Denis Feurer

Jean-Stéphane Bailly

Christian Puech

Yann Le Coarer

Alain A Viau

Very high-resolution mapping of river-immersed topography by remote sensing

Keywords: immersed topography, remote sensing, river, through-water, very high spatial resolution

Remote sensing has been used to map river bathymetry for several decades. Non-contact methods are necessary in several cases: inaccessible rivers, large-scale depth mapping, very shallow rivers. The remote sensing techniques used for river bathymetry are reviewed. Frequently, these techniques have been developed for marine environment and have then been transposed to riverine environments. These techniques can be divided into two types: active remote sensing, such as ground penetrating radar and bathymetric lidar; or passive remote sensing, such as through-water photogrammetry and radiometric models. This last technique -which consists of fi nding a logarithmic relationship between river depth and image values -appears to be the most used. Fewer references exist for the other techniques, but lidar is an emerging technique. For each depth measurement method, we detail the physical principles and then a review of the results obtained in the fi eld. This review shows a lack of data for very shallow rivers, where a very high spatial resolution is needed. Moreover, the cost related to aerial image acquisition is often huge. Hence we propose an application of two techniques, radiometric models and through-water photogrammetry, with very high-resolution passive optical imagery, light platforms, and off-the-shelf cameras. We show that, in the case of the radiometric models, measurement is possible with a spatial fi ltering of about 1 m and a homogeneous river bottom. In contrast, with through-water photogrammetry, fi ne ground resolution and bottom textures are necessary.

I Introduction

Rivers have a prominent role in many contexts -as a natural environment, as a transfer medium, as a physical medium, as a natural resource. This list is not exhaustive. Understanding the river physical and ecological processes requires knowledge of the three-dimensional geometry of the riverbed, at various spatial and temporal scales, as shown by three examples. First, a key parameter in water resource management is the volume of water fl owing in the river and it may be computed by measuring the immersed topography as well as the water level. Second, river morphology monitoring and riverine landscape management requires understanding of underlying physical processes. This is currently done through hydraulic models. These models are most often calibrated or validated with gauging data, which are available at only a few points. The need for detailed knowledge of riverbed topography is hence critically real. Third, when studying processes driving fish population dynamics, the fish habitat approach using spatial data is increasingly used. These spatial models determine, for each fi sh species and each development stage, the relationship between a presence index and river physical parameters (depth, speed, bottom type) (Le Coarer and Dumont, 1995a). These last two needs are at the root of the work presented hereafter.

Three-dimensional representations of riverbeds are now commonly used in hydrological studies [START_REF] Lane | Developments in monitoring and modelling smallscale river bed topography[END_REF]. Accurate measurement of the river geometry at a large scale, and frequently with very high spatial resolution, is required. If these measures can be obtained from a boat for navigable rivers, the operational fi eldwork is tedious. Ground surveys that provide such measurements are time-consuming and necessitate large amounts of manpower. As a consequence, the ratio cost/area covered is very high and investigation is constrained to small parts of the river. As a result, limited funding and working time mean that hydrologic studies cannot be validated for a suffi ciently representative section of the river: other solutions to measure river topography have to be investigated.

The scale question shows up quite often in hydrologic studies. Remote sensing has hence been widely used in this domain [START_REF] Muller | Contribution of space remote sensing to river studies[END_REF][START_REF] Gendreau | Hydrology and remote sensing information[END_REF][START_REF] Mertes | Remote sensing of riverine landscapes[END_REF][START_REF] Schmugge | Remote sensing in hydrology[END_REF]. Moreover, remote sensing, as a non-contact measurement method, allows one to collect data about an inaccessible river and, in addition, provides complementary data characterizing the river [START_REF] Creutin | Local remote sensing of rivers (418)[END_REF]. In the literature river-immersed topography or water depth is often one parameter among others and is rarely the main issue. We have extracted from the literature the information of interest and synthesized it into a review of remote sensing techniques that have been used to map depth and/or riverbedimmersed topography (Table 1).

Excepting sonar, four techniques exist: (1) spectral methods, exploiting the correlation between depth and light absorption; (2) ground penetrating radar (GPR);

(3) bathymetric lidar; (4) photogrammetry. These techniques are either active (ie, the illumination is provided by the device) or passive (ie, the illumination is provided by the sun). For each of these techniques, we give a short physical explanation of the method, its applicability, with regard to the different experimental conditions, and fi nally the measured characteristics, scale, and expectable positioning precision (planimetric and altimetric).

The fi rst part of this paper is a review of the references summarized in Table 1. Thematic applications and remote sensing tools used in these papers are very diverse. In order to deal with such heterogeneous information, the review covers fi rst active remote sensing techniques and then passive ones. In the second part, we present two additional methods, focused on mapping river depth and immersed topography at very high spatial resolution. Indeed, many research issues in : Progress in physical geography, vol 32, n°4, 2008.p.403-419 remain: in short, active methods imply heavy logistics and great costs, and hence have been fi rst studied with simulated data rather than in the fi eld (Lesaignoux, 2006;Lesaignoux et al., 2007). Meanwhile, for local-scale hydrologic studies, in small streams, where centimetric precision is required, there is a big issue in assessing the potential of very high spatial resolution imagery as a tool to map river depth or immersed topography. This is the topic of the two studies presented in the second part of this paper.

II Review of the remote sensing techniques for bathymetry

Most methods employed in the riverine environment have fi rst been developed and tested for the marine environment [START_REF] Hickman | Application of an airborne pulsed laser for near shore bathymetric measurements[END_REF][START_REF] Polcyn | The measurement of water depth by remote sensing techniques (8973-26-F)[END_REF]Lyzenga, 1978;[START_REF] Fryer | Photogrammetry through shallow water[END_REF][START_REF] Morel | Passive multispectral bathymetry mapping of Negril Shores, Jamaica[END_REF].

The theoretical background of the methods described hereafter comes largely from these works. The review is divided into two parts: first, the theoretical background of each method; second, the experimental results obtained in terms of feasibility, characteristics and quality of the measure.

1 Principle of the different methods a Spectral methods: These methods, using passive optical imagery, are based on the fact that light is attenuated through the water column. Thus, image information is related to water depth. As a consequence, these methods do not give access to the absolute position of the river bottom (riverbed topography). Several publications have proposed methods based on image classifi cation [START_REF] Hardy | Application of multispectral videography for the delineation of riverine depths and mesoscale hydraulic features[END_REF]Marcus et al., 2003;[START_REF] Gilvear | The use of remotely sensed data to detect channel hydromorphology[END_REF][START_REF] Leckie | Automated mapping of stream features with high-resolution multispectral imagery: an example of the capabilities[END_REF]. In these works, depth is a descriptive parameter among others, such as bottom type or hydrodynamic unit. The main objective is often to map and characterize the river and its habitats.

A second method, with a physical background, has also been quite widely used.

When the effects due to scattering in the water and internal reflection at the water surface ere neglected, light energy decreases exponentially through the water column [START_REF] Polcyn | The measurement of water depth by remote sensing techniques (8973-26-F)[END_REF]Lyzenga, 1978). Lyzenga (1978) thus proposed using a variable, defi ned by Xi = ln (Li -Lio), with i, spectral band index; Li, radiance measured by the sensor; Lio, radiance of a theoretical infi nite water column. The Xi variable is approximately linearly correlated to the depth. Remaining internal refl ection effects are signifi cant only for very shallow water and high bottom refl ectance. For a given wavelength, atmospheric condition and bottom type, extinction depth value depends on the attenuation coefficient of the water, which mainly depends on water turbidity. Hence, the possibility of measuring depth strongly depends on turbidity conditions.

In addition, an interesting piece of work should be cited here, even though it has not yet been applied to riverine environments. [START_REF] Morel | Passive multispectral bathymetry mapping of Negril Shores, Jamaica[END_REF] proposed a method to derive water depth from remote sensing images without in situ measured depths. The method, called 4SM, uses shallow areas of the image to derive ratios Xi/Xj for all pairs of spectral wavelengths i and j. With these data and the attenuation coeffi cients given by [START_REF] Jerlov | Marine optics[END_REF], a digital elevation model and a low-tide view (corresponding to bottom refl ectance) are computed.

b Stereophotogrammetry: Photogrammetry includes a set of techniques for deriving spatial information from images. Among these techniques, stereorestitution consists of determining terrain elevation from several pictures of the same area taken with different viewing angles. Indeed, within two images of the same area, one -motionless -point will have a different location because of: (1) the sensors' internal characteristic differences (if two different sensors are used); (2) different positions of the two sensors; (3) different viewing angles; (4) point relative height (see Figure 1).

Figure 1

The stereoscopic effect: image acquisition (3D); stereo pair (2 × 2D)

Figure 2 Geometry of through-water photogrammetry (from [START_REF] Fryer | Photogrammetry through shallow water[END_REF]. P is the actual position of the immersed point, P'' is the apparent point. P 1 and P 2 are the intersections of light rays with the water surface. S 1 and S 2 are the optical centres of the two cameras. P' 1 and P' 2 are the image points of the point P It is thus possible to calculate point heights from their positions within the two images. The information needed is: (1) positions of the image points; (2) sensor internal geometry;

(3) sensor external geometry.

In the particular case of through-water photogrammetry, the air/water interface implies additional processing. Refraction through water surface leads to apparent depths inferior to the actual ones (see Figure 2, where P˝ is the apparent position of P). Waves and specular refl ection add extra error sources [START_REF] Okamoto | Wave influences in two-media photogrammetry[END_REF]Fryer, 1984;[START_REF]Errors in depth determination caused by waves in through-water photogrammetry[END_REF][START_REF] Feurer | On the use of very high resolution optical images to map river bathymetry: upscaling from aerial to satellite imagery[END_REF].

c Ground penetrating radar (GPR): The principle of ground penetrating radar is the following (see Figure 3): one antenna generates an electromagnetic wave, which is transmitted, absorbed and reflected by the media and interfaces between two media. The part of the energy returning to the sensor is received by the antenna and recorded. Record shape (echo amplitude and/or two-way time -see Figure 4) allows one to determine the geometry of the crossed media. For instance, interfaces between two media provoke strong refl ections that are generally easily detectable. A quite detailed understanding of the physics associated can be found in [START_REF] Davis | Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy[END_REF].

Figure 3 The principle of ground penetrating radar (GPR) GPR was first developed for geological studies, and its use was then extended to hydrogeological and hydrological studies. It has also been used to measure lake or river depths, either when their surface is frozen or during fl ood events. Physically, both the air/water and the water/ground interfaces return echoes, so water thickness can be measured.

d Lidar (Light detection and ranging): Lidar is the name of an active sensor. Two pulses of different wavelengths are sent out. The near-infrared one only penetrates a few centimetres and is hence quickly attenuated and returned by the water surface. The green one penetrates the water and is returned by the bottom. Measuring the signal travel times allows calculation of the water depth (see Figure 5).

Laser pulses are defl ected by a rotating mirror, which allows ground scanning across the fl ight line, in front of the platform. Water depth calculation algorithms can use various wave returns (Pe'eri and Philpot, 2007; Allouis and Bailly, 2008): (1) infrared return: strongly absorbed, penetrates very little in water, used to determine the air/water interface; (2) red return: due to Raman diffusion, characterizes the volume diffusion;

(3) fi rst green return: water interface slightly refl ects the green pulse -when signifi cant, this fi rst green return can help in the localization of the water surface; (4) last green return, corresponding to river bottom.

The bibliography for bathymetric lidar is essentially focused on applications in coastal marine environment (for instance, [START_REF] Hoge | Water depth measurement using an airborne pulsed neon laser system[END_REF]Lyzenga, 1985;[START_REF] Muirhead | Airborne lidar bathymetry[END_REF][START_REF] Irish | Monitoring New Pass, Florida, with high density lidar bathymetry[END_REF][START_REF] Cracknell | Remote sensing techniques in estuaries and coastal zones-an update[END_REF][START_REF] Parson | Use of lidar technology for collecting shallow water bathymetry of Florida Bay[END_REF][START_REF] Irish | Coastal engineering applications of high-resolution lidar bathymetry[END_REF][START_REF] Cracknell | Remote sensing techniques in estuaries and coastal zones-an update[END_REF][START_REF] Guenther | New capabilities of the 'SHOALS' airborne lidar bathymeter[END_REF][START_REF] Buonaiuto | Limiting slopes and depths at ebb-tidal shoals[END_REF][START_REF] Fitzgerald | Recent developments in the geomorphic investigation of engineered tidal inlets[END_REF][START_REF] Storlazzi | Quantitative morphology of a fringing reef tract from high-resolution laser bathymetry: Southern Molokai, Hawaii[END_REF]. As noticed by [START_REF] Wozencraft | Airborne and integrated technologies for coastal mapping and nautical charting[END_REF], lidar river bathymetry remains rare. At present the only two peer-reviewed works are [START_REF] Hilldale | Assessing the ability of airborne LiDAR to map river bathymetry[END_REF] and [START_REF] Kinzel | Evaluation of an experimental LiDAR for surveying a shallow, braided, sand-bedded river[END_REF]. In addition, one can fi nd an increasing number of conference/workshop presentations [START_REF] Millar | Using airborne lidar bathymetry to map shallow river environments[END_REF]McKean et al., 2006;[START_REF] Pe'eri | Increasing the existence of very shallow-water LIDAR measurements using the red-channel waveforms[END_REF][START_REF] Bailly | Bathymetry with LiDAR on gravel bed-rivers: quality and limits[END_REF].

2 Measurement characteristics and scale -applicability a Potential of passive optical imagery: spectral methods and stereophotogrammetry: Applicability of these methods mainly depends on the solar energy transmitted through the water column and refl ected by the river bottom, which must be visible. As a consequence, measurement is severely limited in turbid waters, locally impeded by overhanging vegetation or specular refl ections (sun glints); maximum measurable depth depends on water clarity. As noticed by Lejot et al. (2007), a limit of around 1 m is often reported for gravel-bed rivers [START_REF] Winterbottom | Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography[END_REF][START_REF] Brasington | Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport[END_REF], but some experiments have been done for rivers deeper than 3 m (Lyon et al., 1992;Lejot et al., 2007), and even 10 m [START_REF] Kumar | Bathymetric mapping in Rupnarayan-Hooghly river confl uence using Indian remote sensing satellite data[END_REF]. These techniques perform a pixel-based image analysis. Thus, depth measurement is spatialized on a regular grid. Depth measurement planimetric resolution ranges from 5.6 cm [START_REF] Brasington | Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport[END_REF] to 36.25 m [START_REF] Kumar | Bathymetric mapping in Rupnarayan-Hooghly river confl uence using Indian remote sensing satellite data[END_REF], depending on sensor and data processing. Using images implies fi nding a compromise Figure 4 Example of record obtained by GPR [START_REF] Davis | Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy[END_REF]. Every single return full waveform has been put vertically side by side so that the whole cross-section is described between planimetric resolution and spatial coverage of each image (and thus global cost). As reported by [START_REF] Gilvear | The use of remotely sensed data to detect channel hydromorphology[END_REF], data processing strongly depends on study site and experiment conditions (bottom spectral heterogeneity, turbidity), and error sources are diverse (riparian vegetation shadows, sun glints). Most often, the results are qualifi ed by giving the correlation between observed and computed data. A few references qualify the results in terms of mean error and standard deviation of the errors. With very high spatial resolution (centimetric ground pixels), measure precision is no better than 20 cm and measure accuracy around 10 cm. Working scales mainly depend on sensors and platforms used. For large streams (over 200 m wide), satellite imagery can be used. For smaller streams (between 20 and 200 m wide), airborne remote sensing is used. Works dealing with remote sensing of smaller streams are less numerous, because of spatial resolution limitations [START_REF] Gilvear | The use of remotely sensed data to detect channel hydromorphology[END_REF].

b Potential of GPR: Following the work of [START_REF] Annan | Impulse radar applied to ice thickness measurements and fresh water bathymetry[END_REF], [START_REF] Kovacs | Remote detection of water under ice-covered lakes on the north slope of Alaska[END_REF] measured water depths up to 5 m under 2 m thick ice, and [START_REF] Moorman | Bathymetric mapping and sub-bottom profiling through lake ice with ground-penetrating radar[END_REF] measured water depths up to 20 m, both on frozen lakes. Most often, GPR is used very close to the surface of the river, frozen river, bridge, cable, or low-flying helicopter. At present, only one reference to GPR mounted on a helicopter exists (Melcher et al., 2002). GPR is most often used along a transect (cable, bridge). [START_REF] Creutin | Local remote sensing of rivers (418)[END_REF] mentioned a survey in which a 200 m long transect was measured within 10 minutes. Using a helicopter, Melcher et al. (2002) fl ew over three sites spanning 100 km within 60 minutes (characteristics of each site are not given). Resolution in elevation mainly depends on the frequency used. The higher the frequency, the higher is the resolution, approximately a third of the wavelength: with a 100 MHz radar, vertical resolution is about 10 cm [START_REF] Spicer | Measuring fl ood discharge in unstable channels using ground-penetrating radar[END_REF]. Due to the fact that water depth is derived from a travel time, precision and accuracy of the measure depend on how well the medium characteristics are known. Penetration depth is best for low water conductivities and low electromagnetic wave frequencies. This leads (see above) to a compromise between penetration depth and vertical resolution. Penetration depth in pure water with a 100 MHz GPR is about 10 m [START_REF] Spicer | Measuring fl ood discharge in unstable channels using ground-penetrating radar[END_REF]. GPR can be used through only lowconductivity water, less than 1000 S cm -1 , and sediment concentration lower than 10,000 mg L -1 [START_REF] Creutin | Local remote sensing of rivers (418)[END_REF]. c Potential of bathymetric lidar: This method encounters the same limitations as passive optical ones concerning the overhanging vegetation, but does not depend on illumination conditions [START_REF] Hilldale | Assessing the ability of airborne LiDAR to map river bathymetry[END_REF]. Nevertheless, this technique is still sensitive to water turbidity. Energy of laser pulse allows the penetration of the water column only typically up to two or three times the Secchi depth. The technique involves algorithms that detect and discriminate energy peaks, which is hardly possible for depths lower than 0.50 m (Lesaignoux et al., 2007). Depth measures obtained with bathymetric lidar consist of a non-regular point cloud. Laser pulse footprints have a typical 2 m diameter extent. It is larger on the river bottom because of light dispersion and refraction through water. The planimetric positioning accuracy of each spot ranges from 1 to 5 m depending on the positioning technology used (see Irish and Lillycrop, 1999, for SHOALS specifi cations). Vertical accuracy is minimally 0.20 m. Swath width depends on the sensor, acquisition mode and fl ight height. It is roughly included within the range of 0.5 to 0.75 h where h is the fl ight height. Ordinarily, fl ight height is about 200 to 500 m, which gives about 100 to 400 m swath widths. The area covered within one hour ranges from about 20 to 60 km 2 .

Synthesis

This review shows that, among the four methods presented, the majority of studies concern the spectral methods. Some very specifi c applications use GPR. In addition, river applications of bathymetric lidar are becoming more numerous. There is a crucial lack of experiments in through-water photogrammetry, with only one test, on the Ashburton River, New Zealand (see Table 2).

In addition, it is noticeable that image acquisition from ultra-light aircrafts or unmanned aerial vehicles (UAV), with off-theshelf sensors, is still very new in the literature (Lejot et al., 2007). Hence, we decided to test the two passive methods with this specifi c equipment, easily exploitable in field and affordable for the majority.

III Case study of small-scale river bathymetry with small-format cameras and ultra-light aircraft 1 Test site and data acquisition

The test site is located on the middle Durance River, France. This part of the river is a 60 km long regulated stretch, downstream of the Serre-Ponçon dam. Water level and discharge are fairly low and constant in the natural bed of the river and the shallow waters are usually clear. Four tributaries still bring small natural variations, so this section was chosen as a test site for hydrobiological studies. River width varies between 5 and 100 m. The mean depth is around 0.30 m, with maximum depths around 2 m. Our study was focused on two test sites of about 1 km length (see Figure 6). On these two test sites, depth varies from 0 to 1.6 m with a mean of 0.26 m; width ranges from 10 to 50 m.

Images were acquired from ultra-light aircrafts and UAVs. Such aircraft can fly relatively slowly and low. In addition, such aerial platforms allow acquisition at a reduced cost and improve the acquisition fl exibility. Usually, UAVs are considered as scale models and benefi t from lighter regulation. Images treated with the radiometric method were acquired for September 2004, with a 35 mm fi lm NIKON F100 camera aboard a Pixy [START_REF] Asseline | Conception et utilization d'un drone à vol lent pour la télédétection rapprochée[END_REF]. We used natural colour, colour infrared, and tungsten fi lms, which cover various spectral bands. Flying heights were between 50 and 150 m, giving ground resolutions between 1 and 3 cm. Some targets (black and white test cards) were installed in the fi eld in order to retrieve acquisition geometry.

A second image acquisition campaign was held in September 2005, with a view to testing the photogrammetric method. We used a Sony DSC-F828 small-format non-metric digital camera. We also used a polarizing fi lter in order to limit sun glints. The sensor was fi xed on a custom-built platform, hand-held off board a Ballerit HM-1000 ultra-light aircraft. Within such conditions the photographer is able to set the rotation of the polarizing fi lter and even rectify the position of the platform to the near vertical in real time. About 200 red crosses were painted as ground control points. Flying height and speed were fi xed, and a timing interval was determined in order to obtain consistent flying axes with 60% overlapping. With an average fl ying height of 220 m, the ground resolution is approximately 0.09 cm. Independent validation data sets were acquired with a Leica TCRA 1102 total station, according to the method published by Le Coarer and Dumont (1995b). Ground control points have been positioned with either the same equipment or a Leica 1200 differential GPS in RTK mode.

2 Methodology a Radiometric models: Using Lyzenga's (1978) method, [START_REF] Winterbottom | Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography[END_REF] exploited the logarithmic relationship between image refl ectance and water depth through regression analysis. Orthophotos were produced with ERDAS Imagine Orthobase, thanks to the ground control points visible in the images. Three image blocks, corresponding to the three film types colour infrared, tungsten, and natural colours), were formed. These stereo models have RMSE of 2.26 (colour infrared), 2.88 (tungsten) and 3.87 pixels (natural colours), with ground pixel sizes respectively of 1 cm, 2 cm and 1 cm. Once the georeferencing step The fi rst regressions performed on a pixel base showed R² values ranging from 0.2 to 0.5 depending on photographic emulsion and river sector. When comparing the depths predicted by these models to the actual ones, the correlation ranges between 0.23 and 0.60. Analysis of differences between predicted and actual depths shows a strong sensitivity of the regression laws to features such as residual sun glints or local algal cover. To reduce the effect of these local features, different median spatial fi lters, with window sizes from 1 × 1 to 200 × 200 pixels, were used. The same regression analysis was done for each of these window sizes.

b Through-water photogrammetry: This method takes advantage of the image geometric information. Knowing the acquisition geometry and the position of a point within two images, the (x, y, z) position of this point can be computed. In the case of a riverbed, an additional issue has to be taken into account: refraction of light rays through the air/water interface. In order to measure depths from the set of images, we followed a three-step procedure: (1) geometric calibration; (2) stereo measurement; (3) processing of the refraction effect. Interior calibration consisted of lens distortion correction, and determination of the position of the CCD matrix relatively to the body of the camera and the optical system. Exterior orientation was done for each stereo pair with bundle adjustment. Stereorestitution implies image point identifi cation between the two images. This can be accomplished with automatic matching algorithms or by manual stereo matching. For the latter technique, we used special glasses and software allowing the operator to see the stereo model in relief. The main advantage of the latter technique is the noticeable reduction of false matching. Finally, the effect of refraction was taken into account. Interface position was derived from the bank lines. Given the positions of the river bed point, of the interface, and the stereo pair acquisition parameters, it was possible to compute the geometry of incident rays and thus the intersection of refracted rays [START_REF] Feurer | Measuring depth of a clear, shallow, gravel-bed river by throughwater photogrammetry with small format cameras and ultra light aircrafts[END_REF].

3 Potential of the methods a Radiometric models: An optimal window size between 50 and 100 pixels occurred for both the riffles and pools (Figure 7). This corresponds to an approximate ground resolution between 1 and 2 m. This is consistent with the results obtained by [START_REF] Carbonneau | Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fl uvial environments[END_REF] on the same type of river. The regression laws corresponding to the optimal coeffi cient of determination were then used to produce depth maps. More than 500 independent immersed points were used for the validation, within the different sectors of the river, and for the different fi lm emulsions. The mean error ranged between 0.03 and 0.13 m. The root mean square error ranged between 0.04 and 0.16 m. The detailed error statistics are summarized in Table 3. As a comparison, actual depths ranged between 0 and 0.90 m, with a standard deviation of 0.22 m and a mean value of 0.40 m. There are still residual errors related to sun glint and algal cover, which are not taken into account by the logarithmic model. A way to improve these results is to refine the algorithm by applying different regressions to more numerous sectors, for instance sectors showing bright and dark bottoms. This has been tested on one natural-colour image and decreased the RMSE from to 0.09 to 0.06 m in the pools with bright bottoms.

b Through-water photogrammetry: Interior orientation and lens calibration was done from four stereo pairs of a known scene, including 50 points in three points, whose position is known with a millimetric accuracy. The Etalonnage software [START_REF] Egels | Photogrammétrie et micro-ordinateur[END_REF] allowed reduction of the RMSE of the models from 5 to 0.25 pixels by taking into account a radial distortion. The exterior orientation parameters were determined for each of the nine stereo pairs covering the full 800 m long reach by the Poivilliers E software, described in [START_REF] Egels | Photogrammétrie et micro-ordinateur[END_REF]. The residual parallax of each stereo model ranged from 0.17 to 0.85 pixels. Due to turbidity and clogging conditions, the distribution of matched points was not uniform. Due to diffusion phenomenon linked to water turbidity, the 0.09 m ground resolution hardly allowed the detection of individual cobble or pebble. Typically, these objects could have been pointed out in very shallow waters, where their sharp shadow provided a good contrast. In deeper waters, the matched points corresponded most often to a limit between algal cover and bottom without immersed vegetation.

The mean error on the whole reach was 0.10 m. The standard deviation of the error was 0.19 m. The deviation of the error was 0.13 m for stereo pairs with better acquisition condition (B/h ratios around one), which should be compared to 0.20 m, the standard deviation of the error for the stereo pairs with a B/h around 0.50. A positive bias can be noticed for all the stereo pairs. This systematic error can be explained by several factors. The river bottom surveyed in fi eld is fi ltered; indeed, operators survey the mean bottom level and avoid taking into account the local variations due to an isolated rock or immersed vegetation covering the riverbed. In addition, in the case of this experiment, most points were taken on the edge of a vegetated area, which increased the relative infl uence of these areas. This requires further study, in particular with different acquisition geometries and scales allowing the detection of individual rocks and coarse pebbles.

IV Conclusion

This review shows that, among the four presented methods, most studies concern spectral methods; most often, data acquisition is from a plane. In the light of this work, we decided to test the two passive methods with very high spatial resolution and specifi c equipment easily exploitable in field and affordable for the majority. The fi rst study, described with more detail in [START_REF] Chaponnière | Télédétection et bathymétrie des rivières: application à la Durance[END_REF], used radiometric models extrapolated at the image scale from calibration points. This work showed that the smallest ground resolution is not the most effective; ground pixels of about 1 m seem indeed to produce the best results. This leads to the conclusion that very high-resolution satellite imagery is a fair compromise when using such models.

The second study, whose method was presented in [START_REF] Feurer | Measuring depth of a clear, shallow, gravel-bed river by throughwater photogrammetry with small format cameras and ultra light aircrafts[END_REF], showed that through-water photogrammetry is possible with such platforms and cameras, with special care taken about geometric calibration and refraction correction. On the other hand, measure precision is proportional to the ground resolution, and measure accuracy is critically sensitive to the geometry acquisition and the accuracy of these parameters when computed from ground control points. Some improvements must therefore be done on the control of these parameters in order to obtain a satisfactory accuracy and precision for long fl ight lines.

Finally, the bathymetric lidar, which was not tested here, appears as a very interesting tool to monitor river bathymetry, because active laser allows measurement even within bad illumination conditions or with low turbidity. On the other hand, its ability to map depth lower than 0.50 m has not yet been demonstrated. For smaller depths, the use of other algorithms and other wavelengths (such as a red one, to detect the Raman diffusion peak, for instance) is necessary [START_REF] Pe'eri | Increasing the existence of very shallow-water LIDAR measurements using the red-channel waveforms[END_REF][START_REF] Allouis | On the use of Raman, infra-red, and green waveforms in bathymetric LiDAR for very shallow waters[END_REF].

Figure 5

 5 Figure 5 The principle of lidar

Figure 6

 6 Figure 6 Test site on the Durance River and left channel depth distribution (histogram). The river flows from top right to bottom left. An image band is superimposed on the left channel Source: Images from http://www.lavionjaune.fr

Table 1

 1 River bathymetry by remote sensing -case studies

		in : Progress in physical geography, vol 32, n°4, 2008.p.403-419
	Sites	Platform; sensor	References	Spectral GPR	Lidar	Stereo
	Yukon River, AK, USA	frozen surface; GPR	Annan and Davis (1977)	X
	Willimantic River, CT, USA	boat; 80 MHz GPR	Beres and Haeni (1991)	X
	St Mary River, MI, USA	plane; Daedalus 1260	Lyon et al. (1992)	X
	Green River, UT, USA	plane; COHU 4810	Hardy et al. (1994)	X
	Faith Creek, AK, USA	plane; Hamamatsu	Gilvear et al. (1995)	X
	Rupnarayan-Hooghly river	satellite; IRS-1b LISS-II	Kumar et al. (1997)	X
	confl uence, India
	Southwestern WA, USA	cable; 100 MHz GPR	Spicer et al. (1997)	X
	River Tummel, Scotland	plane; ATM, B&W photo	Winterbottom and Gilvear (1997);	X
			Gilvear et al. (2004)
	Saco River, Maine	helicopter; SHOALS	Irish and Lillycrop (1999)		X
	River Tay, Scotland	plane; ATM, B&W photo	Bryant and Gilvear (1999)	X
	Harrison and Horsefl y Rivers, BC,	plane; 3 CCD cameras + imaging	Roberts and Anderson (1999)	X
	Canada	spectrometer VIFIS
	Skagit River, WA, USA	cable; 100 MHz GPR	Costa et al. (2000); Haeni et al.	X
			(2000)
	Ashburton River, New Zealand	plane; Zeiss LMK15	Westaway et al. (2000; 2001)			X
	Cowlitz River, WA, USA	helicopter; 100 MHz GPR	Melcher et al. (2002)
	Lamar River, WY, USA	helicopter; Probe-1	Marcus et al. (2003)	X
	Waimakariri River, New Zealand	plane; Zeiss LMK15	Westaway et al. (2003)	X
	Durance, France	UAV; non-metric 35 mm camera	Chaponnière (2004)	X
	River Tummel, Scotland	plane; 35 mm colour camera and	Gilvear et al. (2004)	X
		daedalus 1260
	Soda Butte Creek , WY, USA	helicopter; ADAR and Probe-1	Legleiter et al. (2004)	X
	Sagavanirktok River, AK, USA	frozen surface; GPR	Lunt and Bridge (2004)	X
						(Continued)

Table 2

 2 River depth mapping by remote sensing: characteristics of each method

	Technique	Measure	Accuracy	Data acquisition Applicability Remarks
		density
	Lidar	2×2 m to	XY: 1 to 2.5 m	1 hour for	0.5 to 60 m	in development
		5×5 m	Z: 0.18 to	70 km 2	according to	for shallow
			0.35 m		turbidity, no	waters
					overhanging
					vegetation
	GPR	depends on	Z: 0.30 m	2 m.s -1 along a	turbid and	vertical precision
		protocol	at 100 MHz	cable, airborne	fl ooding rivers	decreases with
			(estimated)	in test		max. penetration
						depth
	Spectral	pixel size	XY: subpixel	aerial or satellite	clear water,	need for
		(5.6 cm to	Z: highly	imagery	homogeneous	calibration data
		36.25 m)	variable		bottoms, no
			(min. 0.20 m)		overhanging
					vegetation
	Photogrammetry pixel size	XY: subpixel	aerial imagery,	clear water,	data taken
			Z: 0.20 cm	60% overlap	homogeneous	from the
					bottoms, no	unique available
					overhanging	reference
					vegetation

Table 3

 3 Radiometric method: comparison with an independent ground truth data set; RMSE is for root mean square error and ME for mean error Determination of the optimal spatial resolution for the radiometric models method in : Progress in physicalgeography, vol 32, n°4, 2008.p.403-419

	Emulsion	Riffl es	Pools
	colour infrared
	pixel: 1 cm

in : Progress in physicalgeography, vol 32, n°4, 2008.p.403-419

Bulletin Français de la Pêche et de la Pisciculture 337/339, 309-16. Legleiter, C. and Roberts, D. 2005: Effects of channel morphology and sensor spatial resolution on image-derived depth estimates. Remote Sensing of Environment 95, 231-47. Legleiter, C., Roberts, D.A., Marcus, A.W. and Fonstad, M.A. 2004: Passive optical remote sensing of river channel morphology and in-stream habitat: physical basis and feasibility. Remote Sensing of Environment 93, 493-510. Lejot, J., Delacourt, C., Piégay, H., Fournier, T., Trémélo, M. and Allemand, P. 2007: Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms 32, 1705-25. Lesaignoux, A. 2006: Modélization et simulations de trains d'ondes LiDAR 'vert': application à la détection de faibles lames d'eau en rivière. Master's thesis, Université Montpellier II -CNAM. Lesaignoux, A., Bailly, J. and Feurer, D. 2007: Small water depth detection from green lidar simulated full waveforms: application to gravel-bed river bathymetry. In Physics in Signal and Image Processing (PSIP) Fifth Internation Conference, 31 January-2 February, France. Lunt, A. and Bridge, J. 2004: Evolution and deposits of a gravelly braid bar, Sagavanirktok River, Alaska. Sedimentology 51, 415-32. Lyon, J.G., Lunetta, R.S. and Williams, D.C. 1992: Airborne multispectral scanner data for evaluating bottom sediment types and water depths of the St. Mary's River, Michigan. Photogrammetric Engineering and Remote Sensing 58, 951-56. Lyzenga, D.R. 1978: Passive remote-sensing techniques for mapping water depth and bottom features. Applied Optics 17, 379-83. -1985: Shallow water bathymetry using combined lidar and passive multispectral scanner data. International Journal of Remote Sensing 6, 115-25. Marcus, A.W., Legleiter, C.J., Aspinall, R.J., Boardman, J.W. and Crabtree, R.L. 2003: High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams. Geomorphology 55, 363-80. McKean, J., Wright, W. and Isaak, D. 2006: Mapping channel morphology and stream habitat with a full waveform water-penetrating green lidar. In European Geosciences Union (EGU) General Assembly, 2-7 April, Vienna. Melcher, N.B., Costa, J., Haeni, F., Cheng, R., Thurman, E., Buursink, M., Spicer, K., Hayes, E., Plant, W., Keller, W. and Hayes, K. 2002: River discharge measurements by using helicoptermounted radar. Geophysical Research Letters 29(22),


Adrien Coulet
email: adrien.coulet@loria.fr

Yael Garten
email: ygarten@stanford.edu

Michel Dumontier
email: michel_dumontier@carlton.carba

Russ B Altman
email: russ.altman@stanford.edu

Mark A Musen
email: musen@stanford.edu

Nigam H Shah

Integration and publication of heterogeneous textmined relationships on the Semantic Web

Background

Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.

Results

We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network.

Conclusions

The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology

forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions.

PHARE is available at http://purl.bioontology.org/ontology/PHARE.

Background

A large amount of biomedical knowledge is in the form of text embedded in published articles, clinical files or biomedical public databases. In order to construct computable knowledge bases from these sources, there is a great interest in capturing and formalizing this knowledge. The capture of relationships between biological entities is of particular interest since such relationships represent elementary and reusable knowledge units-often called "nano-publications" [START_REF] Groth | The anatomy of a nanopublication[END_REF].

Our work is motivated by the need for automated approaches capturing and formalizing knowledge extracted from the literature via manual or computational approaches. Consider for example, that five curators at the Pharmacogenomics Knowledge Base (PharmGKB) manually browse the pharmacogenomics (PGx) literature to curate relationships relevant for storage in the PharmGKB [START_REF] Klein | Integrating genotype and phenotype information: An overview of the PharmGKB project[END_REF]. The result of this curation process is a high quality database queried by clinicians and bioinformaticians. Nevertheless this manual curation process is not sustainable considering the growth of the scientific literature in this domain [START_REF] Garten | Recent progress in automatically extracting information from the pharmacogenomic literature[END_REF]. Automatic approaches using Natural Language Processing (NLP) are therefore increasingly utilized [START_REF] Hunter | OpenDMAP: An open-source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression[END_REF].

The simplest methods to capture relationships rely on co-occurrence of two entities to derive a relation between them. For example, in the sentence "Our study shows that warfarin inhibits the expression of VKORC1" a drug, warfarin, and a gene, VKORC1, can be recognized using simple lexicons. The co-occurrence of these two entities in one or more sentences is used to derive a relation of the form (warfarin, VKORC1).

One key limitation of the co-occurrence based approach is identification of false positive connections. For example the sentence "Warfarin inhibits the expression of VKORC1 while sulfamethoxazole inhibits the expression of CYP2C9" would provide co-occurrence counts towards four relationships including the relationships (warfarin, VKORC1) and (warfarin, CYP2C9); only one of which is true. A second limitation is the coarse granularity of the identified relationships. Considering the previous example, the mentioned relationship links warfarin and the expression of VKORC1, and not VKORC1 per se. We consider this distinction of importance since VKORC1 and expression of VKORC1 refer to a gene and a phenotype respectively-two very distinct entities. Despite these limitations, co-occurrence is successfully used to generate networks including protein-protein interaction networks, gene-disease networks and regulatory gene expression networks [START_REF] Friedman | Genies: a naturallanguage processing system for the extraction of molecular pathways from journal articles[END_REF][START_REF] Saric | Extraction of regulatory gene/protein networks from medline[END_REF]. Most of these networks are hard to compute on since their representation format does not support queries with typed relationships and the semantics associated with the nodes and edges differ in every network.

Other NLP approaches can identify typed relationships and recognize entities that can either be the whole or a part of a subject and an object [START_REF] Ciaramita | Unsupervised learning of semantic relations between concepts of a molecular biology ontology[END_REF][START_REF] Ramakrishnan | Unsupervised Discovery of Compound Entities for Relationship Extraction[END_REF][START_REF] Tari | Discovering drug interactions: a text-mining and reasoning approach based on properties of drug metabolism[END_REF]. For example processing the previous sentence can identify the following relationship inhibits(warfarin, the expression of VKORC1) -that can also be represented as inhibits (warfarin, VKORC1 expression). Figure 1 shows three levels of granularity commonly encountered in text-mined relationships. Fine-grained relationships can be identified via syntactic parsing of sentences, which generates structures such as Parse Trees or Dependency Graphs (DG) [START_REF] Manning | Foundations of Statistical Natural Language Processing[END_REF]. In previous work, we presented a method based on syntactic parsing and DG exploration to extract fine-grained PGx relationships [START_REF] Coulet | Using Text to Build Semantic Networks for Pharmacogenomics[END_REF]. Given the variation in natural language, it is difficult to normalize the fine-grained and typed relationships extracted by this method. In this paper, we report on the construction of a relationship ontology and describe its use for integrating and publishing text-mined relationships on the Semantic Web. The relationships captured as instances of the PHARE ontology can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network. Semantics associated with relationships declared in PHARE-KB allow the text-extracted relationships to be consumed both by humans (for example, to guide curation) as well as by machines (for example, to guide computational prediction of molecular interactions).

Methods

In previous work, we described the extraction of over 40,000 raw relationships in the domain of pharmacogenomics from MEDLINE abstracts [START_REF] Coulet | Using Text to Build Semantic Networks for Pharmacogenomics[END_REF]. In following sections we briefly summarize this extraction process and then describe how we use the PHARE ontology we have created to normalize and integrate these relationships.

Relationships and PGx relationships

We define a relationship as a binary relation R (a, b), where a, and b are subjects and objects related by a relationship of type R. In PGx relationships a and b can be instances of a gene (e.g., VKORC1 gene), drug (e.g., warfarin), or phenotype (e.g., clotting disorder). We note that a and b can also be entities that are related to genes (e.g., VKORC1 expression), drugs (e.g., warfarin dose) or phenotypes (e.g., clotting disorder treatment). R is a type of relation described by words such as "inhibits", "transports", or "treats" and their synonyms.

The three key entities in PGx (genes, drugs, and phenotypes) can be either direct targets for relation extraction, or indicators of latent PGx knowledge, as they modify other entities to create a second set of entities necessary to precisely describe PGx relationships. We refer to these modified entities as composite entities in contrast with the key entities. These composite entities can be any biomedical entity, such as a gene variation, drug effect, or disease treatment. For example, the gene entity VKORC1 (a key entity) is used as a modifier of expression in "warfarin inhibits the expression of VKORC1." Specifically, composite entities are composed of a sequence of terms that can be read left to right and where left term progressively specializes the term on its right. The last word is named the head entity. Figure 2 shows the components of relationships.

Identification of a sentence with PGx relationships

Given the definition of PGx relationships, a sentence that potentially contains a PGx relationship would mention a gene and drug, a gene and a phenotype, or a drug and a phenotype. We used a Lucene index created on individual sentences of MEDLINE abstracts published before 2009 (17,396,436 abstracts and 87,806,828 sentences) processed by Xu et al. to identify those sentences that might contain a PGX relationship [START_REF] Agichtein | Snowball: extracting relations from large plain text collections[END_REF][START_REF] Xu | Unsupervised method for automatic construction of a disease dictionary from a large free text collection[END_REF]. To select only sentences that potentially mention a PGx relationship we queried the index with pairs of key PGx entities (only gene-drug and gene-phenotype pairs) for sentences that are indexed with both the terms in the query.

The PharmGKB lexicon, provides the sets of synonyms used to build such queries for the key entities. Overall, for this study we used 41 genes highlighted by PharmGKB as key, well characterized pharmacogenomic genes [14], as well as 3,007 drugs and 4,202 phenotypes. Future work will expand the relationship extraction to all genes.

Extraction of heterogeneous raw relationships

Sentences returned by the index are parsed using the Stanford Parser to build Dependency Graphs (DGs) [START_REF] De Marneffe | The Stanford typed dependencies representation[END_REF]. DGs are rooted, directed, and labelled graphs, where nodes are words and edges are dependency relations between words (e.g., noun modifier, nominal subject). The extraction of raw relationships of the form R(a,b) relies on the exploration of syntactic structure provided by DGs where:

-a and b are nodes or chains of nodes in a DG, depending on whether they are a single key entity (an instance of gene, drug or phenotype) or a composite entity; -R is a node in the DG that connects a and b, and indicates the nature of their relationship.

We have developed an algorithm to explore the DG and extract raw relationships from the raw text. The extraction of raw relationships is constrained by a set of rules defined using the different type of dependencies that associate nodes in DG. This step results in the extraction of over 40,000 raw relationships discussed in [START_REF] Coulet | Using Text to Build Semantic Networks for Pharmacogenomics[END_REF]. These relationships are highly heterogeneous and contain multiple equivalent ways to express one single fact. The details of the DG exploration algorithm appear in Table 1 of [START_REF] Coulet | Using Text to Build Semantic Networks for Pharmacogenomics[END_REF].

Building the PHArmacogenomic RElationship ontology

In order to create a smaller, normalized set of relationships, we first identified the 200 most frequent relationship types from the ~40,000 raw relationships. In the next step, we manually merged similar relationships and organized them hierarchically. Groups of similar relationships are used to define roles in the PHARE ontology. For example Figure 3 shows how inhibit, repress, and antagonize are merged to define the role inhibits. Role labels are declared using the rdfs:label annotation property. The first label of each role is used as its preferred name. Please note that the symbol $$ is a simple separator symbol that enables us to distinguish the passive voice from the simple past during the next normalization step.

In a similar manner we identified the 200 most frequent terms modified by key entities (e.g., expression for gene names or sensitivity for drug names). Then five PGx experts, including 3 co-authors and 2 PharmGKB curators, manually merged similar ones and organized them hierarchically in the entity hierarchy. Figure 4 shows how variant, polymorphism, and mutation are merged to define the entity Variant.

The entity hierarchy is defined with the subsumption relation (noted as ⊑ or subClassOf in OWL). Existential quantification is used to define sets of composite entities that are only modified by certain concepts. For example the set of entities that are modified by drugs is defined with the existential quantifier (∃) and the role modified by: ∃ modified.Drug (or modified someValuesFrom Drug in Manchester OWL syntax), see Figure 4 for examples. This definition is associated through a subsumption relation to entities that can be modified by drugs, such as DrugSensitivity. This pattern is used to distinguish what thing is specialized (or modified) by drugs from what is specialized by other modifiers (e.g. disease names).

For example warfarin that we know to be a drug enables us to distinguish warfarin sensitivity from cancer sensitivity and to classify warfarin sensitivity as a kind of drug sensitivity versus disease sensitivity (represented by the DiseaseSensitivity concept).

Inverse roles are explicitly defined using the inverse constructor (-1 or inverseOf in OWL). As shown in the example in Figure 5, roles inhibits and isInhibitedBy are inverses of one another.

Class declarations are used to list all key entities of the domain of interest and what entity type they belong to. In our case, where gene-drug relationships are studied, known drugs and genes must be defined in the ontology as being an instance of the entity types Drug and Gene.

Building of WN-PHARE ontology using WordNet

In order to quantify the utility of manual review and editing of the raw relationships in building PHARE, we built a second ontology named WN-PHARE in a purely automated manner using the lexical resource WordNet [START_REF] Feebaum | WordNet, An Electronic Lexical Database[END_REF]. In this case all relationship types-and not just the 200 most frequent ones-are computationally merged in groups according to WordNet synsets. Resulting groups are directly used to define roles without any manual review. Similarly, all terms that modify gene, drug or phenotype names are merged in groups used to define composite entities.

Normalization and integration of heterogeneous relationships

The algorithm to normalize typed relationships between composite entities consists of four steps. The first three steps normalize the subject entity, the object entity, and the relationship type. The last step, assembles the three normalized pieces in a normalized relationship of the kind shown in Figure 1.

Normalization of composite entities (steps 1 and 2)

This step-described in Table 1-takes as input a raw composite (or atomic) entity and the PHARE ontology to return a normalized entity. The first word of the entity is recognized as the key entity. Then each following word that composes the entity is considered from left to right as something further specialized by previous words. The ontology is searched for an entity label that matches with the processed word (named read_word in the Table 1 algorithm). This algorithm is applied successively to the subject entity and the object entity of a relationship (Figure 6).

Normalization of relationship types (step 3)

The next step is to normalize the relationship type. The ontology is searched for role labels that match the raw relationship. When a match is found, the preferred name of the corresponding role is used to normalize the relationship type. Note that during this step the normalization process distinguishes between passive voice of the present tense, such as "A is inhibited by B" and active voice of simple past tense "B inhibited A". Dependency Graphs of these two sentences are different because "inhibited" in the passive voice sentence is related through an aux dependency to "is" (standing for auxiliary). This difference is used during the relationship extraction to extract either is$$inhibited(A, B) or inhibited(A, B).

Assembly of normalized pieces (step 4)

The final step is to group together normalized composite entities and relationship type to produce normalized relationships. For each relationship, this step relies on the simple assembly of normalized type, subject and object. In addition if the role used to normalize the type has inverses or is symmetric then this step also creates the appropriate additional relationships. For each inverse role in the ontology, an inverse relationship is created with the preferred name of the inverse and where normalized subject and object are swapped. If the role is symmetric, one additional relationship is created with the same normalized relationship type but with subject and object swapped. Figure 5 illustrates the integration process that applies such relationship normalization on four heterogeneous sentences.

Applying the normalization on raw relationships produces a set of relationships represented as PHARE entities and roles. Consequently normalized relationships can be directly added to PHARE as instances to create a knowledge base.

Refinement of PHARE by repeating the normalization step

Raw relationships have been normalized twice using PHARE to iteratively refine the ontology. After the first iteration of the normalization, from the pool of un-normalized relationships we manually identify terms and roles that are either frequent or of PGx interest. Such terms (or roles) are then used to extend the set of synonyms of an entity already defined in the ontology, or used to create a new entity in the ontology. analysis. New roles correspond to types of relationships that are not frequent enough in our corpus and consequently have not yet been manually reviewed and defined in PHARE. These roles, which are added solely to instantiate the 23% of un-normalized relationships are associated with only one, label and thus do not yet contribute to the integration of relationships.

Visualizing gene-disease networks

The 28,676 role instances link roughly 16,000 individuals of the KB, including 285 genes, 1,083 drugs and 990 diseases. To facilitate overlap comparisons of PHARE-KB with other data sources individuals that are of type genes, drugs, or diseases are associated with their Entrez Gene, DrugBank, and MeSH identifiers respectively. Individuals in the PHARE-KB can be classified using reasoning. Classification allows us to make the implicit knowledge units explicit. For example, classification infers that Phenotype(VKORC1 expression) i.e., VKORC1 expression is a phenotype on the basis of the following two axioms

Expression(VKORC1 expression)

Expression ⊑ Phenotype i.e., VKORC1 expression is a gene expression and gene expression is a phenotype. Every relationship available in the PHARE-KB (in the form of a RDF triple) is associated with its provenance using the property rdfs:comment. For example, the triple isAssociatedWith(UCHL1, parkinson disease) is associated with the following string: "[14522054, Neuronal ubiquitin C-terminal hydrolase (UCH-L1) has been linked to Parkinson's disease (PD), the progression of certain nonneuronal tumors, and neuropathic pain]", Where 14522054 is the PMID (PubMed ID) of the article and the text is the sentence based on which the triple is created.

Evaluation and comparison

To evaluate the impact of the manual review and curation in the construction of the PHARE ontology, we constructed an alternate relationship ontology-named WN-PHARE-in a fully automated manner using WordNet as described in the methods section. Table 2 compares the structure and the effectiveness of PHARE and WN-PHARE in integrating heterogeneous text-mined relationships. These features are measured for the task of integrating a subset of relationships extracted for Parkinson's Disease (PD). This subset contains 2,827 PD relationships extracted from 2,124 distinct MEDLINE abstracts. Logic criteria (e.g., satisfiability) of the ontologies are not included in the comparison since both ontologies are consistent and coherent.

We find that the roles represented in PHARE cover the set of extracted relationships incompletely but they normalize more relationships than the roles in defined in WN-PHARE. Thus the manually reviewed ontology results in a better identification of similar relationships that are phrased differently in natural language, but it captures a smaller fraction of the total relationships extracted from text. Table 3 provides additional evaluation with numbers of similar relationships (same subject, predicate and object) identified first before normalization, second after normalization using PHARE, and third after normalization using WN-PHARE.

SPARQL query point

In order to publish the PHARE-KB for use on the Semantic Web, we set up a SPARQL endpoint, which is available at http://sparql.bioontology.org/webui/.

Examples of queries are provided as additional file 1 (named additional_file_1.txt).

The KB is classified and inferred triples are materialized before loading into the triple store underlying the SPARQL endpoint. As a consequence queries return asserted as well as inferred facts.

An example of query for entities related to the uchl1 gene is shown below: This query returns the RDF triple isAssociatedWith(UCHL1, parkinson disease) mentioned previously. Queries can also return sets of RDF triples that are used to build sub-network related to a specific diseases as shown in Figure 7.

Disease related gene networks

Figures 7 and8 show gene-disease sub-networks related to AD and PD respectively.

For display purpose, these have been reduced by selecting only those nodes that are asserted to be related in more than 5 different sentences. Since the type of relationship differ in sentences, only the two most frequent relationships are displayed as labels on the edges. Each network was obtained using a SPARQL query to select triples where the disease (AD or PD) is either subject or object. Resulting set of triples is then filtered to keep the frequent relationships. Such filtering enables to us remove both false positives as well as irrelevant triples such as phare:alzheimer=disease rdf:type phare:Disease . Note that in RDF we use the symbol '=' as a simple separator to replace spaces in coumpound nouns.

Discussion

Our work is motivated by the need for automated approaches capturing and formalizing knowledge extracted from the literature and the need for publishing such knowledge on the Semantic Web. Recent advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text [START_REF] Hunter | OpenDMAP: An open-source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression[END_REF]. The variability and the complexity of natural language in expressing similar or simple relationships causes the extracted relationships to be highly heterogeneous. We show that the use of a relationship ontology can normalize and integrate the heterogeneous relationships extracted from text and serve as a common semantic framework to integrate text-mining derived facts into a knowledge base. However, the manual construction of a relationship ontology is a slow and expensive process [START_REF] Smith | Relations in biomedical ontologies[END_REF]. We have devised a method to construct such an ontology using the text-extracted heterogeneous relationships as a starting point. Although we only report on our experiments in the pharmacogenomics domain; we note that the approach described here can be applied for relationship extraction in other domains.

Linked data cloud and text-mined relationships

Our results in publishing RDF triples extracted from text align closely with the objectives of the Linking Open Data community project [START_REF]Linking Open Data Community Project[END_REF] and that of efforts such as the Concept Web Alliance [START_REF]Mission of the Concept Web Alliance[END_REF]. The goal of projects such as Linked Open Data is to publish various data sets as RDF on the Web and to declare links between data items from different data sources.

Currently, the relationships we extract do not integrate easily with content in the Link Data Cloud for two main reasons: the lack of resource unique identifiers and the lack of an agreed upon relation ontology. Despite community efforts to create unique resource identifiers for life sciences, currently there is no clear consensus [21,[START_REF]Shared Name project[END_REF]. In addition, composite entities, such as VKORC1 expression that participate in relationships are too complex to reference using a single identifier. Moreover, the absence of an expressive and comprehensive relation ontology led us to develop our own in a boot-strapped manner from example instances of text-mined relationships.

PHARE is designed for the purpose of representing PGx relationships and we anticipate that sharing it with the community will provide a much needed example set for the development of a proper, formal biomedical relation ontology. PHARE is particularly suited to seed that activity, because it is built from the most frequent relationships that are used in the scientific literature. One challenge is thus to propose consistent mappings between relationship types arising from the literature, such as those suggested by PHARE and relationship types arising from functional annotations such as "suppresses gene" or "enhances gene" suggested by TAIR relations or the Gene Ontology [23].

Limitations of our approach

Adequately representing provenance information at the sentence level is a challenge. Currently, we utilize the rdfs:comment property to store provenance for each extracted fact in PHARE-KB. In the future, we plan to evaluate the Annotation Ontology developed by Ciccarese et al. [START_REF] Ciccarese | AO: An Open Annotation Ontology for Science on the Web[END_REF] for its utility is representing provenance at the sentence level, particularly in workflows where both automated and manual approaches are used simultaneously.

Another limitation is the incoherence between gene name identifiers across data sources. Our gene identifiers are based on PharmGKB gene names that are not entirely consistent with the HUGO Gene nomenclature [START_REF] Hugo Gene | [END_REF], making cross referencing with other sources time consuming. In a similar vein, recall for extracted relations

Conclusions

We have described the construction of an ontology of relationships in the PGx domain and its use to integrate heterogeneous relationships extracted by text-mining. The synonyms, entity descriptions, and the hierarchies of entities and roles represented in the ontology are used to map text-derived relationships to the ontology. Once mapped, relationships can be normalized and compared using the semantics defined in the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast a fully automated and a manually edited version of the PHARE ontology to quantify the degree of integration enabled by manual inspection, curation and refinement of the PHARE ontology. PHARE has been successfully used in a pipeline for the integration of pharmacogenomic relationships extracted from MEDLINE abstracts [START_REF] Coulet | Using Text to Build Semantic Networks for Pharmacogenomics[END_REF]. The result of the integration is compiled into a knowledge base named PHARE-KB, which can now be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network. PHARE-KB can also be queried programmatically, for example, to guide computational prediction of molecular interactions [START_REF] Garten | Text mining the scientific literature to identify pharmacogenomic interactions[END_REF].

Figures

Figure 1 -Coarse to fine-grained relationships

Coarse to fine-grained relationships identified in the sentence "Our study shows that warfarin inhibits the expression of VKORC1". Relationships are mainly of three forms: (1) non-typed relationships composed of two atomic entities; (2) typed relationships between atomic entities; (3) typed relationships between atomic or composite entities.

Figure 2 -Components of relationships

A relationship has three components: relationship type, subject (here limited to a key entity), and object (here a composite entity which uses key entity as a modifier). Four raw relationships are normalized to two expressions, using the PHARE ontology. The first two (s1 and s2) mention the same relationships with different words and sentence structures and are consequently integrated (e.g. 'drug dose' and 'drug requirement' are declared synonyms). s3 illustrates the utility of being able to distinguish between concepts modified by Gene and by Drug to disambiguate two different occurrences of "level": one specialized by a gene name, the other by a drug name. Given the ontology, 'gene level' is a reference to gene expression, whereas 'drug level' refers to drug dose. s3 and s4 illustrate the utility of role inverses in the ontology, which enable the integration of relationships extracted from s3 and s4 by swapping subject and object of s3. The last two raw relationships are inverses that express the same relationship. Starting with the text "differences in coumadin requirements", NLP tools generate the raw entity "coumadin requirements differences" on which we can apply the normalization algorithm (described in table 1) using the PHARE ontology. The first step ensures that the preferred name warfarin is used instead of coumadin. The second step maps "requirements" to the entity type DrugDose, and the final step maps "differences" to the entity type Variation. The axiom noted with a * is added to the ontology during the normalization as a result of the inference that a variation in drug dose was found.

Ontology

Figures 7

 7 Figures 7 and 8 are made using a RDF to GML (Geography Markup Language)

Figure 3 -

 3 Figure 3 -A portion of the role hierarchy of the PHARE ontology Each box represents a role and words in the lower part of the box are the alternative labels for that role. Arrows represent sub-role relation. Each label can only belong to one role.

Figure 4 -

 4 Figure 4 -A portion of the Entity hierarchy of the PHARE ontology Each box represents an entity type and terms in the lower part of the box are the alternative labels for that entity. Subsumption relations are represented with arrows. Non-hierarchical relations are represented without arrow.

Figure 5 -

 5 Figure 5 -Integration of heterogeneous relationships

Figure 6 -

 6 Figure 6 -Normalization of a composite entity

Table 2 -Comparison of PHARE and WN-PHARE Comparison

 2 of PHARE (built semi automatically with added manual review and curation) and WN-PHARE (built in a fully automated manner). The Reduction column quantifies the ability of each ontology to normalize text-mined relationships. Reduction is the ratio of the number of normalized relationships and the initial number of raw relationships. The Coverage column quantifies the fraction of raw relationships that are normalized using roles and entity types encoded in the ontology.

			Raw relationships	Relationships normalized with
			(no normalization)	PHARE	WN-PHARE
	Number of	2 ≤ n < 5	7	87	70
	relationships	5 ≤ n < 10	0	12	6
	identified n times	n ≥ 10	0	5	2

Table 3 -Comparison of the identification of similar relationships

 3 Comparison of the occurrence of relationships in three differentially normalized sets of relationships. Identifications are made on 2,827 relationships related to Parkinson's Disease. Before any normalization only 7 distinct relationships can be identified as occurring several times. Normalization with PHARE and WN-PHARE (built semi automatically) and WN-PHARE (built in a automated manner) enable to reveal more identical relationships. For instance, with PHARE normalization, 5 relationships are found to occur more than 10 times (n ≥ 10).

Acknowledgements

This work was supported in part by the National Center for Biomedical Ontologies, under roadmap-initiative grant (U54HG004028) from the NIH and by the PharmGKB (GM61374), with computing cluster support from the NSF (CNS-0619926).

Results

The PHARE ontology

The PHArmacogenomic RElationship ontology (or PHARE) contains 229 entity classes and 76 roles of interest in the PGx domain. PHARE is encoded in OWL-DL and is constructed semi automatically by (i) listing terms derived from relationships extracted automatically from text ; and (ii) the manual organization of the relationship terms by domain experts. Figures 2 and3 illustrate how the extracted terms are organized in these hierarchies. The PHARE ontology is available online at http://purl.bioontology.org/ontology/PHARE.

The PHARE-Knowledge Base (PHARE-KB)

The ontology-driven integration process described in the method section takes as input a set of relationships extracted from MEDLINE abstracts and outputs a set of normalized relationships of the form Role(subject, object) represented using entity types and roles defined in PHARE. Therefore, normalized relationships can be used to instantiate roles defined in PHARE without additional processing. We performed such instantiation and obtained the PHARE-Knowledge Base (or PHARE-KB) that contains 28,676 roles instantiations encoded as RDF triples from over 41,000 raw relationships. If we consider instantiation of role inverses (e.g., isInhibitedBy (a,b) ≡ inhibits -1 (b,a)), the number of role instantiations rises to 46,526. Note that some roles in PHARE do not have inverse or are symmetric (e.g., isAssociatedWith).

Almost 77% role instantiations use roles initially encoded in PHARE and 23%

necessitate the creation of new roles in PHARE. In other words PHARE roles are sufficiently detailed to capture 77% of the relationships we extracted from text may improve upon using advanced Named Entity Recognition such as disambiguation techniques rather than the current PharmGKB-derived dictionary based approach.

The efficacy of the relationship normalization and integration might vary depending on the source of the text such as full articles, clinical reports, clinical files or drug labels. However, because PHARE has been designed using MEDLINE abstracts, it may capture relationships mentioned in diverse sources.

List of abbreviations used

Competing interests

Authors declare no competing interests.

Authors' contributions

AC designed and implemented the ontology driven relationship normalization as well as wrote the manuscript. YG improved the approach and the ontology. MD contributed to the evaluation and discussion sections. YG and MD critically reviewed the manuscript. RBA and MAM obtained funds and gave scientific directions. NHS supervised the project, participated in technical discussions and wrote the manuscript. Sub-network of genes (or associated entities) strongly related to Alzheimer's Disease (AD) according to PHARE-KB. Linked entities are linked by more than 5 sentences in MEDLINE abstracts. Relationships shown on the edges are the two most frequent type of relations mentioned in these sentences. Some relationships type are false such as "hearing".

Tables

Algorithm for the normalization of composite entities using a domain ontology