wi): the spatial distance between C and Wi, normalized for the length of the paragraph diagonal (longest saccade ever). length(Wi): the number of characters of Wi, normalized for the longest word in the paragraph; hor(Wi): the horizontality of  CW i defined as the angle bet- ween an horizontal line and the vector, normalized for /2; newness(Wi): a binary variable which is 0 in case Wi has already been fixated and 1 otherwise.

The general formula is: w(Wi)=wS.shapeSim(Wi) + wD.dist(Wi) + wL.length(Wi) + wH.hor(Wi) + wN.newness(Wi)

The model is not deterministic because in case two words are equally weighted, a random choice is performed.

Examples of scanpaths

Some examples of scanpaths using different weights are described in Figure 2. The model shows the upper scanpath with wS=10, wD=10, wL=10, wH=100, wN=10. It looks like a scanning scanpath. However, with wS=0, wD=10, wL=0, wH=200, wN=100, the second scanpath is really different and seems to mimic a memorizing scanpath.

Comparison to human data

In order to estimate relevant variable weights wS, wD, wL, wH, wN for each process, simulations were run. For each combination of parameters, one artificial scanpath was generated for each participant scanpath. Then each of these pairs of scanpaths were compared. Averaging the result of these comparisons for each combination of parameters gives an overall measure of the adequacy of that version of the model to the human behavior.

Comparison of scanpaths

Comparing scanpaths cannot be done at the level of fixations. Even two humans do not produce identical scanpaths. Higher level comparisons should be performed. The Levenshtein distance (also called string edit distance) is the most common way of comparing scanpaths [START_REF] Privitera | Algorithms for defining visual Regions-of-Interest: Comparison with eye fixations[END_REF]. Each scanpath is encoded as a string of letters in which each letter corresponds to the area of interest (AOI) that each fixation hits. Then the Levenshtein distance between two scanpaths is the number of insertions, deletions or substitutions that are necessary to go from one string to the other. In our case, this method cannot be used as it is: considering each word as an AOI would be inappropriate because it would not consider the spatial relationship between words (on the same line for example).

An interesting method was recently proposed by [START_REF] Jarodzka | A vector-based multidimensional scanpath similarity measure[END_REF]. Each scanpath is viewed as a sequence of geometric vectors. Each vector corresponds to a saccade in the scanpath. Then a scanpath with n fixations is represented by a set of n-1 vectors. The two sequences that has to be compared are aligned according to their shapes (although the authors note that alignment can be performed on other dimensions): it means that to each vector of scanpath #1 corresponds one or more vectors of scanpath #2, such that the path in the matrix of similarity between vectors going from (1,1) (similarity between first vectors) to (n,m) (similarity between last vectors) is the shortest one. Once the scanpaths are aligned, various measures of similarity between vectors (or sequences of vectors) can be used: average difference in amplitude, average distance between fixations, average difference in duration, etc.

For example, Figure 3a shows the scanpath from participant #13 (first saccade is going upward). The model outputs the scanpath of Figure 3b for a particular combination of variables weights (first fixation is on the first word).

The alignment procedure attempts to match the six vectors (for the six consecutive saccades) of the participant scanpath with the four vectors of the model scanpath. According to Jarodzka's method, the best match is the following: 1-2/1 ; 3/2 ; 4/3 ; 5-6/4 (saccades 1 and 2 of participant scanpath are aligned with saccade 1 of model scanpath, saccade 3 is aligned with saccade 2, etc.).

Once scanpaths are aligned, similarity measures are computed for each alignment. Instead of using Jarodzka's measures of similarity between aligned sequences of saccades which are not fully relevant to the study, the following measures of distance were used:

• the spatial distance between saccades (computed as the distance in pixels between midpoints of each saccades and normalized for the paragraph diagonal). Similar scanpaths should have aligned saccades located in similar regions of the screen. • the angle between saccades (computed as the normalized cosine between saccades). Similar saccades should have aligned saccades in similar directions. • the difference of amplitude between saccades (computed as the normalized difference of saccades lengths). Similar scanpaths should have aligned saccades of similar lengths. On the previous example, the results are: distance between saccades = 0.20; angle between saccades = 0.14; amplitude ratio = 0.38 (AVERAGE = 0.24).

It means that the model with these parameters is quite bad at reproducing the amplitude of saccades. It is however better at reproducing the position of the scanpath and above all the angle between saccades.

With another combination of parameters, another example of artificial scanpath is generated (Figure 3c, the first fixation is on the first word and the third saccade is a regressive saccade).

Alignement with the participant scanpath is now the following: 1/1 ; 2-3/2 ; 4-5/3 ; 6/4-5-6-7-8. Comparison results are: distance between saccades = 0.30; angle between saccades = 0.31; amplitude ratio = 0.39 (AVERAGE = 0.33). That model is about the same as the previous one predicting saccade amplitudes. However, it is much worse with respect to the shape of the scanpath as well as its position.

Parameter adjustment

In order to estimate the appropriate value of parameters wS, wD, wL, wH, wN for the scanning, skimming and memorizing tasks, the average distance between all participant scanpaths and corresponding model scanpaths for each combination of parameters was computed. Actually, only the values of four parameters out of 5 are needed since relative values instead of absolute values are considered. For instance what is relevant is to know that in the scanning condition wL should be 2 or 3 times higher than wD. Therefore, one parameter was set to an arbitrary value and the other values that all together produce scanpaths that are similar to human scanpaths were searched. wN, the memory parameter, that we know cannot have a null value, was set to 100.

In all cases, the first fixation occurred on the first word of the paragraph.

Memorizing condition

In the memorizing condition, there is no target so the wS parameter is not relevant. After several exploratory simulations on different ranges of values and on a subpart of the data, the following integer values were more carefully tested for the remaining parameters: wD ∈[0,9], wL ∈[0,5], wH ∈[0,1000].

For each of the 1120 participant scanpaths, the generation of the corresponding artificial scanpath was stopped when it reached the same number of fixations. The minimum average distance between model and participants was found for the following values: wS=0, wD=1, wL=0, wH=700, wN=100 It means that moving horizontally is the most important thing. Not going back to previously visited words is important but not that much. The length of words has no effect at all. Making short saccades is not very important.

In order to better understand the effects of variables, Figure 4 presents the average distance between scanpaths as a function of horizontality and distance to the current fixation.

Although minimizing the distance to the current fixation does not seem to play much role, results are much worse when the weight of that variable is set to 0. In the same way, the worst results are found when the horizontality weight is set to 0. Fit to human data increases until about 500 and then becomes about the same.

Scanning condition

The same procedure was performed with the data coming from the scanning task. Parameter wN was also set to 100. The entire procedure was longer to perform because there is one more dimension to take into account. Each simulation was stopped when the target word was found or when the number of fixations was the same as the number of fixations performed by the participant.

The minimum average distance between model and participants was found for the following values: wS=3, wD=6, wL=3, wH=15, wN=100

The pattern is completely different from the memorizing task. Horizontality is much less important. As expected, similarity of shape plays a role which appears as important as the length of words (although these variables may be dependent from each other since the targets were not short words). Distance to the current fixation also plays a significant role: saccades should not be too long. Like previously, Figure 5 presents the average distance between scanpaths as a function of horizontality and distance to the current fixation. It shows that results are much worse if the distance weight is set to 0 (meaning that keeping short distance to the current fixation is important) and also if horizontality is close to 0. Even in the scanning task, horizontality of saccades plays a role, but not as much as in the memorizing condition.

Skimming condition

The same procedure was performed with the data coming from the skimming task, whose objective was to make a decision about the semantic relatedness of the paragraph to a goal.

The minimum average distance between model and participants was found for the following values: wS=0, wD=90, wL=40, wH=900, wN=100

Although horizontality of saccades plays a major role, fixating long words is important. This is probably because long words contain more semantic information than short words, which is essential in this semantic task. Distance to the current fixation is even more important. This semantic task requires a comprehension of the text, which often requires reading linearly some group of words, by means of short saccades going from one word to the next one.

Conclusion

This paper presents a model of eye movements on textual material which was applied to 3 different ways of processing a paragraph: searching for a specific word (scanning), assessing the semantic relatedness of that paragraph to a goal (skimming) or memorizing the paragraph. Five parameters were adjusted which showed that:

• length of words plays an important role in the skimming task (40), a reduced role in the scanning task (3) and no role at all in the memorizing task;

• minimizing the distance to the current fixation is crucial in skimming (90), not so important in scanning (6) and slightly necessary in memorizing (1); • horizontality of saccades is very important in memorizing (700) and skimming (900) but not much in scanning (15); • visual similarity of word shape is only necessary in scanning (3).

The effects of some variables in different tasks were described and, more important, we provided a model that is able to reproduce the shape of a human scanpath given the task. The next step is to supplement this model with a semantic component. One goal would be to model the way users navigate in a web page [START_REF] Chanceaux | A model to simulate Web users' eye movements[END_REF].

The process of searching in the space of parameters was done in a brute force way. Optimization techniques, and especially evolutionary algorithms, to improve that process are under investigation.

Figure 1 :

 1 Figure 1: Example of page used in the experiments.

Figure 2 :

 2 Figure 2: Two artificial scanpaths with parameters 10/10/10/100/10 and 0/10/0/200/100.

Figure 4 :

 4 Figure 4: Average distance between human and models scanpaths as a function of distance and horizontality in the memorizing task.

Figure 3 :

 3 Figure 3: (a) A participant scanpath. (b)(c): Two artificial scanpaths.

It is important not to confuse variable weighting with word weighting. To sum up, a given process (scanning, rauding, etc.) assigns predefined weights to variables. Then each word is given a weight by simply combining the values given by all variables.

Acknowledgments

This work was funded by the French National Research Agency (ANR) under the project GAZE&EEG.


Member, IEEE Jungsik S Kim
email: jskim@kaist.ac.kr

Dongjune J Chang
email: dongjune.chang@kaist.ac.kr

Hamid Ladjal
email: hamid.ladjal@ensi-bourges.fr

David Folio
email: david.folio@ensi-bourges.fr

Member, IEEE Antoine Ferreira
email: antoine.ferreira@ensi-bourges.fr

Evaluation of Telerobotic Shared Control for Efficient Manipulation of Single Cells in Microinjection

Microinjection is the high delivery efficient method of exogenous materials into cells, and it has been widely used in biomedical research areas such as transgenics and genomics. However, this direct injection task is timeconsuming and laborious followed by low throughput and poor reproducibility. This paper describes telerobotic shared control (TSC) framework for the microinjection with high manipulation efficiencies, in which a micromanipulator is controlled by the shared motion commands of both the human operator (teleoperation) and the autonomous controller. To determine the optimal gains between the teleoperation and automation modules, we propose the quantitative evaluation method using Fitts' and steering law. The results showed that about 60% weighting on human operator had better performance for both speed and accuracy of task completion, and suggested the some level of automation or human involvement will be important for the microinjection tasks.

I. INTRODUCTION

H IGH efficient transfer of foreign materials into cells is important for fundamental research of cell and molecular biology and biomedicine. Several methods have been developed for the successful delivery of exogenous materials into cells. Among them, single cell microinjection is to directly introduce foreign materials, such as DNA, proteins, sperms, and drugs, into individual cells [START_REF] Luo | Synthetic DNA delivery systems[END_REF]- [START_REF] Zhang | Microinjection as a tool of mechanical delivery[END_REF], and it is relatively high efficient delivery method than electrical [START_REF] Bestman | In vivo singlecell electroporation for transfer of DNA and macromolecules[END_REF], viral [START_REF] Luo | Synthetic DNA delivery systems[END_REF], chemical [START_REF] Luo | Synthetic DNA delivery systems[END_REF], and other transfer methods [START_REF] Tirlapur | Targeted transfection by femtosecond laser[END_REF], [START_REF] Kim | Ultrasound-Mediated Transfection of Mammalian Cells[END_REF]. Microinjection is used in many different biotechnologies such as cell transfection with DNA in mammalian cells [START_REF] Luo | Synthetic DNA delivery systems[END_REF], induced pluripotent stem (iPS) cell transfer [START_REF] Zhao | Production of mice using iPS cells and tetraploid complementation[END_REF], and in vitro fertilization (IVF) with intracytoplasmic sperm injection (ICSI) [START_REF] Kimura | Intracytoplasmic Sperm Injection in the Mouse[END_REF]. However, although microinjection has high delivery efficiencies, the injection task is time-consuming and labor-intensive work which limit the manipulation of large numbers of single cells [START_REF] Luo | Synthetic DNA delivery systems[END_REF], [START_REF] Stephens | The many ways to cross the plasma membrane[END_REF]. For the elaborate manipulations of delicate and small cells, it demands technical requirements, such as development of micro-motion stages and fabrication of micro pipettes with a fine tip. In addition, the great manipulation skills of a human operator are required, who need extensive training to perform the injection tasks, and thus microinjection has low manipulation efficiencies followed by low throughput and poor reproducibility.

The manipulation of micro meter-sized cells is interrupted by limited capability for sensing technologies. The manipulation is performed with insufficient visual information with small field-of-view and narrow depth-of-field, therefore, it is difficult for detection and positioning of cells or glass pipettes. In addition, there is no force/tactile feedback for robot control or haptic interaction. Insensate glass pipettes induce damage itself or of cells.

Several single cell microinjection systems have been proposed to improve the manipulation efficiency. Automated microinjection systems have been developed to remove the human involvement in injection process [START_REF] Sun | Biological Cell Injection Using an Autonomous MicroRobotic System[END_REF]- [START_REF] Lu | A micromanipulation system with dynamic force-feedback for automatic batch microinjection[END_REF], where a visual servoing approach is usually used to control the position and force of a micro-manipulator, but it is challenging to create a fully automated manipulation system because microinjection is conducted in diverse and complex conditions such as various cell sizes (from a micro-meter to hundreds of micro-meter) and cell types (floating or attached cells). In addition, they have difficulties in dexterous manipulation of cells with multiple degrees of freedom (DOF) and in target selection (cell nucleus or cytoplasm) in visual servoing. Teleoperated microinejction systems have been developed to provide haptic feedback [START_REF] Kim | Cellular Force Measurement for Force Reflected Biomanipulation[END_REF]- [START_REF] Kim | A Haptic Interaction Method Using Visual Information and Physically Based Modeling[END_REF]. However, most of them have provided force sensing and feedback with a small number of DOF.

As the manipulator control standpoint, this paper presents a telerobotic shared control (TSC) framework for single cell microinjection for high manipulation efficiency. To achieve high throughput and dexterity simultaneously, both automatic and teleoperative functions of system are needed in microinjection. In the shared control approach, a human operator can control manipulator as much as possible while a controller retain cells and glass pipettes in a desired manipulation path or space to satisfy adequate performance. In the remainder of this paper, first, the TSC algorithm is presented to describe the sharing method of the teleoperation-and automationmodule for the microinjection system. In this paper, we focused on providing the quantitative analysis to determine what level of automation (or teleoperation) is better for optimal TSC. Most previous studies for shared control have lack of studies to decide how much the autonomy will be integrated in the teleoperation (or vice versa) for the best performance. Although the shared control has been applied to various applications [START_REF] Ammi | Involving the Operator in the Control Strategy for Intelligent Telemicromanipulation[END_REF]- [START_REF] Bertocchi | Human-Robot Shared Control for Robot-Assisted Endoscopy of the Spinal Cord[END_REF], this work is, to the authors' knowledge, the first application of shared control in cellular micro-manipulation and first providing a evaluation method to determine optimal shared control gains.

II. METHOD

Shared control is applied to the microinjection system to compensate the lack of sensing ability in the micro environment, especially for insufficient visual information. First, a planner for the shared control calibrates the coordinates of the micro manipulator and cells, and defines a guidance path to constraint the depth directional motion of the micro manipulator and for the fast approach of the micro manipulator to cells.

A. System Setup

A biomicromanipulation workstation is designed for single cell microinjection, as shown in Fig. 1(a). In this paper, zebrafish embryos were used as manipulated cells (Fit. 2, [START_REF] Kim | A Haptic Interaction Method Using Visual Information and Physically Based Modeling[END_REF]). The operator control the master robot (SensAble Technologies, PHANToM Premium 1.0, USA) to input the motion command for the micromanipulator in the slave space. The slave consists in a micromanipulator (PI, F-131, Germany) with injection needle and the other micromanipulator (Sutter, MP225, USA) with a holding glass pipette. The micromanipulator has three translational motion in X, Y and Z direction. The vision system includes a CCD camera (SVS-Vistek, SVS340MUCP, Germany with 640×480 pixels resolution and maximum of 250 fps) with microscopic lens (Moritex, MML2-ST65D) to capture images for the cells and the motion of the slave tip.

B. Planner

Visual information is a crucial feedback to enable the micro-manipulation tasks. Processing visual data determines the focal plane for depth estimation and the guidance path of the end effector in the image frame. In this study, because the input of manipulator is given in the slave frame {S}, the mapping between {S} and the image frame {I} forms a critical component for servoing the slave tip. In order to compute this mapping, a calibration method is developed and implemented. Several calibration methods exist in the literature which are mostly used in macro scale vision applications [START_REF] Zhang | A flexible new technique for camera calibration[END_REF]. Unfortunately, those methods cannot directly be applied to calibrate an optical microscope due to the characteristics of the optical system (e.g., high optical magnifications and very narrow depth of field), and some dedicated methods have been then proposed [START_REF] Ammi | Flexible Microscope Calibration using Virtual Pattern for 3-D Telemicromanipulation[END_REF]. In this work, the camera calibration parameters are defined in the focal plane (image space); therefore, the non-sharp tip image, due to the depth directional motion of the robot, is ignored. For an orthographic camera projection model, the tip position S p = (X,Y, Z) T in the slave frame can be related to its position I p in image space as follow:

I p = ( u v ) = ( Y /S u + u 0 X/S v + v 0 ) (1)
where S u , S v are the pixel dimensions (µm/px), and the initial tip position I p 0 = (u 0 , v 0 ) T is measured by the template matching method in the focal plane [START_REF] Kim | A Haptic Interaction Method Using Visual Information and Physically Based Modeling[END_REF].

When assuming the accurately position-controlled slave manipulator and the orthographic projection model, it is important initially to locate the slave tip in the focal plane. In this paper, the precise placement of the tip along the Zdepth is achieved by the comparison of the pixel gradient magnitudes at the tip-end because the tip is angled and has pure translational motion. The gradient magnitude is at a maximum along the center line in u of the tip image when the tip-end is in the focal plane (Fig. 3).

The position of a cell in the image space is extracted by using Hough transform as the center point of the detected circle, and the focusing of the non-angled holding pipette with a cell was implemented based on the template matching method [START_REF] Sun | Biological Cell Injection Using an Autonomous MicroRobotic System[END_REF]. Finally, the guidance path is determined as the linear path from the cell position to the injection starting position near the cell membrane, as shown in Fig. 4.

C. Shared Control

The overall control architecture of the TSC system is described in Fig 5 . The TSC has two levels. The lower level consists of the teleopertion control and the autonomous control based on aritifical potential field (APF). The higher level is the shared controller to integrate both modules. The total input command S u for the micromanipulator is defined by the weighted summing of motion commands from the operator and the autonomous controller:

S u = K h u h + K c u c ( 2
)
where

K h + K c = 1 and 0 ≤ K h , K c ≤ 1.
In this paper, the input from the human operator is simply the scaled input command from the master device by

u h = α p M p. (3)
APF-based control algorithm [START_REF] Khatib | Real-time obstacle avoidance for manipulators and mobile robots[END_REF] is applied for autonomous manipulation module, in which the manipulator is considered as a particle and controlled under a force field by attractive and repulsive potential functions. Attractive potential field is constructed to attract the manipulator to the guidance path, and repulsive potential field is generated around a cell membrane to prevent the micro pipette tip from passing the membrane not in injection direction. The total potential field is defined by the sum of both potential fields, and each function is defined as,

U total (p) = U att (p) +U rep (p) U att (p) = 1 2 K a |p d -p| 2 U rep (p) = 1 2 K r |p o -p| 2 , for |p o -p| ≤ r ( 4
)
where p d = nearest point from the tip to the guidance path p o = nearest point from the tip position to the membrane K a , K r = constant gains r = radius of influence Fig. 6 shows 2D potential map in the image space. For the net force f p under the potential field, finally, the input command from the APF algorithm can be defined as follows,

u c = c f f p = c f (-∇ p U total ) (5)
where c f is compliance constant for modifying input command.

D. Fitts' and Steering Law

In the TSC, the input commands from the teleoperation module and the autonomous module to the shared controller have a gain (weighting factor) K h and K c , respectively. These gains play a role in deciding which module will be weighted more in the TSC. High human operator weight can lead to that the microinjection system acts as conventional teleoperation system. On the other hand, when more weight is given to automation module, the human operator can lose the control of the micromanipulator.

In this paper, to quantitatively evaluate the effect of weighting level of each module on the TSC and to obtain the optimal gains, we performed 2D pointing and steering tasks based on Fitts' law and steering law. Fitts' and steering law were first developed to quantify the speed and accuracy tradeoff in target-directed movements [START_REF] Fitts | The information capacity of the human motor system in controlling the amplitude of movement[END_REF], [START_REF] Accot | Beyond Fitts's Law: Models for Trajectory-Based HCI Tasks[END_REF]. They have been applied in various applications, such as human-computer interaction and robotics [START_REF] Cannon | Experiments With a Target-Threshold Control Theory Model for Deriving Fitts' Law Parameters for Human-Machine Systems[END_REF]- [START_REF] Choi | Development and Quantitative Performance Evaluation of a Noninvasive EMG Computer Interface[END_REF].

In the Fitts' law, the Movement Time (MT ) in seconds to select a target of width W and at distance A is given by,

MT = a + b log 2 ( A W + c) (6)
where a and b are empirical parameters determined by linear regression. c is 0, 0.5 or 1 (we choose as 1 according to [START_REF] Mackenzie | A note on the information-theoretic basis for Fitts' law[END_REF]). The term log 2 ( A W + c) is referred to Index of Difficulty (ID) in bits which represents the difficulties of the tasks.

In a typical Fitts' law, the trajectory between the initial and final position is ignored. Therefore, if the movement is constrained along a predefined path, steering law well predicts the MT with path length A and width W [START_REF] Accot | Beyond Fitts's Law: Models for Trajectory-Based HCI Tasks[END_REF]:

MT = a + b A W . ( 7
)
Although the shared control between human and robot has been widely studied and various applications for Fitts' law has been presented, the quantitative analysis for choosing ideal weighting in the shared control using Fitts' law has not been studied. In this paper, we present how much weights are ideal for shared control using Fitts' and steering law tests. In the proposed TSC for microinjection, a micro pipette guiding task to a cell along the guidance path with the depth directional motion compensation can be seen as steering law task, and a target selection from the guidance path can be Fitts' law task. We propose the model combining Fitts' law and steering law, in which we hypothesized that total index of difficulty ID t is obtained by

MT = a + bID t ID t = ID f + ID s ( 8
)
where ID f and ID s are defined in Eq. 6 and Eq. 7, respectively, and ID t is dimensionless number.

III. EXPERIMENTAL RESULTS

A. Protocol

Six subjects (6 males and the age 24.5±2.3 years) participated in the experimental task. All subjects were right handed (dominant hand for all subjects) and inexperienced in microinjection and teleoperation system. Fig. 7 shows the task window for the proposed model Eq. 8.

The steering task was performed first for the tunnel (box 1 in Fig. 7). The subjects were asked to guide a white plus sign (the micro pipette tip position) from the right side to the left side of the tunnel without crossing the up or down side of the tunnel. After passing the tunnel, they had to tap inside box 2 by clicking the button on the stylus of the haptic device. The subjects were asked to perform the task as quickly as possible. If the subjects fail to perform the task, the white plus sign changes to the red plus sign, and the trial is restarted with initializing the timer. A completion time was recorded in seconds from the time entering the tunnel to the time tapping box 2, and the number of errors was also recorded.

Four different gains (K h = {0.2, 0.4, 0.6, 0.8}) and fifteen total indices of difficulty (Table. I) were selected. The subjects were not informed about the shared control idea. They performed 4 sets of trials and each set consisted of 60 trials (15 ID t × 4 gains) at random order. The first set was training set to practice the task, and the data from other 3 sets were collected.

B. Results

First, for the performance comparison of different gains, throughput (T P, [START_REF] Soukoreff | Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts law research in HCI[END_REF]) was calculated by T P = 1 n ∑ ID ti /MT i (n is the total number of trials) for each subject. Fig. 8 shows TP as the subjects performance (S1∼S6) and Table . II represents the total TP for all subjects. The subjects showed the better performance at K h = 0.6 (except S1 and S2; they were at K h = 0.4), and when K h = 0.2, none of the subjects performed better because of the strong constraints of the movements on the guidance path. For the weak constraints (K h = 0.8), the subjects were easy to stray from the guidance path but the performance is better than K h = 0.2. That might be because of the learning effect due to the one-dimensional experimental task. Total TP showed a tendency with higher performance between 0.4 and 0.6 of K h although there was no significant difference which can be from the different performances between the subjects. Fig. 9 shows the total number of errors for all trials. More weighting on the human operator, more errors were presented for all subjects, which is that we expected since, for the weak constrains, the accuracy can be lower than higher constraints.

The experimental results are shown in Table . III and Fig. 10 with the linear regression equations. The proposed model (Eq. 8) showed the r 2 value of more than 0.766, for which the model can be used to measure the performance. The model with 0.4 and 0.6 of K h presented better fitting than the other values. This might be from that the skills of the subjects exerted a low influence on the performance of those values. That is, it could be said that the performance of the low skilled human operator can be improved with the TSC.

IV. CONCLUSIONS AND FUTURE WORKS

In the context of the single cell microinjection system, we proposed the TSC to achieve high throughput and dexterity simultaneously. The TSC can compensate the lack of sensing ability by providing guidance path for the fast approaching to cells and the assistance with depth directional motions. As an engineering test, the quantitative evaluation of the TSC with the different weighting was performed using Fitts' and steering law. The results showed that the 60% weighting on human operator had better performance for both speed and accuracy of task completion, and suggested the some level of automation or human involvement will be important for the microinjection tasks.

The design of the optimal control weights on a human operator and a robot is the challenging issue for the shared control. The weighting can be different depending on the application of the shared control idea, for which the proposed method can be used to quantitatively evaluate the performance.

Currently, we are performing the further microinjection experiments with the biological samples (zebrafish embryos [START_REF] Kim | A Haptic Interaction Method Using Visual Information and Physically Based Modeling[END_REF] and mouse oocytes) to evaluate the TSC system and the clinical evaluation is ongoing. Finally, in our future work, the semi-autonomous microinjection system will be developed with the integration of the TSC and robotic or microfluidic conveying of cells for high throughput and dexterity.

Fig. 1 (

 1 b) shows the coordinates of the system considering pure translation. The task space of the master robot is represented by the coordinate frame {M : (x, y, z)} ∈ ℜ 3 , the task space of the micro manipulator is represented by the coordinate frame {S : (X,Y, Z)} ∈ ℜ 3 , and the image space of the fixed camera is defined by the image coordinate frame {I : (u, v)} ∈ ℜ 2 .

Fig. 1 .

 1 Fig. 1. (a) Experimental system setup and (b) coordinate frames for master space {M} with haptic device, slave space {S} with micromanipulator, and image space {I}. p is position vector and γ is the injection angle of a slave tip in the X direction.

Fig. 2 .

 2 Fig. 2. Cell manipulation process for zebrafish embryo; (a) initial position of the slave tip, (b) starting position of the tip, (c) puncturing chorion (membrane) and approaching to the york.

Fig. 3 .

 3 Fig. 3. Maximum pixel gradient magnitudes along the center line of the tip image for different Z-depths.

Fig. 4 .Fig. 5 .

 45 Fig. 4. (a) Captured image of a cell (zebrafish embryo). I p t is the cell position in the york of zebrafish embryo, and green circle represents the edge detection of the york by Hough transform. (b) Path of the slave robot tip in Y Z plane. Subscripts 0 and s represent the initial point and the injection starting point, respectively. Guidance path is defined from S p s to S p t .

Fig. 6 .

 6 Fig.6. Total potential field map in the image space. High potential is represented as red color.

Fig. 7 .

 7 Fig. 7. The Fitts' and steering law task window for the proposed model. A plus sign represents the micro pipette tip position. Two boxes are displayed; box 1 is the tunnel for the steering law task and box 2 is the target for the Fitts' law task. Both tasks have same width but different amplitude.

Fig. 8 .

 8 Fig. 8. TP for six subjects from S1 to S6.

TABLE I TOTAL

 I

		INDEX OF DIFFICULTY
	W (px)	A s (px) A f (px)	ID t
	40	50	90	2.95
	30	50	95	3.73
	40	100	80	4.08
	20	50	80	4.82
	40	200	70	6.46
	30	150	80	6.87
	20	100	70	7.17
	10	50	40	7.32
	30	180	90	8.00
	30	200	90	8.67
	20	150	90	9.96
	20	180	80	11.32
	10	90	90	12.32
	10	100	70	13.00
	10	120	50	14.58

TABLE II

 II

				TOTAL TP FOR TSC GAINS
		K h (K c )	0.2(0.8)	0.4(0.6)	0.6(0.4)	0.8(0.2)
		TP		4.05±1.32 5.39±1.47 5.53±1.02 5.31±0.99
		12	S1
			S2
		10	S3 S4
	Total # of errors	6 8	S5 S6
		4
		2
		0
				0.2	0.4	0.6	0.8
					K h
		Fig. 9. Total number of errors of the subjects for all trials.

  Fig. 10. Relationship between the actual and predicted values (from equations in Table. III) for K h = 0.2 (open circle), 0.4 (open square), 0.6 (open triangle), and 0.8 (cross). Dashed line represents unit slope for ideal model

		4
				Kh=0.2
		3. 5		Kh=0.4
				Kh=0.6
		3		Kh=0.8
	Actual MT (sec)	2 2. 5
		1. 5
		1
		0. 5 0. 5	1	1. 5	2	2. 5	3	3. 5	4
					Predicted MT (sec)
									TABLE III
									LINEAR REGRESSIONS FOR TSC GAINS
									K h (K c )	Model	r 2
									0.2(0.8) MT = 1.084 + 0.139 ID t	0.766
									0.4(0.6) MT = 0.355 + 0.165 ID t	0.918
									0.6(0.4) MT = 0.139 + 0.187 ID t	0.907
									0.8(0.2) MT = 0.260 + 0.190 ID t	0.870

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2010-0022871).


Nouha Omrane

Adeline Nazarenko

Peter Rosina

Sylvie Szulman

Christoph Westphal

Lexicalized ontology for a business rules management platform: An automotive use case

Keywords: Business rules, domain ontology, semantic annotation

This paper describes a platform that helps industrial domain experts to preserve the connection between textual sources and formalized business rules by using lexicalized ontologies both for links and for storage of the conceptual knowledge. Business Rules Management Systems (BRMSs) are used to update and query business rules of an automotive use case. They rely strongly on domain ontologies, which model the business knowledge and provide a conceptual vocabulary for the formalization of the rules that are expressed in written policies. We show that lexicalized ontologies are a key component of such BRMSs and how such knowledge can be encoded. Our proposed solution supports domain experts in the automotive industry in understanding and maintaining their business rules by presenting the relevant source documents that were used to create the ontological concepts. The use case is based on a car development scenario that models the connection between car testing scenarios, e.g., safety tests, and the methods and tools used to analyze and prepare these tests. The intended solution has been developed in the ONTORULE project and is still work in progress.

Introduction

Business Rules Management Systems (BRMSs) are software applications that help organizations to separate their application code from their business knowledge. BRMSs help the users to author and maintain business rules and apply decision logic that reflects this business knowledge. The business rules can have different origins, such as regulations, policy documents or business logic directly entered by domain experts. This business logic expresses both development processes and coherence between different events, including conditions and the resulting conclusions.

One of the main advantages of expressing the logic in business rules is that the domain knowledge is independent of the application code that uses this logic. In such way, there is no need to alter the application code itself, when business logic evolves, new policies are applied, already introduced policies change or retire. Thus, the use of BRMSs leads to increased flexibility and agility of the organization.

In the ONTORULE project, Audi uses OntoBroker as the execution environment for its business rules which are formalized in Objectlogic 1 . The OWL representation of the domain ontology has been developed in parallel for the research purposes described in this paper.

Domain experts who are not also business rules experts may have difficulties expressing their knowledge in formalized logic languages. Supporting them in their management of the knowledge needed to write these rules is one of the goals of the ONTORULE project.

We propose building an ontology as a formal model for representing conceptual vocabulary that is used to express business rules in written policies. Using a normalized vocabulary helps domain experts in writing rules more efficiently and is less costly than managing controlled vocabulary. Such ontologies are shared conceptual models, so experts can share the same vocabulary. We use the OWL-DL language to represent concepts and properties of the domain ontology. In addition,, the ontology is linked to the lexicon used to express rules in the text, so experts can query source documents. This calls for a formalism to link linguistic elements to conceptual ones. We opt to use the SKOS 2 language which provides basic elements to link domain concepts to terms from the text. The combination of OWL entities, SKOS concepts and their related information form a lexicalized ontology which supports the semantic annotation of documents.

The paper is organized as follows. Section 2 describes the Audi use case and Audi's expectations of using a lexicalized ontology. Section 3 explains the choice of OWL and SKOS as languages to support the lexicalized ontology. Section 4 reports the experimentations. Section 5 describes the related work in linking ontology to lexicon and semantic information retrieval. Section 6 presents the conclusion and future work.

The Audi BRMS or platform

Nowadays, the development of new cars has become very challenging and many different process steps are involved. Computer Aided technologies (CAx), like virtual modeling, simulations or the analysis and planning of physical testing, need to be integrated even tighter to satisfy the higher requirements and reduced time-to-market which also shortens the development cycles.

In the ONTORULE project Audi is developing a prototype application that makes use of ontologies and business rules that includes and visualizes the context of the following use case.

The development of a car typically follows a process starting with product planning, runs over concept development to testing (virtual & physical) until a car can be launched for production. This long process is strongly supported by different Computer Aided technologies, e.g., Computer Aided Design (CAD), Computer Aided Engineering (CAE), and Computer Aided Testing (CAT):

-CAD: This branch provides methodologies for the virtual design of the parts of a car -e.g., digital mock-up (DMU) methods, parametric design methods, -and verification of the design concerning the geometry when integrated into a car. -CAE: The main task of CAE is to provide methodologies for simulating the behaviour of a car and its functions -e.g., finite element analysis (FEA) for crash simulation, computational fluid dynamics for thermal management, and multi body simulation (MBS) for driving dynamics. -CAT: Methodologies for performing physical tests of cars are provided by CAT -e.g., vehicle management, job control, testing control, and test result analysis.

Ontologies together with business rules help Audi to keep abreast of technology advances and use them in its R&D IT applications. Especially the interweaving of the various CAx technologies will help Audi to reduce development time and cost.

One of the first steps in the development cycle of a new car model is the definition of properties, i.e., features that can be experienced by customers, like driving comfort, safety or sportiness that the desired car has to fulfill. These target properties are listed in catalogs, that describe the car's required and mandatory behavior in various granularities.

At a later stage in the development cycle, engineers begin to design new or modify already existing Computer Aided Design models, that have to comply with Audi's high quality expectations. These models are then used in simulations (Computer Aided Engineering) or serve as a model for the physical parts which are tested in Computer Aided Testing. For example, new electronic components, like an Electronic Stability Control (ESC), are tested in Hardware in the Loop (HiL) simulations that make use of virtual models that behave like the related dynamic systems.

The entirety of all these attempts and approaches, physical and virtual, is called CAx Methods. The different CAx Methods ensure either that the desired properties are achieved, or that legal requirements are fulfilled. Components, such as ESC, are referred to as "Solution Concepts" (cf. Fig. 1). They affect the customer experience or vehicle property directly.

The knowledge about the initial relation between a property that was defined in the beginning of the development cycle and a CAD part with its related CAx Methods often perishes during the progression of the vehicle's design and By modeling the described scenario in an ontology, and formulating and processing the relevant rules in a BRMS, Audi expects to reduce the knowledge gap between the various process steps for the involved employees and departments and thus to benefit from a tighter CAx integration. Sharing the knowledge by using a common tool and shared data will reduce time-consuming data acquisition and ensures that the personnel involved will access identical data which will help speed up the development and innovation cycles [START_REF] Kiss | Market Intelligence Report, D8.4[END_REF].

For example, with the help of business rules, we are calculating the duration of processes (processDuration) that involve different CAx methods (cf. rule example below). Every CAx Method has an assigned attribute for either an estimated or an actual value for its lead time, cost and maturity. Testing if the requirements for one vehicle property are fulfilled normally takes several process steps. As an example, the execution of a CAE simulation may take only minutes -the preparation, the modeling, and so on might take several weeks.

Every process, e.g., an analysis or the validation of such an analysis, makes use of a CAx Method (cf. Fig. 1). The function processDuration calculates the whole process duration: the relevant attribute time is queried with the function methodDuration (which is defined in another rule) from all the methods involved in the relevant subprocesses.

With this rule, the application is able to visualize different durations involving different methods that all test one specific vehicle property. These indications will support managers when planning their projects, so that they can choose whichever process is best suited for their work. processDuration(?X,?Y,?totalTime) :-methodDuration(?X,?Y,?totalTime). processDuration(?X,?Z,?totalTime) :-methodDuration(?Y,?Z,?time) AND processDuration(?X,?Y,?previousTime) AND ?totalTime = ?time + ?previousTime.

(Rule example, that calculates the total process duration.)

One of the difficulties with business knowledge rules is that various departments or roles sometimes use different vocabularies for the same things so they cannot understand each other immediately. Addionally, formalized rules per se are often not easy to understand.

Using an ontology as a unified model for a heterogeneous vocabulary and annotating the rules and the underlying ontology has another advantage: it will reduce misunderstandings and ensure that people are discussing the same thing. Also, the users can easily confirm and verify the appropriateness of the modeled semantic relations. The prototype that is to be developed will handle links between source documents, such as policies and internal documents, i.e. policies or internal documents, and the concepts and instances of the ontology. Also, if a business rule originates directly from a legal document, the relevant passage will be linked to the rule in the same way.

The role of the lexicalized ontology in BRMS

A BRMS of the kind described here relies strongly on domain knowledge that is encoded in a lexicalized ontology. This section shows how OWL-DL and SKOS standards support the needs for formalization, document annotation, normalization and documentation that business experts face when designing a business rule application.

Formalization of domain knowledge

The idea of the ontology as a conceptual model was explicitely introduced by [START_REF] Gruber | Toward principles for the design of ontologies used for knowledge sharing[END_REF] affirming that an ontology is: "the specification of a conceptualization". The ontology is a set of concepts that are formally defined. We opted for the for OWL-DL 3 language to express domain concepts or classes and their properties.

Such an ontology not only gives the vocabulary to be used in expressing the rules, it also provides a structured vocabulary that encodes relationships between concepts and supports cheking for inconsistencies. The following example describes the concept BuckleTest, its related SKOS concept <rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#BuckleTest"> having as label "buckle test" in the text:

<owl:Class rdf:about="&onto;#BuckleTest"> <rdfs:subClassOf rdf:resource="&onto;#PhysicalMethod"/> </owl:Class> <rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#BuckleTest"> <skos:prefLabel>buckle test</skos:prefLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/ core#Concept"/> </rdf:Description> Formalizing the business rule vocabulary in an ontology gives a structure to, and enables querying of, the rule base. For example, we can display all the roles involving physical methods by querying only the parent concept of all physical method concepts.

Experts can also query the ontology itself to search, e.g., for a test that verifies a property and is related to some constraints, as soon as these properties are encoded as concept roles. The following example shows a query written in Ob-jectLogic to query the ontology. The concept ProductInformation is used in the verification process (ProcessInformation) and every ProcessInformation uses a designated method (i.e., physical or virtual method). The concept MethodInformation has as subconcepts all the physical and virtual methods (cf. Fig. 1). The result of this query displays all ProductInformation (i.e., car parts, functions, etc) and the tools that use them.

@{Systemanalysis_ManagedProductInformation, options[outorder(

?Tool,?ProductInformation),fillNull]} ?-?MethodInformation:MethodInformation[utilizes_Tool->?Tool] AND ?ProcessInformation:ProcessInformation[ uses_ProductInformation->?ProductInformation,uses_Method-> ?MethodInformation] AND ?ProductInformation:ProductInformation.

Finally, using the OWL-DL language supports reasoning on the ontology. This is useful for searching for hidden information that is implicit in the rules, for inferring new knowledge, updating the rule base and ultimately improving the business of the organization. For example, experts may recognize that a safety test is less costly with some specific parameters.

For example, the concept SeatBelt which describes the seat belt, is related through the role assuredBy to the tests CorrosionTest and SeatBeltFlipTest, which are used to test its safety: <owl:Class rdf:about="&onto;#SeatBelt"> <assuredBy rdf:resource="&onto;#CorrosionTest"/> <assuredBy rdf:resource="&onto;#SeatBeltFlipTest"/> </owl:Class>

Semantic annotation of documents

A key issue for experts in managing a rule base is to recognize that the meaning of formal rules and natural language sources, such as written policies and documentation is a precious source of information. It is also important to update the business rules as organizations often modify their policies according to internal or external constraints.

It is therefore important to be able to mine textual sources to understand how a given concept is used in business documents, what rules are related to it and how those concepts and rules evolve when the policies are updated. This is achieved through the semantic annotation of the documents in which the mentions of the ontological entities (concepts, instances and roles) are highlighted and can be searched for.

Semantic annotation means that ontological entities are related to the terms that can be used to mention them in the texts and calls for designing lexicalized ontologies. When the ontology has been created from textual source, as for the Audi ontology, it is easy to keep track of the terms that denote the various conceptual entities. The resulting lexicalized ontology is used to annotate source documents and to query them.

Our aim is to save the terms related to the conceptual vocabulary that is used to express the business rules. We don't need to encode sophisticated information such as the morphological structure of terms since we do not perform a deep analysis of the documents. We simply need to save the various linguistic units that denote a concept, instance or role. We use SKOS for that.

SKOS supports encoding of SKOS concepts that represent the links between the OWL concepts and their related terms, which are encoded as skos labels 4 . This relation is described by <rdf:Description rdf:about>.

Normalization of vocabularies

When designing and updating business rules, experts face the problem of the heterogeneity of information sources and multilingualism. SKOS also supports that normalization of vocabularies.

SKOS enables association of a given SKOS concept with the various terms or labels that denote it in the texts or any other information source. For a given concept, SKOS supports distinguishing one preferred label and as many alternative labels as necessary, using the <skos:prefLabel> and <skos:altLabel> properties. In the Audi ontology, for example, the SKOS concept LowTemper-atureChamber is linked to two terms: low temperature chamber is encoded as the preferred label and refrigerated cabinet as its alternative form: <rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#LowTemperatureChamber"> <skos:prefLabel>low temperature chamber</skos:prefLabel> <skos:altLabel>low-temperature chamber</skos:altLabel> <skos:altLabel>refrigerated cabinet</skos:altLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core# Concept"/> </rdf:Description> Alternative labels are used to encode linguistic variants (e.g., seat belt which is a belt) or unify different vocabularies (e.g., low temperature chamber actually has the same meaning as refrigerated cabinet). The following example shows that the SKOS concept SeatBelt is related to two terms seat belt and belt in the text: <rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#SeatBelt"> <skos:prefLabel>seat belt</skos:prefLabel> <skos:altLabel>belt</skos:altLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core# Concept"/> </rdf:Description> SKOS also supports the encoding of multilingual information. The information about the language used is described by <rdf:lang=''en''>. For example, the SKOS concept TrolleyTest has a preferred label "trolley test" which is mentioned in English in the text and an alternative label "Schlittentest" in German.

<rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#TrolleyTest"> <skos:prefLabel df:lang="en">trolley test</skos:prefLabel> <skos:altLabel df:lang="de">Schlittentest</skos:altLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core# Concept"/> </rdf:Description> Thanks to the alternative labels and language tags, SKOS therefore helps experts managing the heterogeneity of the vocabulary of their sources and controlling the vocabulary used for designing rules.

Documentation of the shared knowledge

Since experts often have to manage a large volume of information but do not always formally describe all the concepts, it is important to add informal documentation when it is available. Defining concepts in natural language is very important to understand what concepts mean, especially if they have ambiguous or implicit labels.

Since legal documents such as policies often define their terminology precisiely, we propose to extract those definitions from the source documents when designing the ontology and to associate them with the related SKOS concepts using the label <skos:definition>.

Source documents are exploited to find definition for existing concepts in the Audi ontology. For example, the concept ReferenceZone is described as follows:

<rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#ReferenceZone"> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core# Concept"/> <skos:definition>"Reference zone" means the space between two vertical longitudinal planes , 400 mm apart and symmetrical with respect planes , 400 mm apart and symmetrical with respect to the H point , and defined by rotation from vertical to horizontal of the head form apparatus. </skos:definition> <skos:prefLabel>reference zone</skos:prefLabel> </rdf:Description>

Formalism for the Audi lexicalized ontology

The Audi ontology is a formal representation of the conceptual vocabulary used to express business rules in written policies. In the Audi use case, we use OWL-DL to describe concepts and their roles. Structuring the vocabulary and normalizing it supports querying of the ontology in order to manage the knowledge base, infer new knowledge and detect inconsistency. As the Audi ontology is lexicalized, the domain concepts and their occurrences in the text can be matched onto one another thanks to the linkage of OWL entities, SKOS concepts and labels.

This is a simple efficient way to represent lexicalized ontologies and we show in the following section its benefit for the Audi BRMS. Figure 2 describes how the Audi ontology is linked to the lexicon and annotated text. Fig. 2. A lexicalized ontology for annotating source documents. Each concept from the ontology is linked to a SKOS concept SC and each SKOS concept is related to its labels l. The annotations link some text entities to these labels This section presents the Audi ontology and illustrates the benefit in the Audi use case of having such a lexicalized ontology.

The Audi BRMS ontology

The Audi ontology has been built in two steps. At first, the goal was to integrate the various existing knowledge sources in a single one. This resulted into a small conceptual model (around 30 concepts) associated with a large knowledge base (thousands of instances).

In a second step, in order to better fits the experts' needs for semantic querying and document mining, the initial ontology has been restructured and lexicalized. It also appeared useful to increase the granularity of the domain model so as to represent not only the various types of tests but also their actual occurrences in the car manufacturing process (instances that are related to the different tests applied to specific vehicle models).

This led to encoding of various elements as concepts rather than instances (90 concepts were added). Modeling tests as concepts supports, for instance, querying of the Audi ontology in such a way as to detect implicit relationships between tests, tools and parameters, which are important for the safety of vehicles. The conceptual structure has been reorganized (4 subsumption levels instead of 1). A SKOS resource has been associated with this resulting ontology: each concept is related to at least 1 preferred label and up to 5 alternative labels. In addition, using a subset of the initial ontology for the exploration of written policies showed that 10 of the mentioned tests were missing in the initial ontology and led us to enrich it [START_REF] Omrane | Les entités nommées : éléments pour la conceptualisation[END_REF].

Semantic querying

The knowledge base of our ontology currently consists of several thousands of instances separated into more than 30 concepts. It currently reflects only a small part of the whole use case and will be enhanced over time. We have annotated a subset of these instances in English and German to allow the users to use the aimed-at application in their preferred language. Additionally, alternative labels can be added when different users prefer different terms in their daily work. We have also planned an interface to query the ontology and search for specific instances and concepts. For that reason, an annotated ontology is much more appropriate.

Document mining

We enrich the Audi ontology with new concepts and link conceptual elements to linguistic ones from the Audi documents. We obtain a lexicalized ontology that contains 90 SKOS concept, 90 preferred terms and 16 alternatives labels.

For example, we create the SKOS concept BreakingStrengthOfStrapTest that describes a specific test of the seat belt and link the preferred and alternative labels which are mentioned in source documents.

<rdf:Description rdf:about="http://lipn.univ-paris13.fr/RCLN/ terminae/Audi#BreakingStrengthOfStrapTest"> <skos:prefLabel>breaking strength test</skos:prefLabel> <skos:altLabel>test of breaking strength</skos:altLabel> <skos:altLabel>breaking strength of strap test </skos:altLabel> <skos:altLabel>test of breaking strength of strap </skos:altLabel> <rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core# Concept"/> </rdf:Description> Once the ontology is lexicalized, domain experts can query source documents to search for fragments of texts that describe specific concepts mentioned in rules. For example, they can find all references of the concept BreakingStrengthOfS-trapTest in the text, wherever it is mentioned in the documents: prefix schema:<http://lipn.univ-paris13.fr/RCLN/terminae/schema#> prefix onto:<http://lipn.univ-paris13.fr/RCLN/terminae/Audi#> prefix skos:<http://www.w3.org/2004/02/skos/core#Concept> select ?sentence where { ?sentence rdf:type schema:sentence ?sentence schema:annotatedBy ?concept ?concept schema:realized concept <onto: BreakingStrengthOfStrapTest> ?skos skos:skosConcept ?concept ?skos skos:preflabel "test of breaking strength" } We can also search for all sentences where the physical methods are mentioned in the text. As the concepts expressing tests are sub-concepts of the concept "MethodInformation", we query the text by searching about all subconcepts of "MethodInformation". The following example shows the query: prefix schema:<http://lipn.univ-paris13.fr/RCLN/terminae/schema#> prefix onto:<http://lipn.univ-paris13.fr/RCLN/terminae/Audi#> prefix skos:<http://www.w3.org/2004/02/skos/core#Concept> select ?sentence where { ?sentence rdf:type schema:sentence ?sentence schema:annotatedBy ?concept ?concept schema:realized concept <onto:MethodInformation> ?conceptfils rdfs:subClassOf ?concept ?skos skos:skosConcept ?conceptfils } Thanks to the labels of concepts, the ontology can be used to annotate the documents. Figure 3 shows an example of texts where all the mentions of known concepts are emphasized. This supports experts in browsing of documents. The first is the NLP domain which aims at adding some semantic structure to a lexicon by linking its elements to ontology's elements. There are several ways to combine a lexicon with an ontology. We describe the most popular ones.

LMF5 standard [START_REF] Francopoulo | Lexical markup framework: ISO standard for semantic information in NLP lexicons[END_REF] aims at detecting the relation between the words used to express objects and their formalization in the ontology. The mapping is assured by axioms declared in the ontology. LMF has the merit of assessing large scale lexical resources. TMF6 is a standard ISO that allows describing the terms of a lexicon within a formal language. The supported language is based on the definition of a meta-model and a set of features used to represent elements of the lexicon (e.g., related class, language, lexical information). OLIF7 is an open standard for exchange format that allows to represent morphological, syntactic and semantic data categories. LMM8 is a formal language described in OWL-DL. It integrates linguistic knowledge resources (e.g., FrameNet) and foundational ontology (e.g., DOLCE). It has the advantage of dealing with multi-lingual resources.

The other family trys to link an ontology to a lexicon by introducing models for representing linguistic information for ontologies. [START_REF] Reymonet | Modélisation de ressources termino-ontologiques en OWL[END_REF] defines a meta model to distinguish terms and concepts. Terms are linked to concepts through a semantic relation "denote". LexOnto proposed by [START_REF] Cimiano | LexOnto: A Model for Ontology Lexicons for Ontology-based NLP[END_REF] supports definition of the lexicon by using all the expresseiveness of OWL. LexOnto considers a term as an "OWL: class" and presents a meta model that supports linking of terms to concepts. The relation between the elements can be simple or complex (e.g., sub-category-of).

LingInfo [START_REF] Buitelaar | A Multilingual/Multimedia Lexicon Model for Ontologies[END_REF] define a meta class to link the linguistic properties to the concept or to its Data/Object properties. The meta class contains the associated term, its language and the morpho-syntactic structure of the term. [START_REF] Ma | Ontologies étendues pour l'annotation sémantique[END_REF] introduces a set of annotation rules to link an existing ontology to its lexicon that is described in the text. The authors consider a standard OWL-DL ontology that they extend and, for each domain ontology, propose to define a set of annotation rules that link each concept to its linguistic representations in the text. LIR [START_REF] Peters | Multilingual and localization support for ontologies[END_REF] supports storage of linguistic information in a lexical ontology and linkage to domain concepts via ontology relations.

From a practical point of view, the choice of one model or another depends on the aimed application and the task. Our aim is to build a lexicalized ontology to allow annotating the technical documents and thus to help the expert in exploring documents by querying its set of annotations explained above. We use for that a W3C standard SKOS that links linguistic to semantic knowledge. SKOS introduces especially the properties: skos:prefLabel and skos:altLabel to link preferred and alternative terms to each concept in the ontology.

Usually users query documents by entering keywords that occur in the target documents in Information Retrieval (IR). But searching relevant information through a keyword-based approach very often provides limited results. Therefore, IR adopts a conceptual search-approach to capture the user needs on a semantic level [START_REF] Fernández | Semantically enhanced Information Retrieval: An ontology-based approach[END_REF]. Information retrieval uses ontologies as formal models to query knowledge data base [START_REF] Davies | Information Retrieval, searching in the 21st century, chap. Semantic Search[END_REF]. It takes into account these keywords and tries to match them with their corresponding concepts in the ontology. To do so, ontologies need to be linked to a rich lexicon as in lexicalized formalism described above so that the result obtained matches the query of users.

The ability to link an ontology to a lexicon is very important for annotation of documents. Annotating documents supports linking of textual units to their corresponding concepts in an ontology, and showing the relationship between these concepts and the terms that are related to them. Such a lexicalized ontology helps the exploration of texts through semantic queries once they are annotated with the ontology.

Conclusion

The proposed integration of CAx systems will increase the flexibility of the development process, allowing Audi to meet the increasing market demand for product diversification. This integration relies on the design of an application that is currently under development and is based on a BRMS. Our approach for the acquisition and management of the knowledge embodied in such BRMS relies on a lexicalized ontology which unifies and normalizes the various vocabularies and links the conceptual knowledge to the source policies and regulation written in natural language. Using a lexicalized ontology enables experts to determine the most suitable CAx Methods from given functional requirements and to query sources documents. These new approaches, standards and technologies are already partially integrated in some processes. During the next years Audi will continue to incorporate the ONTORULE platform in their landscape which will lead to even less time-consuming, cheaper and higher quality processes in the innovation and development cycles.

Fig. 1 .

 1 Fig. 1. Simplified extract of the intended Audi R&D ontology

Fig. 3 .

 3 Fig. 3. A fragment of text annotated by the lexicalized ontology.

This work was realised as part of the FP7 231875 ONTORULE project (http://ontorule-project.eu). We thank our partners for the fruitful discussions, especially to Audi for the collaboration on their use case.

Ontology, rule and query language introduced by ontoprise as successor of F-Logic. Main language supported by the semantic web ontology repository and inference engine OntoBroker.

Lexical Markup Framework

Terminological Markup Framework

Open Lexicon Interchange Format

Linguistic Meta Model